query: I aim to develop a comprehensive fake news detection system capable of identifying and curbing the spread of misinformation. The system should be adaptable enough to detect fake news across various domains, such as tracking misinformation related to COVID-19 on social media platforms. Given the multimedia nature of news, the system should incorporate an automatic speech recognition module, an image (video) processing module, and a language model, among other potential components. It would be beneficial to convert audio recognition into transcripts and image and video processing into natural language descriptions, necessitating the design of a robust language model. Additionally, I am keen to explore the dynamics of large-scale information dissemination, which I hope to address by my proposed fake news detection system.

positive:
Dense video captioning is a task of localizing interesting events from an
untrimmed video and producing textual description (captions) for each localized
event. Most of the previous works in dense video captioning are solely based on
visual information and completely ignore the audio track. However, audio, and
speech, in particular, are vital cues for a human observer in understanding an
environment. In this paper, we present a new dense video captioning approach
that is able to utilize any number of modalities for event description.
Specifically, we show how audio and speech modalities may improve a dense video
captioning model. We apply automatic speech recognition (ASR) system to obtain
a temporally aligned textual description of the speech (similar to subtitles)
and treat it as a separate input alongside video frames and the corresponding
audio track. We formulate the captioning task as a machine translation problem
and utilize recently proposed Transformer architecture to convert multi-modal
input data into textual descriptions. We demonstrate the performance of our
model on ActivityNet Captions dataset. The ablation studies indicate a
considerable contribution from audio and speech components suggesting that
these modalities contain substantial complementary information to video frames.
Furthermore, we provide an in-depth analysis of the ActivityNet Caption results
by leveraging the category tags obtained from original YouTube videos. Code is
publicly available: github.com/v-iashin/MDVC

positive:
Speech summarization, which generates a text summary from speech, can be
achieved by combining automatic speech recognition (ASR) and text summarization
(TS). With this cascade approach, we can exploit state-of-the-art models and
large training datasets for both subtasks, i.e., Transformer for ASR and
Bidirectional Encoder Representations from Transformers (BERT) for TS. However,
ASR errors directly affect the quality of the output summary in the cascade
approach. We propose a cascade speech summarization model that is robust to ASR
errors and that exploits multiple hypotheses generated by ASR to attenuate the
effect of ASR errors on the summary. We investigate several schemes to combine
ASR hypotheses. First, we propose using the sum of sub-word embedding vectors
weighted by their posterior values provided by an ASR system as an input to a
BERT-based TS system. Then, we introduce a more general scheme that uses an
attention-based fusion module added to a pre-trained BERT module to align and
combine several ASR hypotheses. Finally, we perform speech summarization
experiments on the How2 dataset and a newly assembled TED-based dataset that we
will release with this paper. These experiments show that retraining the
BERT-based TS system with these schemes can improve summarization performance
and that the attention-based fusion module is particularly effective.

positive:
The rapid advancement of technology in online communication via social media
platforms has led to a prolific rise in the spread of misinformation and fake
news. Fake news is especially rampant in the current COVID-19 pandemic, leading
to people believing in false and potentially harmful claims and stories.
Detecting fake news quickly can alleviate the spread of panic, chaos and
potential health hazards. We developed a two stage automated pipeline for
COVID-19 fake news detection using state of the art machine learning models for
natural language processing. The first model leverages a novel fact checking
algorithm that retrieves the most relevant facts concerning user claims about
particular COVID-19 claims. The second model verifies the level of truth in the
claim by computing the textual entailment between the claim and the true facts
retrieved from a manually curated COVID-19 dataset. The dataset is based on a
publicly available knowledge source consisting of more than 5000 COVID-19 false
claims and verified explanations, a subset of which was internally annotated
and cross-validated to train and evaluate our models. We evaluate a series of
models based on classical text-based features to more contextual Transformer
based models and observe that a model pipeline based on BERT and ALBERT for the
two stages respectively yields the best results.

positive:
Conventional spoken language understanding systems consist of two main
components: an automatic speech recognition module that converts audio to a
transcript, and a natural language understanding module that transforms the
resulting text (or top N hypotheses) into a set of domains, intents, and
arguments. These modules are typically optimized independently. In this paper,
we formulate audio to semantic understanding as a sequence-to-sequence problem
[1]. We propose and compare various encoder-decoder based approaches that
optimize both modules jointly, in an end-to-end manner. Evaluations on a
real-world task show that 1) having an intermediate text representation is
crucial for the quality of the predicted semantics, especially the intent
arguments and 2) jointly optimizing the full system improves overall accuracy
of prediction. Compared to independently trained models, our best jointly
trained model achieves similar domain and intent prediction F1 scores, but
improves argument word error rate by 18% relative.

positive:
A major focus of recent research in spoken language understanding (SLU) has
been on the end-to-end approach where a single model can predict intents
directly from speech inputs without intermediate transcripts. However, this
approach presents some challenges. First, since speech can be considered as
personally identifiable information, in some cases only automatic speech
recognition (ASR) transcripts are accessible. Second, intent-labeled speech
data is scarce. To address the first challenge, we propose a novel system that
can predict intents from flexible types of inputs: speech, ASR transcripts, or
both. We demonstrate strong performance for either modality separately, and
when both speech and ASR transcripts are available, through system combination,
we achieve better results than using a single input modality. To address the
second challenge, we leverage a semantically robust pre-trained BERT model and
adopt a cross-modal system that co-trains text embeddings and acoustic
embeddings in a shared latent space. We further enhance this system by
utilizing an acoustic module pre-trained on LibriSpeech and domain-adapting the
text module on our target datasets. Our experiments show significant advantages
for these pre-training and fine-tuning strategies, resulting in a system that
achieves competitive intent-classification performance on Snips SLU and Fluent
Speech Commands datasets.

positive:
It has been argued that fake news and the spread of false information pose a
threat to societies throughout the world, from influencing the results of
elections to hindering the efforts to manage the COVID-19 pandemic. To combat
this threat, a number of Natural Language Processing (NLP) approaches have been
developed. These leverage a number of datasets, feature extraction/selection
techniques and machine learning (ML) algorithms to detect fake news before it
spreads. While these methods are well-documented, there is less evidence
regarding their efficacy in this domain. By systematically reviewing the
literature, this paper aims to delineate the approaches for fake news detection
that are most performant, identify limitations with existing approaches, and
suggest ways these can be mitigated. The analysis of the results indicates that
Ensemble Methods using a combination of news content and socially-based
features are currently the most effective. Finally, it is proposed that future
research should focus on developing approaches that address generalisability
issues (which, in part, arise from limitations with current datasets),
explainability and bias.

positive:
Fake news has altered society in negative ways in politics and culture. It
has adversely affected both online social network systems as well as offline
communities and conversations. Using automatic machine learning classification
models is an efficient way to combat the widespread dissemination of fake news.
However, a lack of effective, comprehensive datasets has been a problem for
fake news research and detection model development. Prior fake news datasets do
not provide multimodal text and image data, metadata, comment data, and
fine-grained fake news categorization at the scale and breadth of our dataset.
We present Fakeddit, a novel multimodal dataset consisting of over 1 million
samples from multiple categories of fake news. After being processed through
several stages of review, the samples are labeled according to 2-way, 3-way,
and 6-way classification categories through distant supervision. We construct
hybrid text+image models and perform extensive experiments for multiple
variations of classification, demonstrating the importance of the novel aspect
of multimodality and fine-grained classification unique to Fakeddit.

positive:
Over the last years, there has been an unprecedented proliferation of fake
news. As a consequence, we are more susceptible to the pernicious impact that
misinformation and disinformation spreading can have in different segments of
our society. Thus, the development of tools for automatic detection of fake
news plays and important role in the prevention of its negative effects. Most
attempts to detect and classify false content focus only on using textual
information. Multimodal approaches are less frequent and they typically
classify news either as true or fake. In this work, we perform a fine-grained
classification of fake news on the Fakeddit dataset, using both unimodal and
multimodal approaches. Our experiments show that the multimodal approach based
on a Convolutional Neural Network (CNN) architecture combining text and image
data achieves the best results, with an accuracy of 87%. Some fake news
categories such as Manipulated content, Satire or False connection strongly
benefit from the use of images. Using images also improves the results of the
other categories, but with less impact. Regarding the unimodal approaches using
only text, Bidirectional Encoder Representations from Transformers (BERT) is
the best model with an accuracy of 78%. Therefore, exploiting both text and
image data significantly improves the performance of fake news detection.

positive:
Large-scale dissemination of disinformation online intended to mislead or
deceive the general population is a major societal problem. Rapid progression
in image, video, and natural language generative models has only exacerbated
this situation and intensified our need for an effective defense mechanism.
While existing approaches have been proposed to defend against neural fake
news, they are generally constrained to the very limited setting where articles
only have text and metadata such as the title and authors. In this paper, we
introduce the more realistic and challenging task of defending against
machine-generated news that also includes images and captions. To identify the
possible weaknesses that adversaries can exploit, we create a NeuralNews
dataset composed of 4 different types of generated articles as well as conduct
a series of human user study experiments based on this dataset. In addition to
the valuable insights gleaned from our user study experiments, we provide a
relatively effective approach based on detecting visual-semantic
inconsistencies, which will serve as an effective first line of defense and a
useful reference for future work in defending against machine-generated
disinformation.

positive:
In recent years, misinformation on the Web has become increasingly rampant.
The research community has responded by proposing systems and challenges, which
are beginning to be useful for (various subtasks of) detecting misinformation.
However, most proposed systems are based on deep learning techniques which are
fine-tuned to specific domains, are difficult to interpret and produce results
which are not machine readable. This limits their applicability and adoption as
they can only be used by a select expert audience in very specific settings. In
this paper we propose an architecture based on a core concept of Credibility
Reviews (CRs) that can be used to build networks of distributed bots that
collaborate for misinformation detection. The CRs serve as building blocks to
compose graphs of (i) web content, (ii) existing credibility signals
--fact-checked claims and reputation reviews of websites--, and (iii)
automatically computed reviews. We implement this architecture on top of
lightweight extensions to Schema.org and services providing generic NLP tasks
for semantic similarity and stance detection. Evaluations on existing datasets
of social-media posts, fake news and political speeches demonstrates several
advantages over existing systems: extensibility, domain-independence,
composability, explainability and transparency via provenance. Furthermore, we
obtain competitive results without requiring finetuning and establish a new
state of the art on the Clef'18 CheckThat! Factuality task.

negative:
Fake news and misinformation are a matter of concern for people around the
globe. Users of the internet and social media sites encounter content with
false information much frequently. Fake news detection is one of the most
analyzed and prominent areas of research. These detection techniques apply
popular machine learning and deep learning algorithms. Previous work in this
domain covers fake news detection vastly among text circulating online.
Platforms that have extensively been observed and analyzed include news
websites and Twitter. Facebook, Reddit, WhatsApp, YouTube, and other social
applications are gradually gaining attention in this emerging field.
Researchers are analyzing online data based on multiple modalities composed of
text, image, video, speech, and other contributing factors. The combination of
various modalities has resulted in efficient fake news detection. At present,
there is an abundance of surveys consolidating textual fake news detection
algorithms. This review primarily deals with multi-modal fake news detection
techniques that include images, videos, and their combinations with text. We
provide a comprehensive literature survey of eighty articles presenting
state-of-the-art detection techniques, thereby identifying research gaps and
building a pathway for researchers to further advance this domain.

negative:
With the development of social networks, fake news for various commercial and
political purposes has been appearing in large numbers and gotten widespread in
the online world. With deceptive words, people can get infected by the fake
news very easily and will share them without any fact-checking. For instance,
during the 2016 US president election, various kinds of fake news about the
candidates widely spread through both official news media and the online social
networks. These fake news is usually released to either smear the opponents or
support the candidate on their side. The erroneous information in the fake news
is usually written to motivate the voters' irrational emotion and enthusiasm.
Such kinds of fake news sometimes can bring about devastating effects, and an
important goal in improving the credibility of online social networks is to
identify the fake news timely. In this paper, we propose to study the fake news
detection problem. Automatic fake news identification is extremely hard, since
pure model based fact-checking for news is still an open problem, and few
existing models can be applied to solve the problem. With a thorough
investigation of a fake news data, lots of useful explicit features are
identified from both the text words and images used in the fake news. Besides
the explicit features, there also exist some hidden patterns in the words and
images used in fake news, which can be captured with a set of latent features
extracted via the multiple convolutional layers in our model. A model named as
TI-CNN (Text and Image information based Convolutinal Neural Network) is
proposed in this paper. By projecting the explicit and latent features into a
unified feature space, TI-CNN is trained with both the text and image
information simultaneously. Extensive experiments carried on the real-world
fake news datasets have demonstrate the effectiveness of TI-CNN.

negative:
Recent rapid technological advancements in online social networks such as
Twitter have led to a great incline in spreading false information and fake
news. Misinformation is especially prevalent in the ongoing coronavirus disease
(COVID-19) pandemic, leading to individuals accepting bogus and potentially
deleterious claims and articles. Quick detection of fake news can reduce the
spread of panic and confusion among the public. For our analysis in this paper,
we report a methodology to analyze the reliability of information shared on
social media pertaining to the COVID-19 pandemic. Our best approach is based on
an ensemble of three transformer models (BERT, ALBERT, and XLNET) to detecting
fake news. This model was trained and evaluated in the context of the
ConstraintAI 2021 shared task COVID19 Fake News Detection in English. Our
system obtained 0.9855 f1-score on testset and ranked 5th among 160 teams.

negative:
Social media platforms are vulnerable to fake news dissemination, which
causes negative consequences such as panic and wrong medication in the
healthcare domain. Therefore, it is important to automatically detect fake news
in an early stage before they get widely spread. This paper analyzes the impact
of incorporating content information, prior knowledge, and credibility of
sources into models for the early detection of fake news. We propose a
framework modeling those features by using BERT language model and external
sources, namely Simple English Wikipedia and source reliability tags. The
conducted experiments on CONSTRAINT datasets demonstrated the benefit of
integrating these features for the early detection of fake news in the
healthcare domain.

negative:
As single-task accuracy on individual language and image tasks has improved
substantially in the last few years, the long-term goal of a generally skilled
agent that can both see and talk becomes more feasible to explore. In this
work, we focus on leveraging individual language and image tasks, along with
resources that incorporate both vision and language towards that objective. We
design an architecture that combines state-of-the-art Transformer and ResNeXt
modules fed into a novel attentive multimodal module to produce a combined
model trained on many tasks. We provide a thorough analysis of the components
of the model, and transfer performance when training on one, some, or all of
the tasks. Our final models provide a single system that obtains good results
on all vision and language tasks considered, and improves the state-of-the-art
in image-grounded conversational applications.

negative:
Massive dissemination of fake news and its potential to erode democracy has
increased the demand for accurate fake news detection. Recent advancements in
this area have proposed novel techniques that aim to detect fake news by
exploring how it propagates on social networks. Nevertheless, to detect fake
news at an early stage, i.e., when it is published on a news outlet but not yet
spread on social media, one cannot rely on news propagation information as it
does not exist. Hence, there is a strong need to develop approaches that can
detect fake news by focusing on news content. In this paper, a theory-driven
model is proposed for fake news detection. The method investigates news content
at various levels: lexicon-level, syntax-level, semantic-level and
discourse-level. We represent news at each level, relying on well-established
theories in social and forensic psychology. Fake news detection is then
conducted within a supervised machine learning framework. As an
interdisciplinary research, our work explores potential fake news patterns,
enhances the interpretability in fake news feature engineering, and studies the
relationships among fake news, deception/disinformation, and clickbaits.
Experiments conducted on two real-world datasets indicate the proposed method
can outperform the state-of-the-art and enable fake news early detection when
there is limited content information.

negative:
The proliferation of fake news, i.e., news intentionally spread for
misinformation, poses a threat to individuals and society. Despite various
fact-checking websites such as PolitiFact, robust detection techniques are
required to deal with the increase in fake news. Several deep learning models
show promising results for fake news classification, however, their black-box
nature makes it difficult to explain their classification decisions and
quality-assure the models. We here address this problem by proposing a novel
interpretable fake news detection framework based on the recently introduced
Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to
capture lexical and semantic properties of both true and fake news text.
Further, we use the clause ensembles to calculate the credibility of fake news.
For evaluation, we conduct experiments on two publicly available datasets,
PolitiFact and GossipCop, and demonstrate that the TM framework significantly
outperforms previously published baselines by at least $5\%$ in terms of
accuracy, with the added benefit of an interpretable logic-based
representation. Further, our approach provides higher F1-score than BERT and
XLNet, however, we obtain slightly lower accuracy. We finally present a case
study on our model's explainability, demonstrating how it decomposes into
meaningful words and their negations.

negative:
The rise in online misinformation in recent years threatens democracies by
distorting authentic public discourse and causing confusion, fear, and even, in
extreme cases, violence. There is a need to understand the spread of false
content through online networks for developing interventions that disrupt
misinformation before it achieves virality. Using a Deep Bidirectional
Transformer for Language Understanding (BERT) and propagation graphs, this
study classifies and visualizes the spread of misinformation on a social media
network using publicly available Twitter data. The results confirm prior
research around user clusters and the virality of false content while improving
the precision of deep learning models for misinformation detection. The study
further demonstrates the suitability of BERT for providing a scalable model for
false information detection, which can contribute to the development of more
timely and accurate interventions to slow the spread of misinformation in
online environments.

negative:
In this paper, we introduce UnifiedM2, a general-purpose misinformation model
that jointly models multiple domains of misinformation with a single, unified
setup. The model is trained to handle four tasks: detecting news bias,
clickbait, fake news, and verifying rumors. By grouping these tasks together,
UnifiedM2learns a richer representation of misinformation, which leads to
state-of-the-art or comparable performance across all tasks. Furthermore, we
demonstrate that UnifiedM2's learned representation is helpful for few-shot
learning of unseen misinformation tasks/datasets and model's generalizability
to unseen events.

negative:
Sequence-to-sequence (S2S) modeling is becoming a popular paradigm for
automatic speech recognition (ASR) because of its ability to jointly optimize
all the conventional ASR components in an end-to-end (E2E) fashion. This report
investigates the ability of E2E ASR from standard close-talk to far-field
applications by encompassing entire multichannel speech enhancement and ASR
components within the S2S model. There have been previous studies on jointly
optimizing neural beamforming alongside E2E ASR for denoising. It is clear from
both recent challenge outcomes and successful products that far-field systems
would be incomplete without solving both denoising and dereverberation
simultaneously. This report uses a recently developed architecture for
far-field ASR by composing neural extensions of dereverberation and beamforming
modules with the S2S ASR module as a single differentiable neural network and
also clearly defining the role of each subnetwork. The original implementation
of this architecture was successfully applied to the noisy speech recognition
task (CHiME-4), while we applied this implementation to noisy reverberant tasks
(DIRHA and REVERB). Our investigation shows that the method achieves better
performance than conventional pipeline methods on the DIRHA English dataset and
comparable performance on the REVERB dataset. It also has additional advantages
of being neither iterative nor requiring parallel noisy and clean speech data.

negative:
Social media platforms like Facebook, Twitter, and Instagram have enabled
connection and communication on a large scale. It has revolutionized the rate
at which information is shared and enhanced its reach. However, another side of
the coin dictates an alarming story. These platforms have led to an increase in
the creation and spread of fake news. The fake news has not only influenced
people in the wrong direction but also claimed human lives. During these
critical times of the Covid19 pandemic, it is easy to mislead people and make
them believe in fatal information. Therefore it is important to curb fake news
at source and prevent it from spreading to a larger audience. We look at
automated techniques for fake news detection from a data mining perspective. We
evaluate different supervised text classification algorithms on Contraint@AAAI
2021 Covid-19 Fake news detection dataset. The classification algorithms are
based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM),
and Bidirectional Encoder Representations from Transformers (BERT). We also
evaluate the importance of unsupervised learning in the form of language model
pre-training and distributed word representations using unlabelled covid tweets
corpus. We report the best accuracy of 98.41\% on the Covid-19 Fake news
detection dataset.

negative:
The use of the internet as a fast medium of spreading fake news reinforces
the need for computational tools that combat it. Techniques that train fake
news classifiers exist, but they all assume an abundance of resources including
large labeled datasets and expert-curated corpora, which low-resource languages
may not have. In this work, we make two main contributions: First, we alleviate
resource scarcity by constructing the first expertly-curated benchmark dataset
for fake news detection in Filipino, which we call "Fake News Filipino."
Second, we benchmark Transfer Learning (TL) techniques and show that they can
be used to train robust fake news classifiers from little data, achieving 91%
accuracy on our fake news dataset, reducing the error by 14% compared to
established few-shot baselines. Furthermore, lifting ideas from multitask
learning, we show that augmenting transformer-based transfer techniques with
auxiliary language modeling losses improves their performance by adapting to
writing style. Using this, we improve TL performance by 4-6%, achieving an
accuracy of 96% on our best model. Lastly, we show that our method generalizes
well to different types of news articles, including political news,
entertainment news, and opinion articles.

negative:
The proliferation of fake news and its propagation on social media has become
a major concern due to its ability to create devastating impacts. Different
machine learning approaches have been suggested to detect fake news. However,
most of those focused on a specific type of news (such as political) which
leads us to the question of dataset-bias of the models used. In this research,
we conducted a benchmark study to assess the performance of different
applicable machine learning approaches on three different datasets where we
accumulated the largest and most diversified one. We explored a number of
advanced pre-trained language models for fake news detection along with the
traditional and deep learning ones and compared their performances from
different aspects for the first time to the best of our knowledge. We find that
BERT and similar pre-trained models perform the best for fake news detection,
especially with very small dataset. Hence, these models are significantly
better option for languages with limited electronic contents, i.e., training
data. We also carried out several analysis based on the models' performance,
article's topic, article's length, and discussed different lessons learned from
them. We believe that this benchmark study will help the research community to
explore further and news sites/blogs to select the most appropriate fake news
detection method.

negative:
As the COVID-19 pandemic sweeps across the world, it has been accompanied by
a tsunami of fake news and misinformation on social media. At the time when
reliable information is vital for public health and safety, COVID-19 related
fake news has been spreading even faster than the facts. During times such as
the COVID-19 pandemic, fake news can not only cause intellectual confusion but
can also place lives of people at risk. This calls for an immediate need to
contain the spread of such misinformation on social media. We introduce CTF,
the first COVID-19 Twitter fake news dataset with labeled genuine and fake
tweets. Additionally, we propose Cross-SEAN, a cross-stitch based
semi-supervised end-to-end neural attention model, which leverages the large
amount of unlabelled data. Cross-SEAN partially generalises to emerging fake
news as it learns from relevant external knowledge. We compare Cross-SEAN with
seven state-of-the-art fake news detection methods. We observe that it achieves
$0.95$ F1 Score on CTF, outperforming the best baseline by $9\%$. We also
develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time
detection of fake tweets.

negative:
The viral spread of fake news has caused great social harm, making fake news
detection an urgent task. Current fake news detection methods rely heavily on
text information by learning the extracted news content or writing style of
internal knowledge. However, deliberate rumors can mask writing style,
bypassing language models and invalidating simple text-based models. In fact,
news articles and other related components (such as news creators and news
topics) can be modeled as a heterogeneous information network (HIN for short).
In this paper, we propose a novel fake news detection framework, namely
Hierarchical Graph Attention Network(HGAT), which uses a novel hierarchical
attention mechanism to perform node representation learning in HIN, and then
detects fake news by classifying news article nodes. Experiments on two
real-world fake news datasets show that HGAT can outperform text-based models
and other network-based models. In addition, the experiment proved the
expandability and generalizability of our for graph representation learning and
other node classification related applications in heterogeneous graphs.

negative:
The dissemination of fake news significantly affects personal reputation and
public trust. Recently, fake news detection has attracted tremendous attention,
and previous studies mainly focused on finding clues from news content or
diffusion path. However, the required features of previous models are often
unavailable or insufficient in early detection scenarios, resulting in poor
performance. Thus, early fake news detection remains a tough challenge.
Intuitively, the news from trusted and authoritative sources or shared by many
users with a good reputation is more reliable than other news. Using the
credibility of publishers and users as prior weakly supervised information, we
can quickly locate fake news in massive news and detect them in the early
stages of dissemination.
  In this paper, we propose a novel Structure-aware Multi-head Attention
Network (SMAN), which combines the news content, publishing, and reposting
relations of publishers and users, to jointly optimize the fake news detection
and credibility prediction tasks. In this way, we can explicitly exploit the
credibility of publishers and users for early fake news detection. We conducted
experiments on three real-world datasets, and the results show that SMAN can
detect fake news in 4 hours with an accuracy of over 91%, which is much faster
than the state-of-the-art models.

negative:
Social media for news consumption is a double-edged sword. On the one hand,
its low cost, easy access, and rapid dissemination of information lead people
to seek out and consume news from social media. On the other hand, it enables
the wide spread of "fake news", i.e., low quality news with intentionally false
information. The extensive spread of fake news has the potential for extremely
negative impacts on individuals and society. Therefore, fake news detection on
social media has recently become an emerging research that is attracting
tremendous attention. Fake news detection on social media presents unique
characteristics and challenges that make existing detection algorithms from
traditional news media ineffective or not applicable. First, fake news is
intentionally written to mislead readers to believe false information, which
makes it difficult and nontrivial to detect based on news content; therefore,
we need to include auxiliary information, such as user social engagements on
social media, to help make a determination. Second, exploiting this auxiliary
information is challenging in and of itself as users' social engagements with
fake news produce data that is big, incomplete, unstructured, and noisy.
Because the issue of fake news detection on social media is both challenging
and relevant, we conducted this survey to further facilitate research on the
problem. In this survey, we present a comprehensive review of detecting fake
news on social media, including fake news characterizations on psychology and
social theories, existing algorithms from a data mining perspective, evaluation
metrics and representative datasets. We also discuss related research areas,
open problems, and future research directions for fake news detection on social
media.

negative:
Automatic transcriptions of consumer-generated multi-media content such as
"Youtube" videos still exhibit high word error rates. Such data typically
occupies a very broad domain, has been recorded in challenging conditions, with
cheap hardware and a focus on the visual modality, and may have been
post-processed or edited. In this paper, we extend our earlier work on adapting
the acoustic model of a DNN-based speech recognition system to an RNN language
model and show how both can be adapted to the objects and scenes that can be
automatically detected in the video. We are working on a corpus of "how-to"
videos from the web, and the idea is that an object that can be seen ("car"),
or a scene that is being detected ("kitchen") can be used to condition both
models on the "context" of the recording, thereby reducing perplexity and
improving transcription. We achieve good improvements in both cases and compare
and analyze the respective reductions in word error rate. We expect that our
results can be used for any type of speech processing in which "context"
information is available, for example in robotics, man-machine interaction, or
when indexing large audio-visual archives, and should ultimately help to bring
together the "video-to-text" and "speech-to-text" communities.

negative:
Recently, news consumption using online news portals has increased
exponentially due to several reasons, such as low cost and easy accessibility.
However, such online platforms inadvertently also become the cause of spreading
false information across the web. They are being misused quite frequently as a
medium to disseminate misinformation and hoaxes. Such malpractices call for a
robust automatic fake news detection system that can keep us at bay from such
misinformation and hoaxes. We propose a robust yet simple fake news detection
system, leveraging the tools for paraphrasing, grammar-checking, and
word-embedding. In this paper, we try to the potential of these tools in
jointly unearthing the authenticity of a news article. Notably, we leverage
Spinbot (for paraphrasing), Grammarly (for grammar-checking), and GloVe (for
word-embedding) tools for this purpose. Using these tools, we were able to
extract novel features that could yield state-of-the-art results on the Fake
News AMT dataset and comparable results on Celebrity datasets when combined
with some of the essential features. More importantly, the proposed method is
found to be more robust empirically than the existing ones, as revealed in our
cross-domain analysis and multi-domain analysis.

negative:
Fake News on social media platforms has attracted a lot of attention in
recent times, primarily for events related to politics (2016 US Presidential
elections), healthcare (infodemic during COVID-19), to name a few. Various
methods have been proposed for detecting Fake News. The approaches span from
exploiting techniques related to network analysis, Natural Language Processing
(NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose
DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying
Fake News. Our approach is a combination of the NLP -- where we encode the news
content, and the GNN technique -- where we encode the Knowledge Graph (KG). A
variety of these encodings provides a complementary advantage to our detector.
We evaluate our framework using two publicly available datasets containing
articles from domains such as politics, business, technology, and healthcare.
As part of dataset pre-processing, we also remove the bias, such as the source
of the articles, which could impact the performance of the models. DEAP-FAKED
obtains an F1-score of 88% and 78% for the two datasets, which is an
improvement of 21%, and 3% respectively, which shows the effectiveness of the
approach.

negative:
Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural language description. In this context, we propose an
approach that successfully takes into account both the local and global
temporal structure of videos to produce descriptions. First, our approach
incorporates a spatial temporal 3-D convolutional neural network (3-D CNN)
representation of the short temporal dynamics. The 3-D CNN representation is
trained on video action recognition tasks, so as to produce a representation
that is tuned to human motion and behavior. Second we propose a temporal
attention mechanism that allows to go beyond local temporal modeling and learns
to automatically select the most relevant temporal segments given the
text-generating RNN. Our approach exceeds the current state-of-art for both
BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on
a new, larger and more challenging dataset of paired video and natural language
descriptions.

negative:
Disinformation through fake news is an ongoing problem in our society and has
become easily spread through social media. The most cost and time effective way
to filter these large amounts of data is to use a combination of human and
technical interventions to identify it. From a technical perspective, Natural
Language Processing (NLP) is widely used in detecting fake news. Social media
companies use NLP techniques to identify the fake news and warn their users,
but fake news may still slip through undetected. It is especially a problem in
more localised contexts (outside the United States of America). How do we
adjust fake news detection systems to work better for local contexts such as in
South Africa. In this work we investigate fake news detection on South African
websites. We curate a dataset of South African fake news and then train
detection models. We contrast this with using widely available fake news
datasets (from mostly USA website). We also explore making the datasets more
diverse by combining them and observe the differences in behaviour in writing
between nations' fake news using interpretable machine learning.

negative:
Recent approaches in literature have exploited the multi-modal information in
documents (text, layout, image) to serve specific downstream document tasks.
However, they are limited by their - (i) inability to learn cross-modal
representations across text, layout and image dimensions for documents and (ii)
inability to process multi-page documents. Pre-training techniques have been
shown in Natural Language Processing (NLP) domain to learn generic textual
representations from large unlabelled datasets, applicable to various
downstream NLP tasks. In this paper, we propose a multi-task learning-based
framework that utilizes a combination of self-supervised and supervised
pre-training tasks to learn a generic document representation applicable to
various downstream document tasks. Specifically, we introduce Document Topic
Modelling and Document Shuffle Prediction as novel pre-training tasks to learn
rich image representations along with the text and layout representations for
documents. We utilize the Longformer network architecture as the backbone to
encode the multi-modal information from multi-page documents in an end-to-end
fashion. We showcase the applicability of our pre-training framework on a
variety of different real-world document tasks such as document classification,
document information extraction, and document retrieval. We evaluate our
framework on different standard document datasets and conduct exhaustive
experiments to compare performance against various ablations of our framework
and state-of-the-art baselines.

negative:
Spoken language understanding is typically based on pipeline architectures
including speech recognition and natural language understanding steps. These
components are optimized independently to allow usage of available data, but
the overall system suffers from error propagation. In this paper, we propose a
novel training method that enables pretrained contextual embeddings to process
acoustic features. In particular, we extend it with an encoder of pretrained
speech recognition systems in order to construct end-to-end spoken language
understanding systems. Our proposed method is based on the teacher-student
framework across speech and text modalities that aligns the acoustic and the
semantic latent spaces. Experimental results in three benchmarks show that our
system reaches the performance comparable to the pipeline architecture without
using any training data and outperforms it after fine-tuning with ten examples
per class on two out of three benchmarks.

negative:
During the COVID-19 pandemic, social media platforms were ideal for
communicating due to social isolation and quarantine. Also, it was the primary
source of misinformation dissemination on a large scale, referred to as the
infodemic. Therefore, automatic debunking misinformation is a crucial problem.
To tackle this problem, we present two COVID-19 related misinformation datasets
on Twitter and propose a misinformation detection system comprising
network-based and content-based processes based on machine learning algorithms
and NLP techniques. In the network-based process, we focus on social
properties, network characteristics, and users. On the other hand, we classify
misinformation using the content of the tweets directly in the content-based
process, which contains text classification models (paragraph-level and
sentence-level) and similarity models. The evaluation results on the
network-based process show the best results for the artificial neural network
model with an F1 score of 88.68%. In the content-based process, our novel
similarity models, which obtained an F1 score of 90.26%, show an improvement in
the misinformation classification results compared to the network-based models.
In addition, in the text classification models, the best result was achieved
using the stacking ensemble-learning model by obtaining an F1 score of 95.18%.
Furthermore, we test our content-based models on the Constraint@AAAI2021
dataset, and by getting an F1 score of 94.38%, we improve the baseline results.
Finally, we develop a fact-checking website called Checkovid that uses each
process to detect misinformative and informative claims in the domain of
COVID-19 from different perspectives.

negative:
Fake news detection is a very prominent and essential task in the field of
journalism. This challenging problem is seen so far in the field of politics,
but it could be even more challenging when it is to be determined in the
multi-domain platform. In this paper, we propose two effective models based on
deep learning for solving fake news detection problem in online news contents
of multiple domains. We evaluate our techniques on the two recently released
datasets, namely FakeNews AMT and Celebrity for fake news detection. The
proposed systems yield encouraging performance, outperforming the current
handcrafted feature engineering based state-of-the-art system with a
significant margin of 3.08% and 9.3% by the two models, respectively. In order
to exploit the datasets, available for the related tasks, we perform
cross-domain analysis (i.e. model trained on FakeNews AMT and tested on
Celebrity and vice versa) to explore the applicability of our systems across
the domains.

negative:
The rampant integration of social media in our every day lives and culture
has given rise to fast and easier access to the flow of information than ever
in human history. However, the inherently unsupervised nature of social media
platforms has also made it easier to spread false information and fake news.
Furthermore, the high volume and velocity of information flow in such platforms
make manual supervision and control of information propagation infeasible. This
paper aims to address this issue by proposing a novel deep learning approach
for automated detection of false short-text claims on social media. We first
introduce Sentimental LIAR, which extends the LIAR dataset of short claims by
adding features based on sentiment and emotion analysis of claims. Furthermore,
we propose a novel deep learning architecture based on the BERT-Base language
model for classification of claims as genuine or fake. Our results demonstrate
that the proposed architecture trained on Sentimental LIAR can achieve an
accuracy of 70%, which is an improvement of ~30% over previously reported
results for the LIAR benchmark.

negative:
Fake news is a severe problem in social media. In this paper, we present an
empirical study on visual, textual, and multimodal models for the tasks of
claim, claim check-worthiness, and conspiracy detection, all of which are
related to fake news detection. Recent work suggests that images are more
influential than text and often appear alongside fake text. To this end,
several multimodal models have been proposed in recent years that use images
along with text to detect fake news on social media sites like Twitter.
However, the role of images is not well understood for claim detection,
specifically using transformer-based textual and multimodal models. We
investigate state-of-the-art models for images, text (Transformer-based), and
multimodal information for four different datasets across two languages to
understand the role of images in the task of claim and conspiracy detection.

negative:
Both politics and pandemics have recently provided ample motivation for the
development of machine learning-enabled disinformation (a.k.a. fake news)
detection algorithms. Existing literature has focused primarily on the
fully-automated case, but the resulting techniques cannot reliably detect
disinformation on the varied topics, sources, and time scales required for
military applications. By leveraging an already-available analyst as a
human-in-the-loop, however, the canonical machine learning techniques of
sentiment analysis, aspect-based sentiment analysis, and stance detection
become plausible methods to use for a partially-automated disinformation
detection system. This paper aims to determine which of these techniques is
best suited for this purpose and how each technique might best be used towards
this end. Training datasets of the same size and nearly identical neural
architectures (a BERT transformer as a word embedder with a single feed-forward
layer thereafter) are used for each approach, which are then tested on
sentiment- and stance-specific datasets to establish a baseline of how well
each method can be used to do the other tasks. Four different datasets relating
to COVID-19 disinformation are used to test the ability of each technique to
detect disinformation on a topic that did not appear in the training data set.
Quantitative and qualitative results from these tests are then used to provide
insight into how best to employ these techniques in practice.

negative:
Spoken Language Understanding (SLU) typically comprises of an automatic
speech recognition (ASR) followed by a natural language understanding (NLU)
module. The two modules process signals in a blocking sequential fashion, i.e.,
the NLU often has to wait for the ASR to finish processing on an utterance
basis, potentially leading to high latencies that render the spoken interaction
less natural. In this paper, we propose recurrent neural network (RNN) based
incremental processing towards the SLU task of intent detection. The proposed
methodology offers lower latencies than a typical SLU system, without any
significant reduction in system accuracy. We introduce and analyze different
recurrent neural network architectures for incremental and online processing of
the ASR transcripts and compare it to the existing offline systems. A lexical
End-of-Sentence (EOS) detector is proposed for segmenting the stream of
transcript into sentences for intent classification. Intent detection
experiments are conducted on benchmark ATIS, Snips and Facebook's multilingual
task oriented dialog datasets modified to emulate a continuous incremental
stream of words with no utterance demarcation. We also analyze the prospects of
early intent detection, before EOS, with our proposed system.

negative:
Recent advancements in the area of Computer Vision with state-of-art Neural
Networks has given a boost to Optical Character Recognition (OCR) accuracies.
However, extracting characters/text alone is often insufficient for relevant
information extraction as documents also have a visual structure that is not
captured by OCR. Extracting information from tables, charts, footnotes, boxes,
headings and retrieving the corresponding structured representation for the
document remains a challenge and finds application in a large number of
real-world use cases. In this paper, we propose a novel enterprise based
end-to-end framework called DeepReader which facilitates information extraction
from document images via identification of visual entities and populating a
meta relational model across different entities in the document image. The
model schema allows for an easy to understand abstraction of the entities
detected by the deep vision models and the relationships between them.
DeepReader has a suite of state-of-the-art vision algorithms which are applied
to recognize handwritten and printed text, eliminate noisy effects, identify
the type of documents and detect visual entities like tables, lines and boxes.
Deep Reader maps the extracted entities into a rich relational schema so as to
capture all the relevant relationships between entities (words, textboxes,
lines etc) detected in the document. Relevant information and fields can then
be extracted from the document by writing SQL queries on top of the
relationship tables. A natural language based interface is added on top of the
relationship schema so that a non-technical user, specifying the queries in
natural language, can fetch the information with minimal effort. In this paper,
we also demonstrate many different capabilities of Deep Reader and report
results on a real-world use case.

negative:
The authors of fake news often use facts from verified news sources and mix
them with misinformation to create confusion and provoke unrest among the
readers. The spread of fake news can thereby have serious implications on our
society. They can sway political elections, push down the stock price or crush
reputations of corporations or public figures. Several websites have taken on
the mission of checking rumors and allegations, but are often not fast enough
to check the content of all the news being disseminated. Especially social
media websites have offered an easy platform for the fast propagation of
information. Towards limiting fake news from being propagated among social
media users, the task of this year's PAN 2020 challenge lays the focus on the
fake news spreaders. The aim of the task is to determine whether it is possible
to discriminate authors that have shared fake news in the past from those that
have never done it. In this notebook, we describe our profiling system for the
fake news detection task on Twitter. For this, we conduct different feature
extraction techniques and learning experiments from a multilingual perspective,
namely English and Spanish. Our final submitted systems use character n-grams
as features in combination with a linear SVM for English and Logistic
Regression for the Spanish language. Our submitted models achieve an overall
accuracy of 73% and 79% on the English and Spanish official test set,
respectively. Our experiments show that it is difficult to differentiate
solidly fake news spreaders on Twitter from users who share credible
information leaving room for further investigations. Our model ranked 3rd out
of 72 competitors.

negative:
In a modern spoken language understanding (SLU) system, the natural language
understanding (NLU) module takes interpretations of a speech from the automatic
speech recognition (ASR) module as the input. The NLU module usually uses the
first best interpretation of a given speech in downstream tasks such as domain
and intent classification. However, the ASR module might misrecognize some
speeches and the first best interpretation could be erroneous and noisy. Solely
relying on the first best interpretation could make the performance of
downstream tasks non-optimal. To address this issue, we introduce a series of
simple yet efficient models for improving the understanding of semantics of the
input speeches by collectively exploiting the n-best speech interpretations
from the ASR module.

negative:
Observing the damages that can be done by the rapid propagation of fake news
in various sectors like politics and finance, automatic identification of fake
news using linguistic analysis has drawn the attention of the research
community. However, such methods are largely being developed for English where
low resource languages remain out of the focus. But the risks spawned by fake
and manipulative news are not confined by languages. In this work, we propose
an annotated dataset of ~50K news that can be used for building automated fake
news detection systems for a low resource language like Bangla. Additionally,
we provide an analysis of the dataset and develop a benchmark system with state
of the art NLP techniques to identify Bangla fake news. To create this system,
we explore traditional linguistic features and neural network based methods. We
expect this dataset will be a valuable resource for building technologies to
prevent the spreading of fake news and contribute in research with low resource
languages.

negative:
Today social media has become the primary source for news. Via social media
platforms, fake news travel at unprecedented speeds, reach global audiences and
put users and communities at great risk. Therefore, it is extremely important
to detect fake news as early as possible. Recently, deep learning based
approaches have shown improved performance in fake news detection. However, the
training of such models requires a large amount of labeled data, but manual
annotation is time-consuming and expensive. Moreover, due to the dynamic nature
of news, annotated samples may become outdated quickly and cannot represent the
news articles on newly emerged events. Therefore, how to obtain fresh and
high-quality labeled samples is the major challenge in employing deep learning
models for fake news detection. In order to tackle this challenge, we propose a
reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which
can leverage users' reports as weak supervision to enlarge the amount of
training data for fake news detection. The proposed framework consists of three
main components: the annotator, the reinforced selector and the fake news
detector. The annotator can automatically assign weak labels for unlabeled news
based on users' reports. The reinforced selector using reinforcement learning
techniques chooses high-quality samples from the weakly labeled data and
filters out those low-quality ones that may degrade the detector's prediction
performance. The fake news detector aims to identify fake news based on the
news content. We tested the proposed framework on a large collection of news
articles published via WeChat official accounts and associated user reports.
Extensive experiments on this dataset show that the proposed WeFEND model
achieves the best performance compared with the state-of-the-art methods.

negative:
The proliferation of misleading information in everyday access media outlets
such as social media feeds, news blogs, and online newspapers have made it
challenging to identify trustworthy news sources, thus increasing the need for
computational tools able to provide insights into the reliability of online
content. In this paper, we focus on the automatic identification of fake
content in online news. Our contribution is twofold. First, we introduce two
novel datasets for the task of fake news detection, covering seven different
news domains. We describe the collection, annotation, and validation process in
detail and present several exploratory analysis on the identification of
linguistic differences in fake and legitimate news content. Second, we conduct
a set of learning experiments to build accurate fake news detectors. In
addition, we provide comparative analyses of the automatic and manual
identification of fake news.

negative:
Standard neural machine translation (NMT) is on the assumption of
document-level context independent. Most existing document-level NMT methods
are satisfied with a smattering sense of brief document-level information,
while this work focuses on exploiting detailed document-level context in terms
of multiple forms of document embeddings, which is capable of sufficiently
modeling deeper and richer document-level context. The proposed document-aware
NMT is implemented to enhance the Transformer baseline by introducing both
global and local document-level clues on the source end. Experiments show that
the proposed method significantly improves the translation performance over
strong baselines and other related studies.

negative:
Fake news is a type of pervasive propaganda that spreads misinformation
online, taking advantage of social media's extensive reach to manipulate public
perception. Over the past three years, fake news has become a focal discussion
point in the media due to its impact on the 2016 U.S. presidential election.
Fake news can have severe real-world implications: in 2016, a man walked into a
pizzeria carrying a rifle because he read that Hillary Clinton was harboring
children as sex slaves. This project presents a high accuracy (87%) machine
learning classifier that determines the validity of news based on the word
distributions and specific linguistic and stylistic differences in the first
few sentences of an article. This can help readers identify the validity of an
article by looking for specific features in the opening lines aiding them in
making informed decisions. Using a dataset of 2,107 articles from 30 different
websites, this project establishes an understanding of the variations between
fake and credible news by examining the model, dataset, and features. This
classifier appears to use the differences in word distribution, levels of tone
authenticity, and frequency of adverbs, adjectives, and nouns. The
differentiation in the features of these articles can be used to improve future
classifiers. This classifier can also be further applied directly to browsers
as a Google Chrome extension or as a filter for social media outlets or news
websites to reduce the spread of misinformation.

negative:
Recent studies on machine reading comprehension have focused on text-level
understanding but have not yet reached the level of human understanding of the
visual layout and content of real-world documents. In this study, we introduce
a new visual machine reading comprehension dataset, named VisualMRC, wherein
given a question and a document image, a machine reads and comprehends texts in
the image to answer the question in natural language. Compared with existing
visual question answering (VQA) datasets that contain texts in images,
VisualMRC focuses more on developing natural language understanding and
generation abilities. It contains 30,000+ pairs of a question and an
abstractive answer for 10,000+ document images sourced from multiple domains of
webpages. We also introduce a new model that extends existing
sequence-to-sequence models, pre-trained with large-scale text corpora, to take
into account the visual layout and content of documents. Experiments with
VisualMRC show that this model outperformed the base sequence-to-sequence
models and a state-of-the-art VQA model. However, its performance is still
below that of humans on most automatic evaluation metrics. The dataset will
facilitate research aimed at connecting vision and language understanding.

negative:
The effective incorporation of cross-utterance information has the potential
to improve language models (LMs) for automatic speech recognition (ASR). To
extract more powerful and robust cross-utterance representations for the
Transformer LM (TLM), this paper proposes the R-TLM which uses hidden states in
a long short-term memory (LSTM) LM. To encode the cross-utterance information,
the R-TLM incorporates an LSTM module together with a segment-wise recurrence
in some of the Transformer blocks. In addition to the LSTM module output, a
shortcut connection using a fusion layer that bypasses the LSTM module is also
investigated. The proposed system was evaluated on the AMI meeting corpus, the
Eval2000 and the RT03 telephone conversation evaluation sets. The best R-TLM
achieved 0.9%, 0.6%, and 0.8% absolute WER reductions over the single-utterance
TLM baseline, and 0.5%, 0.3%, 0.2% absolute WER reductions over a strong
cross-utterance TLM baseline on the AMI evaluation set, Eval2000 and RT03
respectively. Improvements on Eval2000 and RT03 were further supported by
significance tests. R-TLMs were found to have better LM scores on words where
recognition errors are more likely to occur. The R-TLM WER can be further
reduced by interpolation with an LSTM-LM.

negative:
Fake news has now grown into a big problem for societies and also a major
challenge for people fighting disinformation. This phenomenon plagues
democratic elections, reputations of individual persons or organizations, and
has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US
or Brazil). Hence, developing effective tools to fight this phenomenon by
employing advanced Machine Learning (ML) methods poses a significant challenge.
The following paper displays the present body of knowledge on the application
of such intelligent tools in the fight against disinformation. It starts by
showing the historical perspective and the current role of fake news in the
information war. Proposed solutions based solely on the work of experts are
analysed and the most important directions of the application of intelligent
systems in the detection of misinformation sources are pointed out.
Additionally, the paper presents some useful resources (mainly datasets useful
when assessing ML solutions for fake news detection) and provides a short
overview of the most important R&D projects related to this subject. The main
purpose of this work is to analyse the current state of knowledge in detecting
fake news; on the one hand to show possible solutions, and on the other hand to
identify the main challenges and methodological gaps to motivate future
research.

negative:
Localizing moments in a longer video via natural language queries is a new,
challenging task at the intersection of language and video understanding.
Though moment localization with natural language is similar to other language
and vision tasks like natural language object retrieval in images, moment
localization offers an interesting opportunity to model temporal dependencies
and reasoning in text. We propose a new model that explicitly reasons about
different temporal segments in a video, and shows that temporal context is
important for localizing phrases which include temporal language. To benchmark
whether our model, and other recent video localization models, can effectively
reason about temporal language, we collect the novel TEMPOral reasoning in
video and language (TEMPO) dataset. Our dataset consists of two parts: a
dataset with real videos and template sentences (TEMPO - Template Language)
which allows for controlled studies on temporal language, and a human language
dataset which consists of temporal sentences annotated by humans (TEMPO - Human
Language).

negative:
Nowadays, social network platforms have been the prime source for people to
experience news and events due to their capacities to spread information
rapidly, which inevitably provides a fertile ground for the dissemination of
fake news. Thus, it is significant to detect fake news otherwise it could cause
public misleading and panic. Existing deep learning models have achieved great
progress to tackle the problem of fake news detection. However, training an
effective deep learning model usually requires a large amount of labeled news,
while it is expensive and time-consuming to provide sufficient labeled news in
actual applications. To improve the detection performance of fake news, we take
advantage of the event correlations of news and propose an event correlation
filtering method (ECFM) for fake news detection, mainly consisting of the news
characterizer, the pseudo label annotator, the event credibility updater, and
the news entropy selector. The news characterizer is responsible for extracting
textual features from news, which cooperates with the pseudo label annotator to
assign pseudo labels for unlabeled news by fully exploiting the event
correlations of news. In addition, the event credibility updater employs
adaptive Kalman filter to weaken the credibility fluctuations of events. To
further improve the detection performance, the news entropy selector
automatically discovers high-quality samples from pseudo labeled news by
quantifying their news entropy. Finally, ECFM is proposed to integrate them to
detect fake news in an event correlation filtering manner. Extensive
experiments prove that the explainable introduction of the event correlations
of news is beneficial to improve the detection performance of fake news.

negative:
The increasing occurrence, forms, and negative effects of misinformation on
social media platforms has necessitated more misinformation detection tools.
Currently, work is being done addressing COVID-19 misinformation however, there
are no misinformation detection tools for any of the 40 distinct indigenous
Ugandan languages. This paper addresses this gap by presenting basic language
resources and a misinformation detection data set based on code-mixed
Luganda-English messages sourced from the Facebook and Twitter social media
platforms. Several machine learning methods are applied on the misinformation
detection data set to develop classification models for detecting whether a
code-mixed Luganda-English message contains misinformation or not. A 10-fold
cross validation evaluation of the classification methods in an experimental
misinformation detection task shows that a Discriminative Multinomial Naive
Bayes (DMNB) method achieves the highest accuracy and F-measure of 78.19% and
77.90% respectively. Also, Support Vector Machine and Bagging ensemble
classification models achieve comparable results. These results are promising
since the machine learning models are based on n-gram features from only the
misinformation detection dataset.

negative:
In many natural language processing (NLP) tasks, a document is commonly
modeled as a bag of words using the term frequency-inverse document frequency
(TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature
vector is that it ignores word orders that carry syntactic and semantic
relationships among the words in a document, and they can be important in some
NLP tasks such as genre classification. This paper proposes a novel distributed
vector representation of a document: a simple recurrent-neural-network language
model (RNN-LM) or a long short-term memory RNN language model (LSTM-LM) is
first created from all documents in a task; some of the LM parameters are then
adapted by each document, and the adapted parameters are vectorized to
represent the document. The new document vectors are labeled as DV-RNN and
DV-LSTM respectively. We believe that our new document vectors can capture some
high-level sequential information in the documents, which other current
document representations fail to capture. The new document vectors were
evaluated in the genre classification of documents in three corpora: the Brown
Corpus, the BNC Baby Corpus and an artificially created Penn Treebank dataset.
Their classification performances are compared with the performance of TF-IDF
vector and the state-of-the-art distributed memory model of paragraph vector
(PV-DM). The results show that DV-LSTM significantly outperforms TF-IDF and
PV-DM in most cases, and combinations of the proposed document vectors with
TF-IDF or PV-DM may further improve performance.

negative:
Cross-document co-reference resolution (CDCR) is the task of identifying and
linking mentions to entities and concepts across many text documents. Current
state-of-the-art models for this task assume that all documents are of the same
type (e.g. news articles) or fall under the same theme. However, it is also
desirable to perform CDCR across different domains (type or theme). A
particular use case we focus on in this paper is the resolution of entities
mentioned across scientific work and newspaper articles that discuss them.
Identifying the same entities and corresponding concepts in both scientific
articles and news can help scientists understand how their work is represented
in mainstream media. We propose a new task and English language dataset for
cross-document cross-domain co-reference resolution (CD$^2$CR). The task aims
to identify links between entities across heterogeneous document types. We show
that in this cross-domain, cross-document setting, existing CDCR models do not
perform well and we provide a baseline model that outperforms current
state-of-the-art CDCR models on CD$^2$CR. Our data set, annotation tool and
guidelines as well as our model for cross-document cross-domain co-reference
are all supplied as open access open source resources.

negative:
The significance of social media has increased manifold in the past few
decades as it helps people from even the most remote corners of the world to
stay connected. With the advent of technology, digital media has become more
relevant and widely used than ever before and along with this, there has been a
resurgence in the circulation of fake news and tweets that demand immediate
attention. In this paper, we describe a novel Fake News Detection system that
automatically identifies whether a news item is "real" or "fake", as an
extension of our work in the CONSTRAINT COVID-19 Fake News Detection in English
challenge. We have used an ensemble model consisting of pre-trained models
followed by a statistical feature fusion network , along with a novel heuristic
algorithm by incorporating various attributes present in news items or tweets
like source, username handles, URL domains and authors as statistical feature.
Our proposed framework have also quantified reliable predictive uncertainty
along with proper class output confidence level for the classification task. We
have evaluated our results on the COVID-19 Fake News dataset and FakeNewsNet
dataset to show the effectiveness of the proposed algorithm on detecting fake
news in short news content as well as in news articles. We obtained a best
F1-score of 0.9892 on the COVID-19 dataset, and an F1-score of 0.9073 on the
FakeNewsNet dataset.

negative:
Current methods for learning visually grounded language from videos often
rely on text annotation, such as human generated captions or machine generated
automatic speech recognition (ASR) transcripts. In this work, we introduce the
Audio-Video Language Network (AVLnet), a self-supervised network that learns a
shared audio-visual embedding space directly from raw video inputs. To
circumvent the need for text annotation, we learn audio-visual representations
from randomly segmented video clips and their raw audio waveforms. We train
AVLnet on HowTo100M, a large corpus of publicly available instructional videos,
and evaluate on image retrieval and video retrieval tasks, achieving
state-of-the-art performance. We perform analysis of AVLnet's learned
representations, showing our model utilizes speech and natural sounds to learn
audio-visual concepts. Further, we propose a tri-modal model that jointly
processes raw audio, video, and text captions from videos to learn a
multi-modal semantic embedding space useful for text-video retrieval. Our code,
data, and trained models will be released at avlnet.csail.mit.edu

negative:
The evolution of electronic media is a mixed blessing. Due to the easy
access, low cost, and faster reach of the information, people search out and
devour news from online social networks. In contrast, the increasing acceptance
of social media reporting leads to the spread of fake news. This is a minacious
problem that causes disputes and endangers societal stability and harmony. Fake
news spread has gained attention from researchers due to its vicious nature.
proliferation of misinformation in all media, from the internet to cable news,
paid advertising and local news outlets, has made it essential for people to
identify the misinformation and sort through the facts. Researchers are trying
to analyze the credibility of information and curtail false information on such
platforms. Credibility is the believability of the piece of information at
hand. Analyzing the credibility of fake news is challenging due to the intent
of its creation and the polychromatic nature of the news. In this work, we
propose a model for detecting fake news. Our method investigates the content of
the news at the early stage i.e. when the news is published but is yet to be
disseminated through social media. Our work interprets the content with
automatic feature extraction and the relevance of the text pieces. In summary,
we introduce stance as one of the features along with the content of the
article and employ the pre-trained contextualized word embeddings BERT to
obtain the state-of-art results for fake news detection. The experiment
conducted on the real-world dataset indicates that our model outperforms the
previous work and enables fake news detection with an accuracy of 95.32%.

negative:
With the rapid evolution of social media, fake news has become a significant
social problem, which cannot be addressed in a timely manner using manual
investigation. This has motivated numerous studies on automating fake news
detection. Most studies explore supervised training models with different
modalities (e.g., text, images, and propagation networks) of news records to
identify fake news. However, the performance of such techniques generally drops
if news records are coming from different domains (e.g., politics,
entertainment), especially for domains that are unseen or rarely-seen during
training. As motivation, we empirically show that news records from different
domains have significantly different word usage and propagation patterns.
Furthermore, due to the sheer volume of unlabelled news records, it is
challenging to select news records for manual labelling so that the
domain-coverage of the labelled dataset is maximized. Hence, this work: (1)
proposes a novel framework that jointly preserves domain-specific and
cross-domain knowledge in news records to detect fake news from different
domains; and (2) introduces an unsupervised technique to select a set of
unlabelled informative news records for manual labelling, which can be
ultimately used to train a fake news detection model that performs well for
many domains while minimizing the labelling cost. Our experiments show that the
integration of the proposed fake news model and the selective annotation
approach achieves state-of-the-art performance for cross-domain news datasets,
while yielding notable improvements for rarely-appearing domains in news
datasets.

negative:
The proliferation of fake news on social media has opened up new directions
of research for timely identification and containment of fake news, and
mitigation of its widespread impact on public opinion. While much of the
earlier research was focused on identification of fake news based on its
contents or by exploiting users' engagements with the news on social media,
there has been a rising interest in proactive intervention strategies to
counter the spread of misinformation and its impact on society. In this survey,
we describe the modern-day problem of fake news and, in particular, highlight
the technical challenges associated with it. We discuss existing methods and
techniques applicable to both identification and mitigation, with a focus on
the significant advances in each method and their advantages and limitations.
In addition, research has often been limited by the quality of existing
datasets and their specific application contexts. To alleviate this problem, we
comprehensively compile and summarize characteristic features of available
datasets. Furthermore, we outline new directions of research to facilitate
future development of effective and interdisciplinary solutions.

negative:
In general, self help systems are being increasingly deployed by service
based industries because they are capable of delivering better customer service
and increasingly the switch is to voice based self help systems because they
provide a natural interface for a human to interact with a machine. A speech
based self help system ideally needs a speech recognition engine to convert
spoken speech to text and in addition a language processing engine to take care
of any misrecognitions by the speech recognition engine. Any off-the-shelf
speech recognition engine is generally a combination of acoustic processing and
speech grammar. While this is the norm, we believe that ideally a speech
recognition application should have in addition to a speech recognition engine
a separate language processing engine to give the system better performance. In
this paper, we discuss ways in which the speech recognition engine and the
language processing engine can be combined to give a better user experience.

negative:
Many users turn to document retrieval systems (e.g. search engines) to seek
answers to controversial questions. Answering such user queries usually require
identifying responses within web documents, and aggregating the responses based
on their different perspectives.
  Classical document retrieval systems fall short at delivering a set of direct
and diverse responses to the users. Naturally, identifying such responses
within a document is a natural language understanding task. In this paper, we
examine the challenges of synthesizing such language understanding objectives
with document retrieval, and study a new perspective-oriented document
retrieval paradigm. We discuss and assess the inherent natural language
understanding challenges in order to achieve the goal. Following the design
challenges and principles, we demonstrate and evaluate a practical prototype
pipeline system. We use the prototype system to conduct a user survey in order
to assess the utility of our paradigm, as well as understanding the user
information needs for controversial queries.

negative:
Fake news can significantly misinform people who often rely on online sources
and social media for their information. Current research on fake news detection
has mostly focused on analyzing fake news content and how it propagates on a
network of users. In this paper, we emphasize the detection of fake news by
assessing its credibility. By analyzing public fake news data, we show that
information on news sources (and authors) can be a strong indicator of
credibility. Our findings suggest that an author's history of association with
fake news, and the number of authors of a news article, can play a significant
role in detecting fake news. Our approach can help improve traditional fake
news detection methods, wherein content features are often used to detect fake
news.

negative:
In this paper, we introduce Iterative Text Summarization (ITS), an
iteration-based model for supervised extractive text summarization, inspired by
the observation that it is often necessary for a human to read an article
multiple times in order to fully understand and summarize its contents. Current
summarization approaches read through a document only once to generate a
document representation, resulting in a sub-optimal representation. To address
this issue we introduce a model which iteratively polishes the document
representation on many passes through the document. As part of our model, we
also introduce a selective reading mechanism that decides more accurately the
extent to which each sentence in the model should be updated. Experimental
results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model
significantly outperforms state-of-the-art extractive systems when evaluated by
machines and by humans.

negative:
This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for
detecting and observing the spread of misinformation about this disease within
texts. CMTA proposes a data science (DS) pipeline that applies machine learning
models for processing, classifying (Dense-CNN) and analyzing (MBERT)
multilingual (micro)-texts. DS pipeline data preparation tasks extract features
from multilingual textual data and categorize it into specific information
classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has
been experimented with multilingual micro-texts (tweets), showing
misinformation spread across different languages. To assess the performance of
CMTA and put it in perspective, we performed a comparative analysis of CMTA
with eight monolingual models used for detecting misinformation. The comparison
shows that CMTA has surpassed various monolingual models and suggests that it
can be used as a general method for detecting misinformation in multilingual
micro-texts. CMTA experimental results show misinformation trends about
COVID-19 in different languages during the first pandemic months.

negative:
Fake news detection is a critical yet challenging problem in Natural Language
Processing (NLP). The rapid rise of social networking platforms has not only
yielded a vast increase in information accessibility but has also accelerated
the spread of fake news. Thus, the effect of fake news has been growing,
sometimes extending to the offline world and threatening public safety. Given
the massive amount of Web content, automatic fake news detection is a practical
NLP problem useful to all online content providers, in order to reduce the
human time and effort to detect and prevent the spread of fake news. In this
paper, we describe the challenges involved in fake news detection and also
describe related tasks. We systematically review and compare the task
formulations, datasets and NLP solutions that have been developed for this
task, and also discuss the potentials and limitations of them. Based on our
insights, we outline promising research directions, including more
fine-grained, detailed, fair, and practical detection models. We also highlight
the difference between fake news detection and other related tasks, and the
importance of NLP solutions for fake news detection.

negative:
WhatsApp emerged as a major communication platform in many countries in the
recent years. Despite offering only one-to-one and small group conversations,
WhatsApp has been shown to enable the formation of a rich underlying network,
crossing the boundaries of existing groups, and with structural properties that
favor information dissemination at large. Indeed, WhatsApp has reportedly been
used as a forum of misinformation campaigns with significant social, political
and economic consequences in several countries. In this article, we aim at
complementing recent studies on misinformation spread on WhatsApp, mostly
focused on content properties and propagation dynamics, by looking into the
network that connects users sharing the same piece of content. Specifically, we
present a hierarchical network-oriented characterization of the users engaged
in misinformation spread by focusing on three perspectives: individuals,
WhatsApp groups and user communities, i.e., groupings of users who,
intentionally or not, share the same content disproportionately often. By
analyzing sharing and network topological properties, our study offers valuable
insights into how WhatsApp users leverage the underlying network connecting
different groups to gain large reach in the spread of misinformation on the
platform.

negative:
News recommendation is an effective information dissemination solution in
modern society. While recent years have witnessed many promising news
recommendation models, they mostly capture the user-news interactions on the
document-level in a static manner. However, in real-world scenarios, the news
can be quite complex and diverse, blindly squeezing all the contents into an
embedding vector can be less effective in extracting information compatible
with the personalized preference of the users. In addition, user preferences in
the news recommendation scenario can be highly dynamic, and a tailored dynamic
mechanism should be designed for better recommendation performance. In this
paper, we propose a novel dynamic news recommender model. For better
understanding the news content, we leverage the attention mechanism to
represent the news from the sentence-, element- and document-levels,
respectively. For capturing users' dynamic preferences, the continuous time
information is seamlessly incorporated into the computing of the attention
weights. More specifically, we design a hierarchical attention network, where
the lower layer learns the importance of different sentences and elements, and
the upper layer captures the correlations between the previously interacted and
the target news. To comprehensively model the dynamic characters, we firstly
enhance the traditional attention mechanism by incorporating both absolute and
relative time information, and then we propose a dynamic negative sampling
method to optimize the users' implicit feedback. We conduct extensive
experiments based on three real-world datasets to demonstrate our model's
effectiveness. Our source code and pre-trained representations are available at
https://github.com/lshowway/D-HAN.

negative:
Spoken language understanding system is traditionally designed as a pipeline
of a number of components. First, the audio signal is processed by an automatic
speech recognizer for transcription or n-best hypotheses. With the recognition
results, a natural language understanding system classifies the text to
structured data as domain, intent and slots for down-streaming consumers, such
as dialog system, hands-free applications. These components are usually
developed and optimized independently. In this paper, we present our study on
an end-to-end learning system for spoken language understanding. With this
unified approach, we can infer the semantic meaning directly from audio
features without the intermediate text representation. This study showed that
the trained model can achieve reasonable good result and demonstrated that the
model can capture the semantic attention directly from the audio features.

negative:
Attention-based sequence-to-sequence models for automatic speech recognition
jointly train an acoustic model, language model, and alignment mechanism. Thus,
the language model component is only trained on transcribed audio-text pairs.
This leads to the use of shallow fusion with an external language model at
inference time. Shallow fusion refers to log-linear interpolation with a
separately trained language model at each step of the beam search. In this
work, we investigate the behavior of shallow fusion across a range of
conditions: different types of language models, different decoding units, and
different tasks. On Google Voice Search, we demonstrate that the use of shallow
fusion with a neural LM with wordpieces yields a 9.1% relative word error rate
reduction (WERR) over our competitive attention-based sequence-to-sequence
model, obviating the need for second-pass rescoring.

negative:
The significance of social media has increased manifold in the past few
decades as it helps people from even the most remote corners of the world stay
connected. With the COVID-19 pandemic raging, social media has become more
relevant and widely used than ever before, and along with this, there has been
a resurgence in the circulation of fake news and tweets that demand immediate
attention. In this paper, we describe our Fake News Detection system that
automatically identifies whether a tweet related to COVID-19 is "real" or
"fake", as a part of CONSTRAINT COVID19 Fake News Detection in English
challenge. We have used an ensemble model consisting of pre-trained models that
has helped us achieve a joint 8th position on the leader board. We have
achieved an F1-score of 0.9831 against a top score of 0.9869. Post completion
of the competition, we have been able to drastically improve our system by
incorporating a novel heuristic algorithm based on username handles and link
domains in tweets fetching an F1-score of 0.9883 and achieving state-of-the art
results on the given dataset.

negative:
Fake news may be intentionally created to promote economic, political and
social interests, and can lead to negative impacts on humans beliefs and
decisions. Hence, detection of fake news is an emerging problem that has become
extremely prevalent during the last few years. Most existing works on this
topic focus on manual feature extraction and supervised classification models
leveraging a large number of labeled (fake or real) articles. In contrast, we
focus on content-based detection of fake news articles, while assuming that we
have a small amount of labels, made available by manual fact-checkers or
automated sources. We argue this is a more realistic setting in the presence of
massive amounts of content, most of which cannot be easily factchecked. To that
end, we represent collections of news articles as multi-dimensional tensors,
leverage tensor decomposition to derive concise article embeddings that capture
spatial/contextual information about each news article, and use those
embeddings to create an article-by-article graph on which we propagate limited
labels. Results on three real-world datasets show that our method performs on
par or better than existing models that are fully supervised, in that we
achieve better detection accuracy using fewer labels. In particular, our
proposed method achieves 75.43% of accuracy using only 30% of labels of a
public dataset while an SVM-based classifier achieved 67.43%. Furthermore, our
method achieves 70.92% of accuracy in a large dataset using only 2% of labels.

negative:
In fighting against fake news, many fact-checking systems comprised of
human-based fact-checking sites (e.g., snopes.com and politifact.com) and
automatic detection systems have been developed in recent years. However,
online users still keep sharing fake news even when it has been debunked. It
means that early fake news detection may be insufficient and we need another
complementary approach to mitigate the spread of misinformation. In this paper,
we introduce a novel application of text generation for combating fake news. In
particular, we (1) leverage online users named \emph{fact-checkers}, who cite
fact-checking sites as credible evidences to fact-check information in public
discourse; (2) analyze linguistic characteristics of fact-checking tweets; and
(3) propose and build a deep learning framework to generate responses with
fact-checking intention to increase the fact-checkers' engagement in
fact-checking activities. Our analysis reveals that the fact-checkers tend to
refute misinformation and use formal language (e.g. few swear words and
Internet slangs). Our framework successfully generates relevant responses, and
outperforms competing models by achieving up to 30\% improvements. Our
qualitative study also confirms that the superiority of our generated responses
compared with responses generated from the existing models.

negative:
As the spread of false information on the internet has increased dramatically
in recent years, more and more attention is being paid to automated fake news
detection. Some fake news detection methods are already quite successful.
Nevertheless, there are still many vulnerabilities in the detection algorithms.
The reason for this is that fake news publishers can structure and formulate
their texts in such a way that a detection algorithm does not expose this text
as fake news. This paper shows that it is possible to automatically attack
state-of-the-art models that have been trained to detect Fake News, making
these vulnerable. For this purpose, corresponding models were first trained
based on a dataset. Then, using Text-Attack, an attempt was made to manipulate
the trained models in such a way that previously correctly identified fake news
was classified as true news. The results show that it is possible to
automatically bypass Fake News detection mechanisms, leading to implications
concerning existing policy initiatives.

negative:
Information Retrieval (IR) models need to deal with two difficult issues,
vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to
the difficulty of retrieving relevant documents that do not contain exact query
terms but semantically related terms. Term dependencies refers to the need of
considering the relationship between the words of the query when estimating the
relevance of a document. A multitude of solutions has been proposed to solve
each of these two problems, but no principled model solve both. In parallel, in
the last few years, language models based on neural networks have been used to
cope with complex natural language processing tasks like emotion and paraphrase
detection. Although they present good abilities to cope with both term
dependencies and vocabulary mismatch problems, thanks to the distributed
representation of words they are based upon, such models could not be used
readily in IR, where the estimation of one language model per document (or
query) is required. This is both computationally unfeasible and prone to
over-fitting. Based on a recent work that proposed to learn a generic language
model that can be modified through a set of document-specific parameters, we
explore use of new neural network models that are adapted to ad-hoc IR tasks.
Within the language model IR framework, we propose and study the use of a
generic language model as well as a document-specific language model. Both can
be used as a smoothing component, but the latter is more adapted to the
document at hand and has the potential of being used as a full document
language model. We experiment with such models and analyze their results on
TREC-1 to 8 datasets.

negative:
The blurry line between nefarious fake news and protected-speech satire has
been a notorious struggle for social media platforms. Further to the efforts of
reducing exposure to misinformation on social media, purveyors of fake news
have begun to masquerade as satire sites to avoid being demoted. In this work,
we address the challenge of automatically classifying fake news versus satire.
Previous work have studied whether fake news and satire can be distinguished
based on language differences. Contrary to fake news, satire stories are
usually humorous and carry some political or social message. We hypothesize
that these nuances could be identified using semantic and linguistic cues.
Consequently, we train a machine learning method using semantic representation,
with a state-of-the-art contextual language model, and with linguistic features
based on textual coherence metrics. Empirical evaluation attests to the merits
of our approach compared to the language-based baseline and sheds light on the
nuances between fake news and satire. As avenues for future work, we consider
studying additional linguistic features related to the humor aspect, and
enriching the data with current news events, to help identify a political or
social message.

negative:
Fake news, misinformation, and unverifiable facts on social media platforms
propagate disharmony and affect society, especially when dealing with an
epidemic like COVID-19. The task of Fake News Detection aims to tackle the
effects of such misinformation by classifying news items as fake or real. In
this paper, we propose a novel approach that improves over the current
automatic fake news detection approaches by automatically gathering evidence
for each claim. Our approach extracts supporting evidence from the web articles
and then selects appropriate text to be treated as evidence sets. We use a
pre-trained summarizer on these evidence sets and then use the extracted
summary as supporting evidence to aid the classification task. Our experiments,
using both machine learning and deep learning-based methods, help perform an
extensive evaluation of our approach. The results show that our approach
outperforms the state-of-the-art methods in fake news detection to achieve an
F1-score of 99.25 over the dataset provided for the CONSTRAINT-2021 Shared
Task. We also release the augmented dataset, our code and models for any
further research.

negative:
Fact verification models have enjoyed a fast advancement in the last two
years with the development of pre-trained language models like BERT and the
release of large scale datasets such as FEVER. However, the challenging problem
of fake news detection has not benefited from the improvement of fact
verification models, which is closely related to fake news detection. In this
paper, we propose a simple yet effective approach to connect the dots between
fact verification and fake news detection. Our approach first employs a text
summarization model pre-trained on news corpora to summarize the long news
article into a short claim. Then we use a fact verification model pre-trained
on the FEVER dataset to detect whether the input news article is real or fake.
Our approach makes use of the recent success of fact verification models and
enables zero-shot fake news detection, alleviating the need of large-scale
training data to train fake news detection models. Experimental results on
FakenewsNet, a benchmark dataset for fake news detection, demonstrate the
effectiveness of our proposed approach.

negative:
Disinformation and fake news have posed detrimental effects on individuals
and society in recent years, attracting broad attention to fake news detection.
The majority of existing fake news detection algorithms focus on mining news
content and/or the surrounding exogenous context for discovering deceptive
signals; while the endogenous preference of a user when he/she decides to
spread a piece of fake news or not is ignored. The confirmation bias theory has
indicated that a user is more likely to spread a piece of fake news when it
confirms his/her existing beliefs/preferences. Users' historical, social
engagements such as posts provide rich information about users' preferences
toward news and have great potential to advance fake news detection. However,
the work on exploring user preference for fake news detection is somewhat
limited. Therefore, in this paper, we study the novel problem of exploiting
user preference for fake news detection. We propose a new framework, UPFD,
which simultaneously captures various signals from user preferences by joint
content and graph modeling. Experimental results on real-world datasets
demonstrate the effectiveness of the proposed framework. We release our code
and data as a benchmark for GNN-based fake news detection:
https://github.com/safe-graph/GNN-FakeNews.

negative:
With the digitization of media, an immense amount of news data has been
generated by online sources, including mainstream media outlets as well as
social networks. However, the ease of production and distribution resulted in
circulation of fake news as well as credible, authentic news. The pervasive
dissemination of fake news has extreme negative impacts on individuals and
society. Therefore, fake news detection has recently become an emerging topic
as an interdisciplinary research field that is attracting significant attention
from many research disciplines, including social sciences and linguistics. In
this study, we propose a method primarily based on lexicons including a scoring
system to facilitate the detection of the fake news in Turkish. We contribute
to the literature by collecting a novel, large scale, and credible dataset of
Turkish news, and by constructing the first fake news detection lexicon for
Turkish.

negative:
Representation learning is a critical ingredient for natural language
processing systems. Recent Transformer language models like BERT learn powerful
textual representations, but these models are targeted towards token- and
sentence-level training objectives and do not leverage information on
inter-document relatedness, which limits their document-level representation
power. For applications on scientific documents, such as classification and
recommendation, the embeddings power strong performance on end tasks. We
propose SPECTER, a new method to generate document-level embedding of
scientific documents based on pretraining a Transformer language model on a
powerful signal of document-level relatedness: the citation graph. Unlike
existing pretrained language models, SPECTER can be easily applied to
downstream applications without task-specific fine-tuning. Additionally, to
encourage further research on document-level models, we introduce SciDocs, a
new evaluation benchmark consisting of seven document-level tasks ranging from
citation prediction, to document classification and recommendation. We show
that SPECTER outperforms a variety of competitive baselines on the benchmark.

negative:
Automatically identifying fake news from the Internet is a challenging
problem in deception detection tasks. Online news is modified constantly during
its propagation, e.g., malicious users distort the original truth and make up
fake news. However, the continuous evolution process would generate
unprecedented fake news and cheat the original model. We present the Fake News
Evolution (FNE) dataset: a new dataset tracking the fake news evolution
process. Our dataset is composed of 950 paired data, each of which consists of
articles representing the three significant phases of the evolution process,
which are the truth, the fake news, and the evolved fake news. We observe the
features during the evolution and they are the disinformation techniques, text
similarity, top 10 keywords, classification accuracy, parts of speech, and
sentiment properties.

negative:
The recently proposed Sequence-to-Sequence (seq2seq) framework advocates
replacing complex data processing pipelines, such as an entire automatic speech
recognition system, with a single neural network trained in an end-to-end
fashion. In this contribution, we analyse an attention-based seq2seq speech
recognition system that directly transcribes recordings into characters. We
observe two shortcomings: overconfidence in its predictions and a tendency to
produce incomplete transcriptions when language models are used. We propose
practical solutions to both problems achieving competitive speaker independent
word error rates on the Wall Street Journal dataset: without separate language
models we reach 10.6% WER, while together with a trigram language model, we
reach 6.7% WER.

negative:
We explore neural language modeling for speech recognition where the context
spans multiple sentences. Rather than encode history beyond the current
sentence using a cache of words or document-level features, we focus our study
on the ability of LSTM and Transformer language models to implicitly learn to
carry over context across sentence boundaries. We introduce a new architecture
that incorporates an attention mechanism into LSTM to combine the benefits of
recurrent and attention architectures. We conduct language modeling and speech
recognition experiments on the publicly available LibriSpeech corpus. We show
that conventional training on a paragraph-level corpus results in significant
reductions in perplexity compared to training on a sentence-level corpus. We
also describe speech recognition experiments using long-span language models in
second-pass re-ranking, and provide insights into the ability of such models to
take advantage of context beyond the current sentence.

negative:
Previous researchers have considered sentiment analysis as a document
classification task, in which input documents are classified into predefined
sentiment classes. Although there are sentences in a document that support
important evidences for sentiment analysis and sentences that do not, they have
treated the document as a bag of sentences. In other words, they have not
considered the importance of each sentence in the document. To effectively
determine polarity of a document, each sentence in the document should be dealt
with different degrees of importance. To address this problem, we propose a
document-level sentence classification model based on deep neural networks, in
which the importance degrees of sentences in documents are automatically
determined through gate mechanisms. To verify our new sentiment analysis model,
we conducted experiments using the sentiment datasets in the four different
domains such as movie reviews, hotel reviews, restaurant reviews, and music
reviews. In the experiments, the proposed model outperformed previous
state-of-the-art models that do not consider importance differences of
sentences in a document. The experimental results show that the importance of
sentences should be considered in a document-level sentiment classification
task.

negative:
Document AI, or Document Intelligence, is a relatively new research topic
that refers to the techniques for automatically reading, understanding, and
analyzing business documents. It is an important research direction for natural
language processing and computer vision. In recent years, the popularity of
deep learning technology has greatly advanced the development of Document AI,
such as document layout analysis, visual information extraction, document
visual question answering, document image classification, etc. This paper
briefly reviews some of the representative models, tasks, and benchmark
datasets. Furthermore, we also introduce early-stage heuristic rule-based
document analysis, statistical machine learning algorithms, and deep learning
approaches especially pre-training methods. Finally, we look into future
directions for Document AI research.

negative:
Existing language models excel at writing from scratch, but many real-world
scenarios require rewriting an existing document to fit a set of constraints.
Although sentence-level rewriting has been fairly well-studied, little work has
addressed the challenge of rewriting an entire document coherently. In this
work, we introduce the task of document-level targeted content transfer and
address it in the recipe domain, with a recipe as the document and a dietary
restriction (such as vegan or dairy-free) as the targeted constraint. We
propose a novel model for this task based on the generative pre-trained
language model (GPT-2) and train on a large number of roughly-aligned recipe
pairs (https://github.com/microsoft/document-level-targeted-content-transfer).
Both automatic and human evaluations show that our model out-performs existing
methods by generating coherent and diverse rewrites that obey the constraint
while remaining close to the original document. Finally, we analyze our model's
rewrites to assess progress toward the goal of making language generation more
attuned to constraints that are substantive rather than stylistic.

negative:
Recently, the end-to-end approach has proven its efficacy in monaural
multi-speaker speech recognition. However, high word error rates (WERs) still
prevent these systems from being used in practical applications. On the other
hand, the spatial information in multi-channel signals has proven helpful in
far-field speech recognition tasks. In this work, we propose a novel neural
sequence-to-sequence (seq2seq) architecture, MIMO-Speech, which extends the
original seq2seq to deal with multi-channel input and multi-channel output so
that it can fully model multi-channel multi-speaker speech separation and
recognition. MIMO-Speech is a fully neural end-to-end framework, which is
optimized only via an ASR criterion. It is comprised of: 1) a monaural masking
network, 2) a multi-source neural beamformer, and 3) a multi-output speech
recognition model. With this processing, the input overlapped speech is
directly mapped to text sequences. We further adopted a curriculum learning
strategy, making the best use of the training set to improve the performance.
The experiments on the spatialized wsj1-2mix corpus show that our model can
achieve more than 60% WER reduction compared to the single-channel system with
high quality enhanced signals (SI-SDR = 23.1 dB) obtained by the above
separation function.

negative:
Social media has greatly enabled people to participate in online activities
at an unprecedented rate. However, this unrestricted access also exacerbates
the spread of misinformation and fake news online which might cause confusion
and chaos unless being detected early for its mitigation. Given the rapidly
evolving nature of news events and the limited amount of annotated data,
state-of-the-art systems on fake news detection face challenges due to the lack
of large numbers of annotated training instances that are hard to come by for
early detection. In this work, we exploit multiple weak signals from different
sources given by user and content engagements (referred to as weak social
supervision), and their complementary utilities to detect fake news. We jointly
leverage the limited amount of clean data along with weak signals from social
engagements to train deep neural networks in a meta-learning framework to
estimate the quality of different weak instances. Experiments on realworld
datasets demonstrate that the proposed framework outperforms state-of-the-art
baselines for early detection of fake news without using any user engagements
at prediction time.

negative:
In recent years, the proliferation of so-called "fake news" has caused much
disruptions in society and weakened the news ecosystem. Therefore, to mitigate
such problems, researchers have developed state-of-the-art models to
auto-detect fake news on social media using sophisticated data science and
machine learning techniques. In this work, then, we ask "what if adversaries
attempt to attack such detection models?" and investigate related issues by (i)
proposing a novel threat model against fake news detectors, in which
adversaries can post malicious comments toward news articles to mislead fake
news detectors, and (ii) developing MALCOM, an end-to-end adversarial comment
generation framework to achieve such an attack. Through a comprehensive
evaluation, we demonstrate that about 94% and 93.5% of the time on average
MALCOM can successfully mislead five of the latest neural detection models to
always output targeted real and fake news labels. Furthermore, MALCOM can also
fool black box fake news detectors to always output real news labels 90% of the
time on average. We also compare our attack model with four baselines across
two real-world datasets, not only on attack performance but also on generated
quality, coherency, transferability, and robustness.

negative:
The rapid increase in fake news, which causes significant damage to society,
triggers many fake news related studies, including the development of fake news
detection and fact verification techniques. The resources for these studies are
mainly available as public datasets taken from Web data. We surveyed 118
datasets related to fake news research on a large scale from three
perspectives: (1) fake news detection, (2) fact verification, and (3) other
tasks; for example, the analysis of fake news and satire detection. We also
describe in detail their utilization tasks and their characteristics. Finally,
we highlight the challenges in the fake news dataset construction and some
research opportunities that address these challenges. Our survey facilitates
fake news research by helping researchers find suitable datasets without
reinventing the wheel, and thereby, improves fake news studies in depth.

negative:
In this paper, we focus on the problem of unsupervised image-sentence
matching. Existing research explores to utilize document-level structural
information to sample positive and negative instances for model training.
Although the approach achieves positive results, it introduces a sampling bias
and fails to distinguish instances with high semantic similarity. To alleviate
the bias, we propose a new sampling strategy to select additional
intra-document image-sentence pairs as positive or negative samples.
Furthermore, to recognize the complex pattern in intra-document samples, we
propose a Transformer based model to capture fine-grained features and
implicitly construct a graph for each document, where concepts in a document
are introduced to bridge the representation learning of images and sentences in
the context of a document. Experimental results show the effectiveness of our
approach to alleviate the bias and learn well-aligned multimodal
representations.

negative:
Technical documents contain a fair amount of unnatural language, such as
tables, formulas, pseudo-codes, etc. Unnatural language can be an important
factor of confusing existing NLP tools. This paper presents an effective method
of distinguishing unnatural language from natural language, and evaluates the
impact of unnatural language detection on NLP tasks such as document
clustering. We view this problem as an information extraction task and build a
multiclass classification model identifying unnatural language components into
four categories. First, we create a new annotated corpus by collecting slides
and papers in various formats, PPT, PDF, and HTML, where unnatural language
components are annotated into four categories. We then explore features
available from plain text to build a statistical model that can handle any
format as long as it is converted into plain text. Our experiments show that
removing unnatural language components gives an absolute improvement in
document clustering up to 15%. Our corpus and tool are publicly available.

negative:
Document-level relation extraction aims to extract relations among multiple
entity pairs from a document. Previously proposed graph-based or
transformer-based models utilize the entities independently, regardless of
global information among relational triples. This paper approaches the problem
by predicting an entity-level relation matrix to capture local and global
information, parallel to the semantic segmentation task in computer vision.
Herein, we propose a Document U-shaped Network for document-level relation
extraction. Specifically, we leverage an encoder module to capture the context
information of entities and a U-shaped segmentation module over the image-style
feature map to capture global interdependency among triples. Experimental
results show that our approach can obtain state-of-the-art performance on three
benchmark datasets DocRED, CDR, and GDA.

negative:
Cross-lingual document representations enable language understanding in
multilingual contexts and allow transfer learning from high-resource to
low-resource languages at the document level. Recently large pre-trained
language models such as BERT, XLM and XLM-RoBERTa have achieved great success
when fine-tuned on sentence-level downstream tasks. It is tempting to apply
these cross-lingual models to document representation learning. However, there
are two challenges: (1) these models impose high costs on long document
processing and thus many of them have strict length limit; (2) model
fine-tuning requires extra data and computational resources, which is not
practical in resource-limited settings. In this work, we address these
challenges by proposing unsupervised Language-Agnostic Weighted Document
Representations (LAWDR). We study the geometry of pre-trained sentence
embeddings and leverage it to derive document representations without
fine-tuning. Evaluated on cross-lingual document alignment, LAWDR demonstrates
comparable performance to state-of-the-art models on benchmark datasets.

negative:
Text alignment finds application in tasks such as citation recommendation and
plagiarism detection. Existing alignment methods operate at a single,
predefined level and cannot learn to align texts at, for example, sentence and
document levels. We propose a new learning approach that equips previously
established hierarchical attention encoders for representing documents with a
cross-document attention component, enabling structural comparisons across
different levels (document-to-document and sentence-to-document). Our component
is weakly supervised from document pairs and can align at multiple levels. Our
evaluation on predicting document-to-document relationships and
sentence-to-document relationships on the tasks of citation recommendation and
plagiarism detection shows that our approach outperforms previously established
hierarchical, attention encoders based on recurrent and transformer
contextualization that are unaware of structural correspondence between
documents.

negative:
Document grounded generation is the task of using the information provided in
a document to improve text generation. This work focuses on two different
document grounded generation tasks: Wikipedia Update Generation task and
Dialogue response generation. Our work introduces two novel adaptations of
large scale pre-trained encoder-decoder models focusing on building context
driven representation of the document and enabling specific attention to the
information in the document. Additionally, we provide a stronger BART baseline
for these tasks. Our proposed techniques outperform existing methods on both
automated (at least 48% increase in BLEU-4 points) and human evaluation for
closeness to reference and relevance to the document. Furthermore, we perform
comprehensive manual inspection of the generated output and categorize errors
to provide insights into future directions in modeling these tasks.

negative:
Neural networks with deep architectures have demonstrated significant
performance improvements in computer vision, speech recognition, and natural
language processing. The challenges in information retrieval (IR), however, are
different from these other application areas. A common form of IR involves
ranking of documents--or short passages--in response to keyword-based queries.
Effective IR systems must deal with query-document vocabulary mismatch problem,
by modeling relationships between different query and document terms and how
they indicate relevance. Models should also consider lexical matches when the
query contains rare terms--such as a person's name or a product model
number--not seen during training, and to avoid retrieving semantically related
but irrelevant results. In many real-life IR tasks, the retrieval involves
extremely large collections--such as the document index of a commercial Web
search engine--containing billions of documents. Efficient IR methods should
take advantage of specialized IR data structures, such as inverted index, to
efficiently retrieve from large collections. Given an information need, the IR
system also mediates how much exposure an information artifact receives by
deciding whether it should be displayed, and where it should be positioned,
among other results. Exposure-aware IR systems may optimize for additional
objectives, besides relevance, such as parity of exposure for retrieved items
and content publishers. In this thesis, we present novel neural architectures
and methods motivated by the specific needs and challenges of IR tasks.

negative:
Today all kind of information is getting digitized and along with all this
digitization, the huge archive of various kinds of documents is being digitized
too. We know that, Optical Character Recognition is the method through which,
newspapers and other paper documents convert into digital resources. But, it is
a fact that this method works on texts only. As a result, if we try to process
any document which contains non-textual zones, then we will get garbage texts
as output. That is why; in order to digitize documents properly they should be
prepossessed carefully. And while preprocessing, segmenting document in
different regions according to the category properly is most important. But,
the Optical Character Recognition processes available for Bangla language have
no such algorithm that can categorize a newspaper/book page fully. So we worked
to decompose a document into its several parts like headlines, sub headlines,
columns, images etc. And if the input is skewed and rotated, then the input was
also deskewed and de-rotated. To decompose any Bangla document we found out the
edges of the input image. Then we find out the horizontal and vertical area of
every pixel where it lies in. Later on the input image was cut according to
these areas. Then we pick each and every sub image and found out their
height-width ratio, line height. Then according to these values the sub images
were categorized. To deskew the image we found out the skew angle and de skewed
the image according to this angle. To de-rotate the image we used the line
height, matra line, pixel ratio of matra line.

negative:
This paper describes the short-term competition on the Components
Segmentation Task of Document Photos that was prepared in the context of the
16th International Conference on Document Analysis and Recognition (ICDAR
2021). This competition aims to bring together researchers working in the field
of identification document image processing and provides them a suitable
benchmark to compare their techniques on the component segmentation task of
document images. Three challenge tasks were proposed entailing different
segmentation assignments to be performed on a provided dataset. The collected
data are from several types of Brazilian ID documents, whose personal
information was conveniently replaced. There were 16 participants whose results
obtained for some or all the three tasks show different rates for the adopted
metrics, like Dice Similarity Coefficient ranging from 0.06 to 0.99. Different
Deep Learning models were applied by the entrants with diverse strategies to
achieve the best results in each of the tasks. Obtained results show that the
currently applied methods for solving one of the proposed tasks (document
boundary detection) are already well established. However, for the other two
challenge tasks (text zone and handwritten sign detection) research and
development of more robust approaches are still required to achieve acceptable
results.

negative:
Abstractive text summarization aims to shorten long text documents into a
human readable form that contains the most important facts from the original
document. However, the level of actual abstraction as measured by novel phrases
that do not appear in the source document remains low in existing approaches.
We propose two techniques to improve the level of abstraction of generated
summaries. First, we decompose the decoder into a contextual network that
retrieves relevant parts of the source document, and a pretrained language
model that incorporates prior knowledge about language generation. Second, we
propose a novelty metric that is optimized directly through policy learning to
encourage the generation of novel phrases. Our model achieves results
comparable to state-of-the-art models, as determined by ROUGE scores and human
evaluations, while achieving a significantly higher level of abstraction as
measured by n-gram overlap with the source document.

negative:
Long document summarization is an important and hard task in the field of
natural language processing. A good performance of the long document
summarization reveals the model has a decent understanding of the human
language. Currently, most researches focus on how to modify the attention
mechanism of the transformer to achieve a higher ROUGE score. The study of data
pre-processing and post-processing are relatively few. In this paper, we use
two pre-processing methods and a post-processing method and analyze the effect
of these methods on various long document summarization models.

negative:
Most existing models for multilingual natural language processing (NLP) treat
language as a discrete category, and make predictions for either one language
or the other. In contrast, we propose using continuous vector representations
of language. We show that these can be learned efficiently with a
character-based neural language model, and used to improve inference about
language varieties not seen during training. In experiments with 1303 Bible
translations into 990 different languages, we empirically explore the capacity
of multilingual language models, and also show that the language vectors
capture genetic relationships between languages.

negative:
We present a contrasting learning approach with data augmentation techniques
to learn document representations in an unsupervised manner. Inspired by recent
contrastive self-supervised learning algorithms used for image and NLP
pretraining, we hypothesize that high-quality document embedding should be
invariant to diverse paraphrases that preserve the semantics of the original
document. With different backbones and contrastive learning frameworks, our
study reveals the enormous benefits of contrastive augmentation for document
representation learning with two additional insights: 1) including data
augmentation in a contrastive way can substantially improve the embedding
quality in unsupervised document representation learning, and 2) in general,
stochastic augmentations generated by simple word-level manipulation work much
better than sentence-level and document-level ones. We plug our method into a
classifier and compare it with a broad range of baseline methods on six
benchmark datasets. Our method can decrease the classification error rate by up
to 6.4% over the SOTA approaches on the document classification task, matching
or even surpassing fully-supervised methods.

negative:
Sentence embedding is an important research topic in natural language
processing. It is essential to generate a good embedding vector that fully
reflects the semantic meaning of a sentence in order to achieve an enhanced
performance for various natural language processing tasks, such as machine
translation and document classification. Thus far, various sentence embedding
models have been proposed, and their feasibility has been demonstrated through
good performances on tasks following embedding, such as sentiment analysis and
sentence classification. However, because the performances of sentence
classification and sentiment analysis can be enhanced by using a simple
sentence representation method, it is not sufficient to claim that these models
fully reflect the meanings of sentences based on good performances for such
tasks. In this paper, inspired by human language recognition, we propose the
following concept of semantic coherence, which should be satisfied for a good
sentence embedding method: similar sentences should be located close to each
other in the embedding space. Then, we propose the Paraphrase-Thought
(P-thought) model to pursue semantic coherence as much as possible.
Experimental results on two paraphrase identification datasets (MS COCO and STS
benchmark) show that the P-thought models outperform the benchmarked sentence
embedding methods.

negative:
Document date is essential for many important tasks, such as document
retrieval, summarization, event detection, etc. While existing approaches for
these tasks assume accurate knowledge of the document date, this is not always
available, especially for arbitrary documents from the Web. Document Dating is
a challenging problem which requires inference over the temporal structure of
the document. Prior document dating systems have largely relied on handcrafted
features while ignoring such document internal structures. In this paper, we
propose NeuralDater, a Graph Convolutional Network (GCN) based document dating
approach which jointly exploits syntactic and temporal graph structures of
document in a principled way. To the best of our knowledge, this is the first
application of deep learning for the problem of document dating. Through
extensive experiments on real-world datasets, we find that NeuralDater
significantly outperforms state-of-the-art baseline by 19% absolute (45%
relative) accuracy points.

negative:
The multi-stream paradigm of audio processing, in which several sources are
simultaneously considered, has been an active research area for information
fusion. Our previous study offered a promising direction within end-to-end
automatic speech recognition, where parallel encoders aim to capture diverse
information followed by a stream-level fusion based on attention mechanisms to
combine the different views. However, with an increasing number of streams
resulting in an increasing number of encoders, the previous approach could
require substantial memory and massive amounts of parallel data for joint
training. In this work, we propose a practical two-stage training scheme.
Stage-1 is to train a Universal Feature Extractor (UFE), where encoder outputs
are produced from a single-stream model trained with all data. Stage-2
formulates a multi-stream scheme intending to solely train the attention fusion
module using the UFE features and pretrained components from Stage-1.
Experiments have been conducted on two datasets, DIRHA and AMI, as a
multi-stream scenario. Compared with our previous method, this strategy
achieves relative word error rate reductions of 8.2--32.4%, while consistently
outperforming several conventional combination methods.

negative:
The selection of a suitable document representation approach plays a crucial
role in the performance of a document clustering task. Being able to pick out
representative words within a document can lead to substantial improvements in
document clustering. In the case of web documents, the HTML markup that defines
the layout of the content provides additional structural information that can
be further exploited to identify representative words. In this paper we
introduce a fuzzy term weighing approach that makes the most of the HTML
structure for document clustering. We set forth and build on the hypothesis
that a good representation can take advantage of how humans skim through
documents to extract the most representative words. The authors of web pages
make use of HTML tags to convey the most important message of a web page
through page elements that attract the readers' attention, such as page titles
or emphasized elements. We define a set of criteria to exploit the information
provided by these page elements, and introduce a fuzzy combination of these
criteria that we evaluate within the context of a web page clustering task. Our
proposed approach, called Abstract Fuzzy Combination of Criteria (AFCC), can
adapt to datasets whose features are distributed differently, achieving good
results compared to other similar fuzzy logic based approaches and TF-IDF
across different datasets.

negative:
We present a novel model for the problem of ranking a collection of documents
according to their semantic similarity to a source (query) document. While the
problem of document-to-document similarity ranking has been studied, most
modern methods are limited to relatively short documents or rely on the
existence of "ground-truth" similarity labels. Yet, in most common real-world
cases, similarity ranking is an unsupervised problem as similarity labels are
unavailable. Moreover, an ideal model should not be restricted by documents'
length. Hence, we introduce SDR, a self-supervised method for document
similarity that can be applied to documents of arbitrary length. Importantly,
SDR can be effectively applied to extremely long documents, exceeding the 4,096
maximal token limits of Longformer. Extensive evaluations on large document
datasets show that SDR significantly outperforms its alternatives across all
metrics. To accelerate future research on unlabeled long document similarity
ranking, and as an additional contribution to the community, we herein publish
two human-annotated test sets of long documents similarity evaluation. The SDR
code and datasets are publicly available.

negative:
Conceptual entanglement is a crucial phenomenon in quantum cognition because
it implies that classical probabilities cannot model non--compositional
conceptual phenomena. While several psychological experiments have been
developed to test conceptual entanglement, this has not been explored in the
context of Natural Language Processing. In this paper, we apply the hypothesis
that words of a document are traces of the concepts that a person has in mind
when writing the document. Therefore, if these concepts are entangled, we
should be able to observe traces of their entanglement in the documents. In
particular, we test conceptual entanglement by contrasting language simulations
with results obtained from a text corpus. Our analysis indicates that
conceptual entanglement is strongly linked to the way in which language is
structured. We discuss the implications of this finding in the context of
conceptual modeling and of Natural Language Processing.

negative:
Segmenting an unordered text document into different sections is a very
useful task in many text processing applications like multiple document
summarization, question answering, etc. This paper proposes structuring of an
unordered text document based on the keywords in the document. We test our
approach on Wikipedia documents using both statistical and predictive methods
such as the TextRank algorithm and Google's USE (Universal Sentence Encoder).
From our experimental results, we show that the proposed model can effectively
structure an unordered document into sections.

negative:
We show that Bayes' rule provides an effective mechanism for creating
document translation models that can be learned from only parallel sentences
and monolingual documents---a compelling benefit as parallel documents are not
always available. In our formulation, the posterior probability of a candidate
translation is the product of the unconditional (prior) probability of the
candidate output document and the "reverse translation probability" of
translating the candidate output back into the source language. Our proposed
model uses a powerful autoregressive language model as the prior on target
language documents, but it assumes that each sentence is translated
independently from the target to the source language. Crucially, at test time,
when a source document is observed, the document language model prior induces
dependencies between the translations of the source sentences in the posterior.
The model's independence assumption not only enables efficient use of available
data, but it additionally admits a practical left-to-right beam-search
algorithm for carrying out inference. Experiments show that our model benefits
from using cross-sentence context in the language model, and it outperforms
existing document translation approaches.

negative:
We provide a series of algorithms demonstrating that solutions according to
the fundamental game-theoretic solution concept of closed under rational
behavior (CURB) sets in two-player, normal-form games can be computed in
polynomial time (we also discuss extensions to n-player games). First, we
describe an algorithm that identifies all of a player's best responses
conditioned on the belief that the other player will play from within a given
subset of its strategy space. This algorithm serves as a subroutine in a series
of polynomial-time algorithms for finding all minimal CURB sets, one minimal
CURB set, and the smallest minimal CURB set in a game. We then show that the
complexity of finding a Nash equilibrium can be exponential only in the size of
a game's smallest CURB set. Related to this, we show that the smallest CURB set
can be an arbitrarily small portion of the game, but it can also be arbitrarily
larger than the supports of its only enclosed Nash equilibrium. We test our
algorithms empirically and find that most commonly studied academic games tend
to have either very large or very small minimal CURB sets.

negative:
This paper proposes an algorithm to improve the calculation of confidence
measure for spoken term detection (STD). Given an input query term, the
algorithm first calculates a measurement named document ranking weight for each
document in the speech database to reflect its relevance with the query term by
summing all the confidence measures of the hypothesized term occurrences in
this document. The confidence measure of each term occurrence is then
re-estimated through linear interpolation with the calculated document ranking
weight to improve its reliability by integrating document-level information.
Experiments are conducted on three standard STD tasks for Tamil, Vietnamese and
English respectively. The experimental results all demonstrate that the
proposed algorithm achieves consistent improvements over the state-of-the-art
method for confidence measure calculation. Furthermore, this algorithm is still
effective even if a high accuracy speech recognizer is not available, which
makes it applicable for the languages with limited speech resources.