query: I am currently developing a chatbot capable of engaging in open-ended conversations with users. However, I am dissatisfied with the conventional temperature-based stochastic sampling decoding procedure, as it results in derivative output that lacks creativity and diversity. I am seeking literature that explores the issue of diversity deficiency in language model decoding procedures. Additionally, I aim to create a decoding scheme that strikes a balance between output diversity and quality. I am also interested in research that examines the phenomenon of high-likelihood sentences exhibiting unexpectedly low quality. I wish to understand this occurrence and prevent it from affecting my decoding scheme. Ultimately, my goal is to develop a model that surpasses existing decoding algorithms in terms of balancing the quality and diversity of generated responses.

positive:
A conversational agent (chatbot) is a piece of software that is able to
communicate with humans using natural language. Modeling conversation is an
important task in natural language processing and artificial intelligence.
While chatbots can be used for various tasks, in general they have to
understand users' utterances and provide responses that are relevant to the
problem at hand.
  In my work, I conduct an in-depth survey of recent literature, examining over
70 publications related to chatbots published in the last 3 years. Then, I
proceed to make the argument that the very nature of the general conversation
domain demands approaches that are different from current state-of-of-the-art
architectures. Based on several examples from the literature I show why current
chatbot models fail to take into account enough priors when generating
responses and how this affects the quality of the conversation. In the case of
chatbots, these priors can be outside sources of information that the
conversation is conditioned on like the persona or mood of the conversers. In
addition to presenting the reasons behind this problem, I propose several ideas
on how it could be remedied.
  The next section focuses on adapting the very recent Transformer model to the
chatbot domain, which is currently state-of-the-art in neural machine
translation. I first present experiments with the vanilla model, using
conversations extracted from the Cornell Movie-Dialog Corpus. Secondly, I
augment the model with some of my ideas regarding the issues of encoder-decoder
architectures. More specifically, I feed additional features into the model
like mood or persona together with the raw conversation data. Finally, I
conduct a detailed analysis of how the vanilla model performs on conversational
data by comparing it to previous chatbot models and how the additional features
affect the quality of the generated responses.

positive:
Neural text generation models are likely to suffer from the low-diversity
problem. Various decoding strategies and training-based methods have been
proposed to promote diversity only by exploiting contextual features, but
rarely do they consider incorporating syntactic structure clues. In this work,
we propose using linguistic annotation, i.e., part-of-speech (POS), to guide
the text generation. In detail, we introduce POS Guided Softmax to explicitly
model two posterior probabilities: (i) next-POS, and (ii) next-token from the
vocabulary of the target POS. A POS Guided Sampling strategy is further
proposed to address the low-diversity problem by enriching the diversity of
POS. Extensive experiments and human evaluations show that, compared with
existing state-of-the-art methods, our POS Guided Softmax and Sampling (POSG)
can generate more diverse text while maintaining comparable quality.

positive:
Large pre-trained language models have repeatedly shown their ability to
produce fluent text. Yet even when starting from a prompt, generation can
continue in many plausible directions. Current decoding methods with the goal
of controlling generation, e.g., to ensure specific words are included, either
require additional models or fine-tuning, or work poorly when the task at hand
is semantically unconstrained, e.g., story generation. In this work, we present
a plug-and-play decoding method for controlled language generation that is so
simple and intuitive, it can be described in a single sentence: given a topic
or keyword, we add a shift to the probability distribution over our vocabulary
towards semantically similar words. We show how annealing this distribution can
be used to impose hard constraints on language generation, something no other
plug-and-play method is currently able to do with SOTA language generators.
Despite the simplicity of this approach, we see it works incredibly well in
practice: decoding from GPT-2 leads to diverse and fluent sentences while
guaranteeing the appearance of given guide words. We perform two user studies,
revealing that (1) our method outperforms competing methods in human
evaluations; and (2) forcing the guide words to appear in the generated text
has no impact on the fluency of the generated text.

positive:
Many existing conversation models that are based on the encoder-decoder
framework have focused on ways to make the encoder more complicated to enrich
the context vectors so as to increase the diversity and informativeness of
generated responses. However, these approaches face two problems. First, the
decoder is too simple to effectively utilize the previously generated
information and tends to generate duplicated and self-contradicting responses.
Second, the complex encoder tends to generate diverse but incoherent responses
because the complex context vectors may deviate from the original semantics of
context. In this work, we proposed a conversation model named "THINK" (Teamwork
generation Hover around Impressive Noticeable Keywords) to make the decoder
more complicated and avoid generating duplicated and self-contradicting
responses. The model simplifies the context vectors and increases the coherence
of generated responses in a reasonable way. For this model, we propose Teamwork
generation framework and Semantics Extractor. Compared with other baselines,
both automatic and human evaluation showed the advantages of our model.

positive:
Deep-learning models for language generation tasks tend to produce repetitive
output. Various methods have been proposed to encourage lexical diversity
during decoding, but this often comes at a cost to the perceived fluency and
adequacy of the output. In this work, we propose to ameliorate this cost by
using an Imitation Learning approach to explore the level of diversity that a
language generation model can reliably produce. Specifically, we augment the
decoding process with a meta-classifier trained to distinguish which words at
any given timestep will lead to high-quality output. We focus our experiments
on concept-to-text generation where models are sensitive to the inclusion of
irrelevant words due to the strict relation between input and output. Our
analysis shows that previous methods for diversity underperform in this
setting, while human evaluation suggests that our proposed method achieves a
high level of diversity with minimal effect to the output's fluency and
adequacy.

positive:
We study response generation for open domain conversation in chatbots.
Existing methods assume that words in responses are generated from an identical
vocabulary regardless of their inputs, which not only makes them vulnerable to
generic patterns and irrelevant noise, but also causes a high cost in decoding.
We propose a dynamic vocabulary sequence-to-sequence (DVS2S) model which allows
each input to possess their own vocabulary in decoding. In training, vocabulary
construction and response generation are jointly learned by maximizing a lower
bound of the true objective with a Monte Carlo sampling method. In inference,
the model dynamically allocates a small vocabulary for an input with the word
prediction model, and conducts decoding only with the small vocabulary. Because
of the dynamic vocabulary mechanism, DVS2S eludes many generic patterns and
irrelevant words in generation, and enjoys efficient decoding at the same time.
Experimental results on both automatic metrics and human annotations show that
DVS2S can significantly outperform state-of-the-art methods in terms of
response quality, but only requires 60% decoding time compared to the most
efficient baseline.

positive:
Despite considerable advancements with deep neural language models, the
enigma of neural text degeneration persists when these models are tested as
text generators. The counter-intuitive empirical observation is that even
though the use of likelihood as training objective leads to high quality models
for a broad range of language understanding tasks, using likelihood as a
decoding objective leads to text that is bland and strangely repetitive.
  In this paper, we reveal surprising distributional differences between human
text and machine text. In addition, we find that decoding strategies alone can
dramatically effect the quality of machine text, even when generated from
exactly the same neural language model. Our findings motivate Nucleus Sampling,
a simple but effective method to draw the best out of neural generation. By
sampling text from the dynamic nucleus of the probability distribution, which
allows for diversity while effectively truncating the less reliable tail of the
distribution, the resulting text better demonstrates the quality of human text,
yielding enhanced diversity without sacrificing fluency and coherence.

positive:
In open-domain dialogue systems, generative approaches have attracted much
attention for response generation. However, existing methods are heavily
plagued by generating safe responses and unnatural responses. To alleviate
these two problems, we propose a novel framework named Dual Adversarial
Learning (DAL) for high-quality response generation. DAL is the first work to
innovatively utilizes the duality between query generation and response
generation to avoid safe responses and increase the diversity of the generated
responses. Additionally, DAL uses adversarial learning to mimic human judges
and guides the system to generate natural responses. Experimental results
demonstrate that DAL effectively improves both diversity and overall quality of
the generated responses. DAL outperforms the state-of-the-art methods regarding
automatic metrics and human evaluations.

positive:
While conditional language models have greatly improved in their ability to
output high-quality natural language, many NLP applications benefit from being
able to generate a diverse set of candidate sequences. Diverse decoding
strategies aim to, within a given-sized candidate list, cover as much of the
space of high-quality outputs as possible, leading to improvements for tasks
that re-rank and combine candidate outputs. Standard decoding methods, such as
beam search, optimize for generating high likelihood sequences rather than
diverse ones, though recent work has focused on increasing diversity in these
methods. In this work, we perform an extensive survey of decoding-time
strategies for generating diverse outputs from conditional language models. We
also show how diversity can be improved without sacrificing quality by
over-sampling additional candidates, then filtering to the desired number.

positive:
Recent advances in neural sequence-to-sequence models have led to promising
results for several language generation-based tasks, including dialogue
response generation, summarization, and machine translation. However, these
models are known to have several problems, especially in the context of
chit-chat based dialogue systems: they tend to generate short and dull
responses that are often too generic. Furthermore, these models do not ground
conversational responses on knowledge and facts, resulting in turns that are
not accurate, informative and engaging for the users. In this paper, we propose
and experiment with a series of response generation models that aim to serve in
the general scenario where in addition to the dialogue context, relevant
unstructured external knowledge in the form of text is also assumed to be
available for models to harness. Our proposed approach extends
pointer-generator networks (See et al., 2017) by allowing the decoder to
hierarchically attend and copy from external knowledge in addition to the
dialogue context. We empirically show the effectiveness of the proposed model
compared to several baselines including (Ghazvininejad et al., 2018; Zhang et
al., 2018) through both automatic evaluation metrics and human evaluation on
CONVAI2 dataset.

positive:
Diversity is a long-studied topic in information retrieval that usually
refers to the requirement that retrieved results should be non-repetitive and
cover different aspects. In a conversational setting, an additional dimension
of diversity matters: an engaging response generation system should be able to
output responses that are diverse and interesting. Sequence-to-sequence
(Seq2Seq) models have been shown to be very effective for response generation.
However, dialogue responses generated by Seq2Seq models tend to have low
diversity. In this paper, we review known sources and existing approaches to
this low-diversity problem. We also identify a source of low diversity that has
been little studied so far, namely model over-confidence. We sketch several
directions for tackling model over-confidence and, hence, the low-diversity
problem, including confidence penalties and label smoothing.

positive:
How can we measure whether a natural language generation system produces both
high quality and diverse outputs? Human evaluation captures quality but not
diversity, as it does not catch models that simply plagiarize from the training
set. On the other hand, statistical evaluation (i.e., perplexity) captures
diversity but not quality, as models that occasionally emit low quality samples
would be insufficiently penalized. In this paper, we propose a unified
framework which evaluates both diversity and quality, based on the optimal
error rate of predicting whether a sentence is human- or machine-generated. We
demonstrate that this error rate can be efficiently estimated by combining
human and statistical evaluation, using an evaluation metric which we call
HUSE. On summarization and chit-chat dialogue, we show that (i) HUSE detects
diversity defects which fool pure human evaluation and that (ii) techniques
such as annealing for improving quality actually decrease HUSE due to decreased
diversity.

positive:
Many sequence-to-sequence dialogue models tend to generate safe,
uninformative responses. There have been various useful efforts on trying to
eliminate them. However, these approaches either improve decoding algorithms
during inference, rely on hand-crafted features, or employ complex models. In
our work, we build dialogue models that are dynamically aware of what
utterances or tokens are dull without any feature-engineering. Specifically, we
start with a simple yet effective automatic metric, AvgOut, which calculates
the average output probability distribution of all time steps on the decoder
side during training. This metric directly estimates which tokens are more
likely to be generated, thus making it a faithful evaluation of the model
diversity (i.e., for diverse models, the token probabilities should be more
evenly distributed rather than peaked at a few dull tokens). We then leverage
this novel metric to propose three models that promote diversity without losing
relevance. The first model, MinAvgOut, directly maximizes the diversity score
through the output distributions of each batch; the second model, Label
Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled
by the diversity score to control the diversity level; the third model, RL,
adopts Reinforcement Learning and treats the diversity score as a reward
signal. Moreover, we experiment with a hybrid model by combining the loss terms
of MinAvgOut and RL. All four models outperform their base LSTM-RNN model on
both diversity and relevance by a large margin, and are comparable to or better
than competitive baselines (also verified via human evaluation). Moreover, our
approaches are orthogonal to the base model, making them applicable as an
add-on to other emerging better dialogue models in the future.

positive:
The wave of pre-training language models has been continuously improving the
quality of the machine-generated conversations, however, some of the generated
responses still suffer from excessive repetition, sometimes repeating words
from utterance, sometimes repeating words within self-generated responses, or
both. Inappropriate repetition of words can significantly degrade the quality
of the generated texts. Penalized sampling is one popular solution, reducing
the sampling probability of existing words during inference, however, it is
highly vulnerable to the inappropriate setting of the static weight. Setting it
too high can yield strange and unrealistic sentences while setting it too low
makes the task of suppressing repetition trivial. To remedy the shortcomings of
the above methods, we design a context-aware classifier to explicitly decide
when to allow repetition and when to employ penalized sampling. Such a
classifier can be easily integrated with existing decoding methods, reducing
repetitions where appropriate while preserving the diversity of the text.
Experimental results demonstrate that our method can generate higher quality
and more authentic dialogues.

positive:
Recent progress in pre-trained language models led to systems that are able
to generate text of an increasingly high quality. While several works have
investigated the fluency and grammatical correctness of such models, it is
still unclear to which extent the generated text is consistent with factual
world knowledge. Here, we go beyond fluency and also investigate the
verifiability of text generated by state-of-the-art pre-trained language
models. A generated sentence is verifiable if it can be corroborated or
disproved by Wikipedia, and we find that the verifiability of generated text
strongly depends on the decoding strategy. In particular, we discover a
tradeoff between factuality (i.e., the ability of generating Wikipedia
corroborated text) and repetitiveness. While decoding strategies such as top-k
and nucleus sampling lead to less repetitive generations, they also produce
less verifiable text. Based on these finding, we introduce a simple and
effective decoding strategy which, in comparison to previously used decoding
strategies, produces less repetitive and more verifiable text.

positive:
A good open-domain chatbot should avoid presenting contradictory responses
about facts or opinions in a conversational session, known as its consistency
capacity. However, evaluating the consistency capacity of a chatbot is still
challenging. Employing human judges to interact with chatbots on purpose to
check their capacities is costly and low-efficient, and difficult to get rid of
subjective bias. In this paper, we propose the Addressing Inquiries about
History (AIH), an efficient and practical framework for the consistency
evaluation. At the conversation stage, AIH attempts to address appropriate
inquiries about the dialogue history to induce the chatbot to redeclare the
historical facts or opinions. We carry out the conversation between chatbots,
which is more efficient than the human-bot interaction and can also alleviate
the subjective bias. In this way, we manage to rapidly obtain a dialog session
that contains responses with high contradiction possibilities. At the
contradiction recognition stage, we can either employ human judges or a natural
language inference (NLI) model to recognize whether the answers to the
inquiries are contradictory with history. Finally, we are able to rank chatbots
according to the contradiction statistics. Experiments on open-domain chatbots
show that our approach can efficiently and reliably assess the consistency
capacity of chatbots and achieve a high ranking correlation with the human
evaluation. We release the framework and hope to help improve the consistency
capacity of chatbots. \footnote{\url{https://github.com/ictnlp/AIH}}

positive:
In this paper, we propose a simple, fast decoding algorithm that fosters
diversity in neural generation. The algorithm modifies the standard beam search
algorithm by adding an inter-sibling ranking penalty, favoring choosing
hypotheses from diverse parents. We evaluate the proposed model on the tasks of
dialogue response generation, abstractive summarization and machine
translation. We find that diverse decoding helps across all tasks, especially
those for which reranking is needed.
  We further propose a variation that is capable of automatically adjusting its
diversity decoding rates for different inputs using reinforcement learning
(RL). We observe a further performance boost from this RL technique. This paper
includes material from the unpublished script "Mutual Information and Diverse
Decoding Improve Neural Machine Translation" (Li and Jurafsky, 2016).

positive:
The neural text generation suffers from the text degeneration issue such as
repetition. Traditional stochastic sampling methods only focus on truncating
the unreliable "tail" of the distribution, and do not address the "head" part,
which we show might contain tedious or even repetitive candidates with high
probability that lead to repetition loops. They also do not consider the issue
that human text does not always favor high-probability words. Inspired by
these, in this work we propose a heuristic sampling method. We propose to use
interquartile range of the predicted distribution to determine the "head" part,
then permutate and rescale the "head" with inverse probability. This aims at
decreasing the probability for the tedious and possibly repetitive candidates
with higher probability, and increasing the probability for the rational but
more surprising candidates with lower probability. The proposed algorithm
provides a reasonable permutation on the predicted distribution which enhances
diversity without compromising rationality of the distribution. We use
pre-trained language model to compare our algorithm with traditional methods.
Results show that our algorithm can effectively increase the diversity of
generated samples while achieving close resemblance to human text.

positive:
Many neural network models nowadays have achieved promising performances in
Chit-chat settings. The majority of them rely on an encoder for understanding
the post and a decoder for generating the response. Without given assigned
semantics, the models lack the fine-grained control over responses as the
semantic mapping between posts and responses is hidden on the fly within the
end-to-end manners. Some previous works utilize sampled latent words as a
controllable semantic form to drive the generated response around the work, but
few works attempt to use more complex semantic patterns to guide the
generation. In this paper, we propose to use more detailed semantic forms,
including latent responses and part-of-speech sequences sampled from the
corresponding distributions, as the controllable semantics to guide the
generation. Our results show that the richer semantics are not only able to
provide informative and diverse responses, but also increase the overall
performance of response quality, including fluency and coherence.

positive:
Neural conversation models such as encoder-decoder models are easy to
generate bland and generic responses. Some researchers propose to use the
conditional variational autoencoder(CVAE) which maximizes the lower bound on
the conditional log-likelihood on a continuous latent variable. With different
sampled la-tent variables, the model is expected to generate diverse responses.
Although the CVAE-based models have shown tremendous potential, their
improvement of generating high-quality responses is still unsatisfactory. In
this paper, we introduce a discrete latent variable with an explicit semantic
meaning to improve the CVAE on short-text conversation. A major advantage of
our model is that we can exploit the semantic distance between the latent
variables to maintain good diversity between the sampled latent variables.
Accordingly, we pro-pose a two-stage sampling approach to enable efficient
diverse variable selection from a large latent space assumed in the short-text
conversation task. Experimental results indicate that our model outperforms
various kinds of generation models under both automatic and human evaluations
and generates more diverse and in-formative responses.

negative:
Despite the effectiveness of sequence-to-sequence framework on the task of
Short-Text Conversation (STC), the issue of under-exploitation of training data
(i.e., the supervision signals from query text is \textit{ignored}) still
remains unresolved. Also, the adopted \textit{maximization}-based decoding
strategies, inclined to generating the generic responses or responses with
repetition, are unsuited to the STC task. In this paper, we propose to
formulate the STC task as a language modeling problem and tailor-make a
training strategy to adapt a language model for response generation. To enhance
generation performance, we design a relevance-promoting transformer language
model, which performs additional supervised source attention after the
self-attention to increase the importance of informative query tokens in
calculating the token-level representation. The model further refines the query
representation with relevance clues inferred from its multiple references
during training. In testing, we adopt a
\textit{randomization-over-maximization} strategy to reduce the generation of
generic responses. Experimental results on a large Chinese STC dataset
demonstrate the superiority of the proposed model on relevance metrics and
diversity metrics.\footnote{Code available at
https://ai.tencent.com/ailab/nlp/dialogue/.

negative:
Neural text decoding is important for generating high-quality texts using
language models. To generate high-quality text, popular decoding algorithms
like top-k, top-p (nucleus), and temperature-based sampling truncate or distort
the unreliable low probability tail of the language model. Though these methods
generate high-quality text after parameter tuning, they are ad hoc. Not much is
known about the control they provide over the statistics of the output, which
is important since recent reports show text quality is highest for a specific
range of likelihoods. Here, first we provide a theoretical analysis of
perplexity in top-k, top-p, and temperature sampling, finding that
cross-entropy behaves approximately linearly as a function of p in top-p
sampling whereas it is a nonlinear function of k in top-k sampling, under
Zipfian statistics. We use this analysis to design a feedback-based adaptive
top-k text decoding algorithm called mirostat that generates text (of any
length) with a predetermined value of perplexity, and thereby high-quality text
without any tuning. Experiments show that for low values of k and p in top-k
and top-p sampling, perplexity drops significantly with generated text length,
which is also correlated with excessive repetitions in the text (the boredom
trap). On the other hand, for large values of k and p, we find that perplexity
increases with generated text length, which is correlated with incoherence in
the text (confusion trap). Mirostat avoids both traps: experiments show that
cross-entropy has a near-linear relation with repetition in generated text.
This relation is almost independent of the sampling method but slightly
dependent on the model used. Hence, for a given language model, control over
perplexity also gives control over repetitions. Experiments with human raters
for fluency, coherence, and quality further verify our findings.

negative:
Having engaging and informative conversations with users is the utmost goal
for open-domain conversational systems. Recent advances in transformer-based
language models and their applications to dialogue systems have succeeded to
generate fluent and human-like responses. However, they still lack control over
the generation process towards producing contentful responses and achieving
engaging conversations. To achieve this goal, we present \textbf{DiSCoL}
(\textbf{Di}alogue \textbf{S}ystems through \textbf{Co}versational
\textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue
system that leverages conversational lines (briefly \textbf{convlines}) as
controllable and informative content-planning elements to guide the generation
model produce engaging and informative responses. Two primary modules in
DiSCoL's pipeline are conditional generators trained for 1) predicting relevant
and informative convlines for dialogue contexts and 2) generating high-quality
responses conditioned on the predicted convlines. Users can also change the
returned convlines to \textit{control} the direction of the conversations
towards topics that are more interesting for them. Through automatic and human
evaluations, we demonstrate the efficiency of the convlines in producing
engaging conversations.

negative:
Endowing a chatbot with personality or an identity is quite challenging but
critical to deliver more realistic and natural conversations. In this paper, we
address the issue of generating responses that are coherent to a pre-specified
agent profile. We design a model consisting of three modules: a profile
detector to decide whether a post should be responded using the profile and
which key should be addressed, a bidirectional decoder to generate responses
forward and backward starting from a selected profile value, and a position
detector that predicts a word position from which decoding should start given a
selected profile value. We show that general conversation data from social
media can be used to generate profile-coherent responses. Manual and automatic
evaluation shows that our model can deliver more coherent, natural, and
diversified responses.

negative:
Quite surprisingly, exact maximum a posteriori (MAP) decoding of neural
language generators frequently leads to low-quality results. Rather, most
state-of-the-art results on language generation tasks are attained using beam
search despite its overwhelmingly high search error rate. This implies that the
MAP objective alone does not express the properties we desire in text, which
merits the question: if beam search is the answer, what was the question? We
frame beam search as the exact solution to a different decoding objective in
order to gain insights into why high probability under a model alone may not
indicate adequacy. We find that beam search enforces uniform information
density in text, a property motivated by cognitive science. We suggest a set of
decoding objectives that explicitly enforce this property and find that exact
decoding with these objectives alleviates the problems encountered when
decoding poorly calibrated language generation models. Additionally, we analyze
the text produced using various decoding strategies and see that, in our neural
machine translation experiments, the extent to which this property is adhered
to strongly correlates with BLEU.

negative:
Chatbot is increasingly thriving in different domains, however, because of
unexpected discourse complexity and training data sparseness, its potential
distrust hatches vital apprehension. Recently, Machine-Human Chatting Handoff
(MHCH), predicting chatbot failure and enabling human-algorithm collaboration
to enhance chatbot quality, has attracted increasing attention from industry
and academia. In this study, we propose a novel model, Role-Selected Sharing
Network (RSSN), which integrates both dialogue satisfaction estimation and
handoff prediction in one multi-task learning framework. Unlike prior efforts
in dialog mining, by utilizing local user satisfaction as a bridge, global
satisfaction detector and handoff predictor can effectively exchange critical
information. Specifically, we decouple the relation and interaction between the
two tasks by the role information after the shared encoder. Extensive
experiments on two public datasets demonstrate the effectiveness of our model.

negative:
There has been considerable progress made towards conversational models that
generate coherent and fluent responses; however, this often involves training
large language models on large dialogue datasets, such as Reddit. These large
conversational models provide little control over the generated responses, and
this control is further limited in the absence of annotated conversational
datasets for attribute specific generation that can be used for fine-tuning the
model. In this paper, we first propose and evaluate plug-and-play methods for
controllable response generation, which does not require dialogue specific
datasets and does not rely on fine-tuning a large model. While effective, the
decoding procedure induces considerable computational overhead, rendering the
conversational model unsuitable for interactive usage. To overcome this, we
introduce an approach that does not require further computation at decoding
time, while also does not require any fine-tuning of a large language model. We
demonstrate, through extensive automatic and human evaluation, a high degree of
control over the generated conversational responses with regard to multiple
desired attributes, while being fluent.

negative:
Responses generated by neural conversational models tend to lack
informativeness and diversity. We present Adversarial Information Maximization
(AIM), an adversarial learning strategy that addresses these two related but
distinct problems. To foster response diversity, we leverage adversarial
training that allows distributional matching of synthetic and real responses.
To improve informativeness, our framework explicitly optimizes a variational
lower bound on pairwise mutual information between query and response.
Empirical results from automatic and human evaluations demonstrate that our
methods significantly boost informativeness and diversity.

negative:
All AI models are susceptible to learning biases in data that they are
trained on. For generative dialogue models, being trained on real human
conversations containing unbalanced gender and race/ethnicity references can
lead to models that display learned biases, which we define here broadly as any
measurable differences in the distributions of words or semantic content of
conversations based on demographic groups. We measure the strength of such
biases by producing artificial conversations between two copies of a dialogue
model, conditioning one conversational partner to state a name commonly
associated with a certain gender and/or race/ethnicity. We find that larger
capacity models tend to exhibit more gender bias and greater stereotyping of
occupations by gender. We show that several methods of tuning these dialogue
models, specifically name scrambling, controlled generation, and unlikelihood
training, are effective in reducing bias in conversation, including on a
downstream conversational task. Name scrambling is also effective in lowering
differences in token usage across conversations where partners have names
associated with different genders or races/ethnicities.

negative:
Neural Machine Translation (NMT) models often lack diversity in their
generated translations, even when paired with search algorithm, like beam
search. A challenge is that the diversity in translations are caused by the
variability in the target language, and cannot be inferred from the source
sentence alone. In this paper, we propose to explicitly model this one-to-many
mapping by conditioning the decoder of a NMT model on a latent variable that
represents the domain of target sentences. The domain is a discrete variable
generated by a target encoder that is jointly trained with the NMT model. The
predicted domain of target sentences are given as input to the decoder during
training. At inference, we can generate diverse translations by decoding with
different domains. Unlike our strongest baseline (Shen et al., 2019), our
method can scale to any number of domains without affecting the performance or
the training time. We assess the quality and diversity of translations
generated by our model with several metrics, on three different datasets.

negative:
Encoder-decoder based neural architectures serve as the basis of
state-of-the-art approaches in end-to-end open domain dialog systems. Since
most of such systems are trained with a maximum likelihood~(MLE) objective they
suffer from issues such as lack of generalizability and the generic response
problem, i.e., a system response that can be an answer to a large number of
user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the
relevance and interestingness of a system response at each turn can be a useful
signal for mitigating such issues and improving system quality by selecting
responses from different approaches. Towards this goal, we present a system
that evaluates chatbot responses at each dialog turn for coherence and
engagement. Our system provides explicit turn-level dialog quality feedback,
which we show to be highly correlated with human evaluation. To show that
incorporating this feedback in the neural response generation models improves
dialog quality, we present two different and complementary mechanisms to
incorporate explicit feedback into a neural response generation model:
reranking and direct modification of the loss function during training. Our
studies show that a response generation model that incorporates these combined
feedback mechanisms produce more engaging and coherent responses in an
open-domain spoken dialog setting, significantly improving the response quality
using both automatic and human evaluation.

negative:
Conventional seq2seq chatbot models attempt only to find sentences with the
highest probabilities conditioned on the input sequences, without considering
the sentiment of the output sentences. In this paper, we investigate four
models to scale or adjust the sentiment of the chatbot response: a
persona-based model, reinforcement learning, a plug and play model, and
CycleGAN, all based on the seq2seq model. We also develop machine-evaluated
metrics to estimate whether the responses are reasonable given the input. These
metrics, together with human evaluation, are used to analyze the performance of
the four models in terms of different aspects; reinforcement learning and
CycleGAN are shown to be very attractive.

negative:
Using chatbots to deliver recommendations is increasingly popular. The design
of recommendation chatbots has primarily been taking an information-centric
approach by focusing on the recommended content per se. Limited attention is on
how social connection and relational strategies, such as self-disclosure from a
chatbot, may influence users' perception and acceptance of the recommendation.
In this work, we designed, implemented, and evaluated a social chatbot capable
of performing three different levels of self-disclosure: factual information
(low), cognitive opinions (medium), and emotions (high). In the evaluation, we
recruited 372 participants to converse with the chatbot on two topics: movies
and COVID-19 experiences. In each topic, the chatbot performed small talks and
made recommendations relevant to the topic. Participants were randomly assigned
to four experimental conditions where the chatbot used factual, cognitive,
emotional, and adaptive strategies to perform self-disclosures. By training a
text classifier to identify users' level of self-disclosure in real-time, the
adaptive chatbot can dynamically match its self-disclosure to the level of
disclosure exhibited by the users. Our results show that users reciprocate with
higher-level self-disclosure when a recommendation chatbot consistently
displays emotions throughout the conversation. Chatbot's emotional disclosure
also led to increased interactional enjoyment and more positive interpersonal
perception towards the bot, fostering a stronger human-chatbot relationship and
thus leading to increased recommendation effectiveness, including a higher
tendency to accept the recommendation. We discuss the understandings obtained
and implications to future design.

negative:
Although current state-of-the-art language models have achieved impressive
results in numerous natural language processing tasks, still they could not
solve the problem of producing repetitive, dull and sometimes inconsistent text
in open-ended text generation. Studies often attribute this problem to the
maximum likelihood training objective, and propose alternative approaches by
using stochastic decoding methods or altering the training objective. However,
there is still a lack of consistent evaluation metrics to directly compare the
efficacy of these solutions. In this work, we study different evaluation
metrics that have been proposed to evaluate quality, diversity and consistency
of machine-generated text. From there, we propose a practical pipeline to
evaluate language models in open-ended generation task, and research on how to
improve the model's performance in all dimensions by leveraging different
auxiliary training objectives.

negative:
The purpose of unconditional text generation is to train a model with real
sentences, then generate novel sentences of the same quality and diversity as
the training data. However, when different metrics are used for comparing the
methods of unconditional text generation, contradictory conclusions are drawn.
The difficulty is that both the diversity and quality of the sample should be
considered simultaneously when the models are evaluated. To solve this problem,
a novel metric of distributional discrepancy (DD) is designed to evaluate
generators based on the discrepancy between the generated and real training
sentences. However, it cannot compute the DD directly because the distribution
of real sentences is unavailable. Thus, we propose a method for estimating the
DD by training a neural-network-based text classifier. For comparison, three
existing metrics, bi-lingual evaluation understudy (BLEU) versus self-BLEU,
language model score versus reverse language model score, and Fr\'{e}chet
embedding distance, along with the proposed DD, are used to evaluate two
popular generative models of long short-term memory and generative pretrained
transformer 2 on both syntactic and real data. Experimental results show that
DD is significantly better than the three existing metrics for ranking these
generative models.

negative:
This paper addresses the question: Why do neural dialog systems generate
short and meaningless replies? We conjecture that, in a dialog system, an
utterance may have multiple equally plausible replies, causing the deficiency
of neural networks in the dialog application. We propose a systematic way to
mimic the dialog scenario in a machine translation system, and manage to
reproduce the phenomenon of generating short and less meaningful sentences in
the translation setting, showing evidence of our conjecture.

negative:
Recently, variational auto-encoder (VAE) based approaches have made
impressive progress on improving the diversity of generated responses. However,
these methods usually suffer the cost of decreased relevance accompanied by
diversity improvements. In this paper, we propose a novel multimodal response
generation framework with exemplar augmentation and curriculum optimization to
enhance relevance and diversity of generated responses. First, unlike existing
VAE-based models that usually approximate a simple Gaussian posterior
distribution, we present a Gaussian mixture posterior distribution (i.e,
multimodal) to further boost response diversity, which helps capture complex
semantics of responses. Then, to ensure that relevance does not decrease while
diversity increases, we fully exploit similar examples (exemplars) retrieved
from the training data into posterior distribution modeling to augment response
relevance. Furthermore, to facilitate the convergence of Gaussian mixture prior
and posterior distributions, we devise a curriculum optimization strategy to
progressively train the model under multiple training criteria from easy to
hard. Experimental results on widely used SwitchBoard and DailyDialog datasets
demonstrate that our model achieves significant improvements compared to strong
baselines in terms of diversity and relevance.

negative:
Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated
translations. This can be attributed to the limitation of SEQ2SEQ models in
capturing lexical and syntactic variations in a parallel corpus resulting from
different styles, genres, topics, or ambiguity of the translation process. In
this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that
improves both translation diversity and quality by adopting a committee of
specialized translation models rather than a single translation model. Each
mixture component selects its own training dataset via optimization of the
marginal loglikelihood, which leads to a soft clustering of the parallel
corpus. Experiments on four language pairs demonstrate the superiority of our
mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted
beam search. Our mixture model uses negligible additional parameters and incurs
no extra computation cost during decoding.

negative:
Neural sequence models are widely used to model time-series data. Equally
ubiquitous is the usage of beam search (BS) as an approximate inference
algorithm to decode output sequences from these models. BS explores the search
space in a greedy left-right fashion retaining only the top-B candidates -
resulting in sequences that differ only slightly from each other. Producing
lists of nearly identical sequences is not only computationally wasteful but
also typically fails to capture the inherent ambiguity of complex AI tasks. To
overcome this problem, we propose Diverse Beam Search (DBS), an alternative to
BS that decodes a list of diverse outputs by optimizing for a
diversity-augmented objective. We observe that our method finds better top-1
solutions by controlling for the exploration and exploitation of the search
space - implying that DBS is a better search algorithm. Moreover, these gains
are achieved with minimal computational or memory over- head as compared to
beam search. To demonstrate the broad applicability of our method, we present
results on image captioning, machine translation and visual question generation
using both standard quantitative metrics and qualitative human studies.
Further, we study the role of diversity for image-grounded language generation
tasks as the complexity of the image changes. We observe that our method
consistently outperforms BS and previously proposed techniques for diverse
decoding from neural sequence models.

negative:
Exposure bias has been regarded as a central problem for auto-regressive
language models (LM). It claims that teacher forcing would cause the test-time
generation to be incrementally distorted due to the training-generation
discrepancy. Although a lot of algorithms have been proposed to avoid teacher
forcing and therefore alleviate exposure bias, there is little work showing how
serious the exposure bias problem actually is. In this work, we focus on the
task of open-ended language generation, propose metrics to quantify the impact
of exposure bias in the aspects of quality, diversity, and consistency. Our key
intuition is that if we feed ground-truth data prefixes (instead of prefixes
generated by the model itself) into the model and ask it to continue the
generation, the performance should become much better because the
training-generation discrepancy in the prefix is removed. Both automatic and
human evaluations are conducted in our experiments. On the contrary to the
popular belief in exposure bias, we find that the the distortion induced by the
prefix discrepancy is limited, and does not seem to be incremental during the
generation. Moreover, our analysis reveals an interesting self-recovery ability
of the LM, which we hypothesize to be countering the harmful effects from
exposure bias.

negative:
Search result diversification is a beneficial approach to overcome
under-specified queries, such as those that are ambiguous or multi-faceted.
Existing approaches often rely on massive query logs and interaction data to
generate a variety of possible query intents, which then can be used to re-rank
documents. However, relying on user interaction data is problematic because one
first needs a massive user base to build a sufficient log; public query logs
are insufficient on their own. Given the recent success of causal language
models (such as the Text-To-Text Transformer (T5) model) at text generation
tasks, we explore the capacity of these models to generate potential query
intents. We find that to encourage diversity in the generated queries, it is
beneficial to adapt the model by including a new Distributional Causal Language
Modeling (DCLM) objective during fine-tuning and a representation replacement
during inference. Across six standard evaluation benchmarks, we find that our
method (which we call IntenT5) improves search result diversity and attains
(and sometimes exceeds) the diversity obtained when using query suggestions
based on a proprietary query log. Our analysis shows that our approach is most
effective for multi-faceted queries and is able to generalize effectively to
queries that were unseen in training data.

negative:
In recent years, several high-performance conversational systems have been
proposed based on the Transformer encoder-decoder model. Although previous
studies analyzed the effects of the model parameters and the decoding method on
subjective dialogue evaluations with overall metrics, they did not analyze how
the differences of fine-tuning datasets affect on user's detailed impression.
In addition, the Transformer-based approach has only been verified for English,
not for such languages with large inter-language distances as Japanese. In this
study, we develop large-scale Transformer-based Japanese dialogue models and
Japanese chit-chat datasets to examine the effectiveness of the
Transformer-based approach for building chit-chat dialogue systems. We
evaluated and analyzed the impressions of human dialogues in different
fine-tuning datasets, model parameters, and the use of additional information.

negative:
Open-domain response generation is the task of generating sensible and
informative re-sponses to the source sentence. However, neural models tend to
generate safe and mean-ingless responses. While cue-word introducing approaches
encourage responses with concrete semantics and have shown tremendous
potential, they still fail to explore di-verse responses during decoding. In
this paper, we propose a novel but natural approach that can produce multiple
cue-words during decoding, and then uses the produced cue-words to drive
decoding and shrinks the decoding vocabulary. Thus the neural genera-tion model
can explore the full space of responses and discover informative ones with
efficiency. Experimental results show that our approach significantly
outperforms several strong baseline models with much lower decoding complexity.
Especially, our approach can converge to concrete semantics more efficiently
during decoding.

negative:
Building open-domain chatbots is a challenging area for machine learning
research. While prior work has shown that scaling neural models in the number
of parameters and the size of the data they are trained on gives improved
results, we show that other ingredients are important for a high-performing
chatbot. Good conversation requires a number of skills that an expert
conversationalist blends in a seamless way: providing engaging talking points
and listening to their partners, and displaying knowledge, empathy and
personality appropriately, while maintaining a consistent persona. We show that
large scale models can learn these skills when given appropriate training data
and choice of generation strategy. We build variants of these recipes with 90M,
2.7B and 9.4B parameter models, and make our models and code publicly
available. Human evaluations show our best models are superior to existing
approaches in multi-turn dialogue in terms of engagingness and humanness
measurements. We then discuss the limitations of this work by analyzing failure
cases of our models.

negative:
Conventional seq2seq chatbot models only try to find the sentences with the
highest probabilities conditioned on the input sequences, without considering
the sentiment of the output sentences. Some research works trying to modify the
sentiment of the output sequences were reported. In this paper, we propose five
models to scale or adjust the sentiment of the chatbot response: persona-based
model, reinforcement learning, plug and play model, sentiment transformation
network and cycleGAN, all based on the conventional seq2seq model. We also
develop two evaluation metrics to estimate if the responses are reasonable
given the input. These metrics together with other two popularly used metrics
were used to analyze the performance of the five proposed models on different
aspects, and reinforcement learning and cycleGAN were shown to be very
attractive. The evaluation metrics were also found to be well correlated with
human evaluation.

negative:
We develop a chatbot using Deep Bidirectional Transformer models (BERT) to
handle client questions in financial investment customer service. The bot can
recognize 381 intents, and decides when to say "I don't know" and escalates
irrelevant/uncertain questions to human operators. Our main novel contribution
is the discussion about uncertainty measure for BERT, where three different
approaches are systematically compared on real problems. We investigated two
uncertainty metrics, information entropy and variance of dropout sampling in
BERT, followed by mixed-integer programming to optimize decision thresholds.
Another novel contribution is the usage of BERT as a language model in
automatic spelling correction. Inputs with accidental spelling errors can
significantly decrease intent classification performance. The proposed approach
combines probabilities from masked language model and word edit distances to
find the best corrections for misspelled words. The chatbot and the entire
conversational AI system are developed using open-source tools, and deployed
within our company's intranet. The proposed approach can be useful for
industries seeking similar in-house solutions in their specific business
domains. We share all our code and a sample chatbot built on a public dataset
on Github.

negative:
Non-goal oriented dialog agents (i.e. chatbots) aim to produce varying and
engaging conversations with a user; however, they typically exhibit either
inconsistent personality across conversations or the average personality of all
users. This paper addresses these issues by controlling an agent's persona upon
generation via conditioning on prior conversations of a target actor. In doing
so, we are able to utilize more abstract patterns within a person's speech and
better emulate them in generated responses. This work introduces the Generative
Conversation Control model, an augmented and fine-tuned GPT-2 language model
that conditions on past reference conversations to probabilistically model
multi-turn conversations in the actor's persona. We introduce an accompanying
data collection procedure to obtain 10.3M conversations from 6 months worth of
Reddit comments. We demonstrate that scaling model sizes from 117M to 8.3B
parameters yields an improvement from 23.14 to 13.14 perplexity on 1.7M held
out Reddit conversations. Increasing model scale yielded similar improvements
in human evaluations that measure preference of model samples to the held out
target distribution in terms of realism (31% increased to 37% preference),
style matching (37% to 42%), grammar and content quality (29% to 42%), and
conversation coherency (32% to 40%). We find that conditionally modeling past
conversations improves perplexity by 0.47 in automatic evaluations. Through
human trials we identify positive trends between conditional modeling and style
matching and outline steps to further improve persona control.

negative:
Technology for language generation has advanced rapidly, spurred by
advancements in pre-training large models on massive amounts of data and the
need for intelligent agents to communicate in a natural manner. While
techniques can effectively generate fluent text, they can also produce
undesirable societal biases that can have a disproportionately negative impact
on marginalized populations. Language generation presents unique challenges for
biases in terms of direct user interaction and the structure of decoding
techniques. To better understand these challenges, we present a survey on
societal biases in language generation, focusing on how data and techniques
contribute to biases and progress towards reducing biases. Motivated by a lack
of studies on biases from decoding techniques, we also conduct experiments to
quantify the effects of these techniques. By further discussing general trends
and open challenges, we call to attention promising directions for research and
the importance of fairness and inclusivity considerations for language
generation applications.

negative:
Generating high-quality text with sufficient diversity is essential for a
wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood (MLE)
models trained with teacher forcing have consistently been reported as weak
baselines, where poor performance is attributed to exposure bias (Bengio et
al., 2015; Ranzato et al., 2015); at inference time, the model is fed its own
prediction instead of a ground-truth token, which can lead to accumulating
errors and poor samples. This line of reasoning has led to an outbreak of
adversarial based approaches for NLG, on the account that GANs do not suffer
from exposure bias. In this work, we make several surprising observations which
contradict common beliefs. First, we revisit the canonical evaluation framework
for NLG, and point out fundamental flaws with quality-only evaluation: we show
that one can outperform such metrics using a simple, well-known temperature
parameter to artificially reduce the entropy of the model's conditional
distributions. Second, we leverage the control over the quality / diversity
trade-off given by this parameter to evaluate models over the whole
quality-diversity spectrum and find MLE models constantly outperform the
proposed GAN variants over the whole quality-diversity space. Our results have
several implications: 1) The impact of exposure bias on sample quality is less
severe than previously thought, 2) temperature tuning provides a better quality
/ diversity trade-off than adversarial training while being easier to train,
easier to cross-validate, and less computationally expensive. Code to reproduce
the experiments is available at github.com/pclucas14/GansFallingShort

negative:
Machine translation is a popular test bed for research in neural
sequence-to-sequence models but despite much recent research, there is still a
lack of understanding of these models. Practitioners report performance
degradation with large beams, the under-estimation of rare words and a lack of
diversity in the final translations. Our study relates some of these issues to
the inherent uncertainty of the task, due to the existence of multiple valid
translations for a single source sentence, and to the extrinsic uncertainty
caused by noisy training data. We propose tools and metrics to assess how
uncertainty in the data is captured by the model distribution and how it
affects search strategies that generate translations. Our results show that
search works remarkably well but that models tend to spread too much
probability mass over the hypothesis space. Next, we propose tools to assess
model calibration and show how to easily fix some shortcomings of current
models. As part of this study, we release multiple human reference translations
for two popular benchmarks.

negative:
Neural text generation is a key tool in natural language applications, but it
is well known there are major problems at its core. In particular, standard
likelihood training and decoding leads to dull and repetitive outputs. While
some post-hoc fixes have been proposed, in particular top-$k$ and nucleus
sampling, they do not address the fact that the token-level probabilities
predicted by the model are poor. In this paper we show that the likelihood
objective itself is at fault, resulting in a model that assigns too much
probability to sequences containing repeats and frequent words, unlike those
from the human training distribution. We propose a new objective, unlikelihood
training, which forces unlikely generations to be assigned lower probability by
the model. We show that both token and sequence level unlikelihood training
give less repetitive, less dull text while maintaining perplexity, giving
superior generations using standard greedy or beam search. According to human
evaluations, our approach with standard beam search also outperforms the
currently popular decoding methods of nucleus sampling or beam blocking, thus
providing a strong alternative to existing techniques.

negative:
To build an open-domain multi-turn conversation system is one of the most
interesting and challenging tasks in Artificial Intelligence. Many research
efforts have been dedicated to building such dialogue systems, yet few shed
light on modeling the conversation flow in an ongoing dialogue. Besides, it is
common for people to talk about highly relevant aspects during a conversation.
And the topics are coherent and drift naturally, which demonstrates the
necessity of dialogue flow modeling. To this end, we present the multi-turn
cue-words driven conversation system with reinforcement learning method (RLCw),
which strives to select an adaptive cue word with the greatest future credit,
and therefore improve the quality of generated responses. We introduce a new
reward to measure the quality of cue words in terms of effectiveness and
relevance. To further optimize the model for long-term conversations, a
reinforcement approach is adopted in this paper. Experiments on real-life
dataset demonstrate that our model consistently outperforms a set of
competitive baselines in terms of simulated turns, diversity and human
evaluation.

negative:
Systems powered by artificial intelligence are being developed to be more
user-friendly by communicating with users in a progressively human-like
conversational way. Chatbots, also known as dialogue systems, interactive
conversational agents, or virtual agents are an example of such systems used in
a wide variety of applications ranging from customer support in the business
domain to companionship in the healthcare sector. It is becoming increasingly
important to develop chatbots that can best respond to the personalized needs
of their users so that they can be as helpful to the user as possible in a real
human way. This paper investigates and compares three popular existing chatbots
API offerings and then propose and develop a voice interactive and multilingual
chatbot that can effectively respond to users mood, tone, and language using
IBM Watson Assistant, Tone Analyzer, and Language Translator. The chatbot was
evaluated using a use case that was targeted at responding to users needs
regarding exam stress based on university students survey data generated using
Google Forms. The results of measuring the chatbot effectiveness at analyzing
responses regarding exam stress indicate that the chatbot responding
appropriately to the user queries regarding how they are feeling about exams
76.5%. The chatbot could also be adapted for use in other application areas
such as student info-centers, government kiosks, and mental health support
systems.

negative:
The ability of a dialog system to express consistent language style during
conversations has a direct, positive impact on its usability and on user
satisfaction. Although previous studies have demonstrated that style transfer
is feasible with a large amount of parallel data, it is often impossible to
collect such data for different styles. In this paper, instead of manually
constructing conversation data with a certain style, we propose a flexible
framework that adapts a generic retrieval-based dialogue system to mimic the
language style of a specified persona without any parallel data. Our approach
is based on automatic generation of stylized data by learning the usage of
jargon, and then rewriting the generic conversations to a stylized one by
incorporating the jargon. In experiments we implemented dialogue systems with
five distinct language styles, and the result shows our framework significantly
outperforms baselines in terms of the average score of responses' relevance and
style degree, and content diversity. A/B testing on a commercial chatbot shows
that users are more satisfied with our system. This study demonstrates the
feasibility of building stylistic dialogue systems by simple data augmentation.

negative:
Being able to generate informative and coherent dialogue responses is crucial
when designing human-like open-domain dialogue systems. Encoder-decoder-based
dialogue models tend to produce generic and dull responses during the decoding
step because the most predictable response is likely to be a non-informative
response instead of the most suitable one. To alleviate this problem, we
propose to train the generation model in a bidirectional manner by adding a
backward reasoning step to the vanilla encoder-decoder training. The proposed
backward reasoning step pushes the model to produce more informative and
coherent content because the forward generation step's output is used to infer
the dialogue context in the backward direction. The advantage of our method is
that the forward generation and backward reasoning steps are trained
simultaneously through the use of a latent variable to facilitate bidirectional
optimization. Our method can improve response quality without introducing side
information (e.g., a pre-trained topic model). The proposed bidirectional
response generation method achieves state-of-the-art performance for response
quality.

negative:
We study multi-turn response generation for open-domain dialogues. The
existing state-of-the-art addresses the problem with deep neural architectures.
While these models improved response quality, their complexity also hinders the
application of the models in real systems. In this work, we pursue a model that
has a simple structure yet can effectively leverage conversation contexts for
response generation. To this end, we propose four auxiliary tasks including
word order recovery, utterance order recovery, masked word recovery, and masked
utterance recovery, and optimize the objectives of these tasks together with
maximizing the likelihood of generation. By this means, the auxiliary tasks
that relate to context understanding can guide the learning of the generation
model to achieve a better local optimum. Empirical studies with three
benchmarks indicate that our model can significantly outperform
state-of-the-art generation models in terms of response quality on both
automatic evaluation and human judgment, and at the same time enjoys a much
faster decoding process.

negative:
In recent times, a large number of people have been involved in establishing
their own businesses. Unlike humans, chatbots can serve multiple customers at a
time, are available 24/7 and reply in less than a fraction of a second. Though
chatbots perform well in task-oriented activities, in most cases they fail to
understand personalized opinions, statements or even queries which later impact
the organization for poor service management. Lack of understanding
capabilities in bots disinterest humans to continue conversations with them.
Usually, chatbots give absurd responses when they are unable to interpret a
user's text accurately. Extracting the client reviews from conversations by
using chatbots, organizations can reduce the major gap of understanding between
the users and the chatbot and improve their quality of products and
services.Thus, in our research we incorporated all the key elements that are
necessary for a chatbot to analyse and understand an input text precisely and
accurately. We performed sentiment analysis, emotion detection, intent
classification and named-entity recognition using deep learning to develop
chatbots with humanistic understanding and intelligence. The efficiency of our
approach can be demonstrated accordingly by the detailed analysis.

negative:
Recent research in neural machine translation has largely focused on two
aspects; neural network architectures and end-to-end learning algorithms. The
problem of decoding, however, has received relatively little attention from the
research community. In this paper, we solely focus on the problem of decoding
given a trained neural machine translation model. Instead of trying to build a
new decoding algorithm for any specific decoding objective, we propose the idea
of trainable decoding algorithm in which we train a decoding algorithm to find
a translation that maximizes an arbitrary decoding objective. More
specifically, we design an actor that observes and manipulates the hidden state
of the neural machine translation decoder and propose to train it using a
variant of deterministic policy gradient. We extensively evaluate the proposed
algorithm using four language pairs and two decoding objectives and show that
we can indeed train a trainable greedy decoder that generates a better
translation (in terms of a target decoding objective) with minimal
computational overhead.

negative:
Generating diverse and relevant questions over text is a task with widespread
applications. We argue that commonly-used evaluation metrics such as BLEU and
METEOR are not suitable for this task due to the inherent diversity of
reference questions, and propose a scheme for extending conventional metrics to
reflect diversity. We furthermore propose a variational encoder-decoder model
for this task. We show through automatic and human evaluation that our
variational model improves diversity without loss of quality, and demonstrate
how our evaluation scheme reflects this improvement.

negative:
Chatbot is a technology that is used to mimic human behavior using natural
language. There are different types of Chatbot that can be used as
conversational agent in various business domains in order to increase the
customer service and satisfaction. For any business domain, it requires a
knowledge base to be built for that domain and design an information retrieval
based system that can respond the user with a piece of documentation or
generated sentences. The core component of a Chatbot is Natural Language
Understanding (NLU) which has been impressively improved by deep learning
methods. But we often lack such properly built NLU modules and requires more
time to build it from scratch for high quality conversations. This may
encourage fresh learners to build a Chatbot from scratch with simple
architecture and using small dataset, although it may have reduced
functionality, rather than building high quality data driven methods. This
research focuses on Named Entity Recognition (NER) and Intent Classification
models which can be integrated into NLU service of a Chatbot. Named entities
will be inserted manually in the knowledge base and automatically detected in a
given sentence. The NER model in the proposed architecture is based on
artificial neural network which is trained on manually created entities and
evaluated using CoNLL-2003 dataset.

negative:
Traditional neural language models tend to generate generic replies with poor
logic and no emotion. In this paper, a syntactically constrained
bidirectional-asynchronous approach for emotional conversation generation
(E-SCBA) is proposed to address this issue. In our model, pre-generated emotion
keywords and topic keywords are asynchronously introduced into the process of
decoding. It is much different from most existing methods which generate
replies from the first word to the last. Through experiments, the results
indicate that our approach not only improves the diversity of replies, but
gains a boost on both logic and emotion compared with baselines.

negative:
Existing approaches to neural machine translation (NMT) generate the target
language sequence token by token from left to right. However, this kind of
unidirectional decoding framework cannot make full use of the target-side
future contexts which can be produced in a right-to-left decoding direction,
and thus suffers from the issue of unbalanced outputs. In this paper, we
introduce a synchronous bidirectional neural machine translation (SB-NMT) that
predicts its outputs using left-to-right and right-to-left decoding
simultaneously and interactively, in order to leverage both of the history and
future information at the same time. Specifically, we first propose a new
algorithm that enables synchronous bidirectional decoding in a single model.
Then, we present an interactive decoding model in which left-to-right
(right-to-left) generation does not only depend on its previously generated
outputs, but also relies on future contexts predicted by right-to-left
(left-to-right) decoding. We extensively evaluate the proposed SB-NMT model on
large-scale NIST Chinese-English, WMT14 English-German, and WMT18
Russian-English translation tasks. Experimental results demonstrate that our
model achieves significant improvements over the strong Transformer model by
3.92, 1.49 and 1.04 BLEU points respectively, and obtains the state-of-the-art
performance on Chinese-English and English-German translation tasks.

negative:
In this paper, we study sequence-to-sequence (S2S) keyphrase generation
models from the perspective of diversity. Recent advances in neural natural
language generation have made possible remarkable progress on the task of
keyphrase generation, demonstrated through improvements on quality metrics such
as F1-score. However, the importance of diversity in keyphrase generation has
been largely ignored. We first analyze the extent of information redundancy
present in the outputs generated by a baseline model trained using maximum
likelihood estimation (MLE). Our findings show that repetition of keyphrases is
a major issue with MLE training. To alleviate this issue, we adopt neural
unlikelihood (UL) objective for training the S2S model. Our version of UL
training operates at (1) the target token level to discourage the generation of
repeating tokens; (2) the copy token level to avoid copying repetitive tokens
from the source text. Further, to encourage better model planning during the
decoding process, we incorporate K-step ahead token prediction objective that
computes both MLE and UL losses on future tokens as well. Through extensive
experiments on datasets from three different domains we demonstrate that the
proposed approach attains considerably large diversity gains, while maintaining
competitive output quality.

negative:
Politically sensitive topics are still a challenge for open-domain chatbots.
However, dealing with politically sensitive content in a responsible,
non-partisan, and safe behavior way is integral for these chatbots. Currently,
the main approach to handling political sensitivity is by simply changing such
a topic when it is detected. This is safe but evasive and results in a chatbot
that is less engaging. In this work, as a first step towards a politically safe
chatbot, we propose a group of metrics for assessing their political prudence.
We then conduct political prudence analysis of various chatbots and discuss
their behavior from multiple angles through our automatic metric and human
evaluation metrics. The testsets and codebase are released to promote research
in this area.

negative:
We present Meena, a multi-turn open-domain chatbot trained end-to-end on data
mined and filtered from public domain social media conversations. This 2.6B
parameter neural network is simply trained to minimize perplexity of the next
token. We also propose a human evaluation metric called Sensibleness and
Specificity Average (SSA), which captures key elements of a human-like
multi-turn conversation. Our experiments show strong correlation between
perplexity and SSA. The fact that the best perplexity end-to-end trained Meena
scores high on SSA (72% on multi-turn evaluation) suggests that a human-level
SSA of 86% is potentially within reach if we can better optimize perplexity.
Additionally, the full version of Meena (with a filtering mechanism and tuned
decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots
we evaluated.

negative:
Narrative generation is an open-ended NLP task in which a model generates a
story given a prompt. The task is similar to neural response generation for
chatbots; however, innovations in response generation are often not applied to
narrative generation, despite the similarity between these tasks. We aim to
bridge this gap by applying and evaluating advances in decoding methods for
neural response generation to neural narrative generation. In particular, we
employ GPT-2 and perform ablations across nucleus sampling thresholds and
diverse decoding hyperparameters -- specifically, maximum mutual information --
analyzing results over multiple criteria with automatic and human evaluation.
We find that (1) nucleus sampling is generally best with thresholds between 0.7
and 0.9; (2) a maximum mutual information objective can improve the quality of
generated stories; and (3) established automatic metrics do not correlate well
with human judgments of narrative quality on any qualitative metric.

negative:
Diverse and accurate vision+language modeling is an important goal to retain
creative freedom and maintain user engagement. However, adequately capturing
the intricacies of diversity in language models is challenging. Recent works
commonly resort to latent variable models augmented with more or less
supervision from object detectors or part-of-speech tags. Common to all those
methods is the fact that the latent variable either only initializes the
sentence generation process or is identical across the steps of generation.
Both methods offer no fine-grained control. To address this concern, we propose
Seq-CVAE which learns a latent space for every word position. We encourage this
temporal latent space to capture the 'intention' about how to complete the
sentence by mimicking a representation which summarizes the future. We
illustrate the efficacy of the proposed approach to anticipate the sentence
continuation on the challenging MSCOCO dataset, significantly improving
diversity metrics compared to baselines while performing on par w.r.t sentence
quality.

negative:
Despite growing interest in natural language generation (NLG) models that
produce diverse outputs, there is currently no principled method for evaluating
the diversity of an NLG system. In this work, we propose a framework for
evaluating diversity metrics. The framework measures the correlation between a
proposed diversity metric and a diversity parameter, a single parameter that
controls some aspect of diversity in generated text. For example, a diversity
parameter might be a binary variable used to instruct crowdsourcing workers to
generate text with either low or high content diversity. We demonstrate the
utility of our framework by: (a) establishing best practices for eliciting
diversity judgments from humans, (b) showing that humans substantially
outperform automatic metrics in estimating content diversity, and (c)
demonstrating that existing methods for controlling diversity by tuning a
"decoding parameter" mostly affect form but not meaning. Our framework can
advance the understanding of different diversity metrics, an essential step on
the road towards better NLG systems.

negative:
In neural machine translation, a source sequence of words is encoded into a
vector from which a target sequence is generated in the decoding phase.
Differently from statistical machine translation, the associations between
source words and their possible target counterparts are not explicitly stored.
Source and target words are at the two ends of a long information processing
procedure, mediated by hidden states at both the source encoding and the target
decoding phases. This makes it possible that a source word is incorrectly
translated into a target word that is not any of its admissible equivalent
counterparts in the target language.
  In this paper, we seek to somewhat shorten the distance between source and
target words in that procedure, and thus strengthen their association, by means
of a method we term bridging source and target word embeddings. We experiment
with three strategies: (1) a source-side bridging model, where source word
embeddings are moved one step closer to the output target sequence; (2) a
target-side bridging model, which explores the more relevant source word
embeddings for the prediction of the target sequence; and (3) a direct bridging
model, which directly connects source and target word embeddings seeking to
minimize errors in the translation of ones by the others.
  Experiments and analysis presented in this paper demonstrate that the
proposed bridging models are able to significantly improve quality of both
sentence translation, in general, and alignment and translation of individual
source words with target words, in particular.

negative:
Existing research on response generation for chatbot focuses on \textbf{First
Response Generation} which aims to teach the chatbot to say the first response
(e.g. a sentence) appropriate to the conversation context (e.g. the user's
query). In this paper, we introduce a new task \textbf{Second Response
Generation}, termed as Improv chat, which aims to teach the chatbot to say the
second response after saying the first response with respect the conversation
context, so as to lighten the burden on the user to keep the conversation
going. Specifically, we propose a general learning based framework and develop
a retrieval based system which can generate the second responses with the
users' query and the chatbot's first response as input. We present the approach
to building the conversation corpus for Improv chat from public forums and
social networks, as well as the neural networks based models for response
matching and ranking. We include the preliminary experiments and results in
this paper. This work could be further advanced with better deep matching
models for retrieval base systems or generative models for generation based
systems as well as extensive evaluations in real-life applications.

negative:
Searching for an available, reliable, official, and understandable
information is not a trivial task due to scattered information across the
internet, and the availability lack of governmental communication channels
communicating with African dialects and languages. In this paper, we introduce
an Artificial Intelligence Powered chatbot for crisis communication that would
be omnichannel, multilingual and multi dialectal. We present our work on
modified StarSpace embedding tailored for African dialects for the
question-answering task along with the architecture of the proposed chatbot
system and a description of the different layers. English, French, Arabic,
Tunisian, Igbo,Yor\`ub\'a, and Hausa are used as languages and dialects.
Quantitative and qualitative evaluation results are obtained for our real
deployed Covid-19 chatbot. Results show that users are satisfied and the
conversation with the chatbot is meeting customer needs.

negative:
Despite being virtually ubiquitous, sequence-to-sequence models are
challenged by their lack of diversity and inability to be externally
controlled. In this paper, we speculate that a fundamental shortcoming of
sequence generation models is that the decoding is done strictly from
left-to-right, meaning that outputs values generated earlier have a profound
effect on those generated later. To address this issue, we propose a novel
middle-out decoder architecture that begins from an initial middle-word and
simultaneously expands the sequence in both directions. To facilitate
information flow and maintain consistent decoding, we introduce a dual
self-attention mechanism that allows us to model complex dependencies between
the outputs. We illustrate the performance of our model on the task of video
captioning, as well as a synthetic sequence de-noising task. Our middle-out
decoder achieves significant improvements on de-noising and competitive
performance in the task of video captioning, while quantifiably improving the
caption diversity. Furthermore, we perform a qualitative analysis that
demonstrates our ability to effectively control the generation process of our
decoder.

negative:
Through advances in their conversational abilities, chatbots have started to
request and process an increasing variety of sensitive personal information.
The accurate disclosure of sensitive information is essential where it is used
to provide advice and support to users in the healthcare and finance sectors.
In this study, we explore users' concerns regarding factors associated with the
use of sensitive data by chatbot providers. We surveyed a representative sample
of 491 British citizens. Our results show that the user concerns focus on
deleting personal information and concerns about their data's inappropriate
use. We also identified that individuals were concerned about losing control
over their data after a conversation with conversational agents. We found no
effect from a user's gender or education but did find an effect from the user's
age, with those over 45 being more concerned than those under 45. We also
considered the factors that engender trust in a chatbot. Our respondents'
primary focus was on the chatbot's technical elements, with factors such as the
response quality being identified as the most critical factor. We again found
no effect from the user's gender or education level; however, when we
considered some social factors (e.g. avatars or perceived 'friendliness'), we
found those under 45 years old rated these as more important than those over
45. The paper concludes with a discussion of these results within the context
of designing inclusive, digital systems that support a wide range of users.

negative:
In this paper, we explore the problem of developing personalized chatbots. A
personalized chatbot is designed as a digital chatting assistant for a user.
The key characteristic of a personalized chatbot is that it should have a
consistent personality with the corresponding user. It can talk the same way as
the user when it is delegated to respond to others' messages. We present a
retrieval-based personalized chatbot model, namely IMPChat, to learn an
implicit user profile from the user's dialogue history. We argue that the
implicit user profile is superior to the explicit user profile regarding
accessibility and flexibility. IMPChat aims to learn an implicit user profile
through modeling user's personalized language style and personalized
preferences separately. To learn a user's personalized language style, we
elaborately build language models from shallow to deep using the user's
historical responses; To model a user's personalized preferences, we explore
the conditional relations underneath each post-response pair of the user. The
personalized preferences are dynamic and context-aware: we assign higher
weights to those historical pairs that are topically related to the current
query when aggregating the personalized preferences. We match each response
candidate with the personalized language style and personalized preference,
respectively, and fuse the two matching signals to determine the final ranking
score. Comprehensive experiments on two large datasets show that our method
outperforms all baseline models.

negative:
Errors in medication intake among elderly people are very common. One of the
main causes for this is their loss of ability to retain information. The high
amount of medicine intake required by the advanced age is another limiting
factor. Thence, the design of an interactive aid system, preferably using
natural language, to help the older population with medication is in demand. A
chatbot based on a subtractive cluster algorithm, included in unsupervised
learned models, is the chosen solution since the processing of natural
languages is a necessary step in view to construct a chatbot able to answer
questions that older people may pose upon themselves concerning a particular
drug. In this work, the subtractive mountain clustering algorithm has been
adapted to the problem of natural languages processing. This algorithm version
allows for the association of a set of words into clusters. After finding the
centre of every cluster -- the most relevant word, all the others are
aggregated according to a defined metric adapted to the language processing
realm. All the relevant stored information is processed, as well as the
questions, by the algorithm. The correct processing of the text enables the
chatbot to produce answers that relate to the posed queries. To validate the
method, we use the package insert of a drug as the available information and
formulate associated questions.

negative:
This work presents an empirical approach to quantifying the loss of lexical
richness in Machine Translation (MT) systems compared to Human Translation
(HT). Our experiments show how current MT systems indeed fail to render the
lexical diversity of human generated or translated text. The inability of MT
systems to generate diverse outputs and its tendency to exacerbate already
frequent patterns while ignoring less frequent ones, might be the underlying
cause for, among others, the currently heavily debated issues related to gender
biased output. Can we indeed, aside from biased data, talk about an algorithm
that exacerbates seen biases?

negative:
Most chatbot literature that focuses on improving the fluency and coherence
of a chatbot, is dedicated to making chatbots more human-like. However, very
little work delves into what really separates humans from chatbots -- humans
intrinsically understand the effect their responses have on the interlocutor
and often respond with an intention such as proposing an optimistic view to
make the interlocutor feel better. This paper proposes an innovative framework
to train chatbots to possess human-like intentions. Our framework includes a
guiding chatbot and an interlocutor model that plays the role of humans. The
guiding chatbot is assigned an intention and learns to induce the interlocutor
to reply with responses matching the intention, for example, long responses,
joyful responses, responses with specific words, etc. We examined our framework
using three experimental setups and evaluated the guiding chatbot with four
different metrics to demonstrate flexibility and performance advantages.
Additionally, we performed trials with human interlocutors to substantiate the
guiding chatbot's effectiveness in influencing the responses of humans to a
certain extent. Code will be made available to the public.

negative:
As the wide adoption of intelligent chatbot in human daily life, user demands
for such systems evolve from basic task-solving conversations to more casual
and friend-like communication. To meet the user needs and build emotional bond
with users, it is essential for social chatbots to incorporate more human-like
and advanced linguistic features. In this paper, we investigate the usage of a
commonly used rhetorical device by human -- metaphor for social chatbot. Our
work first designs a metaphor generation framework, which generates topic-aware
and novel figurative sentences. By embedding the framework into a chatbot
system, we then enables the chatbot to communicate with users using figurative
language. Human annotators validate the novelty and properness of the generated
metaphors. More importantly, we evaluate the effects of employing metaphors in
human-chatbot conversations. Experiments indicate that our system effectively
arouses user interests in communicating with our chatbot, resulting in
significantly longer human-chatbot conversations.

negative:
Chatbots are envisioned to dramatically change the future of Software
Engineering, allowing practitioners to chat and inquire about their software
projects and interact with different services using natural language. At the
heart of every chatbot is a Natural Language Understanding (NLU) component that
enables the chatbot to understand natural language input. Recently, many NLU
platforms were provided to serve as an off-the-shelf NLU component for
chatbots, however, selecting the best NLU for Software Engineering chatbots
remains an open challenge.
  Therefore, in this paper, we evaluate four of the most commonly used NLUs,
namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on
which NLU should be used in Software Engineering based chatbots. Specifically,
we examine the NLUs' performance in classifying intents, confidence scores
stability, and extracting entities. To evaluate the NLUs, we use two datasets
that reflect two common tasks performed by Software Engineering practitioners,
1) the task of chatting with the chatbot to ask questions about software
repositories 2) the task of asking development questions on Q&A forums (e.g.,
Stack Overflow). According to our findings, IBM Watson is the best performing
NLU when considering the three aspects (intents classification, confidence
scores, and entity extraction). However, the results from each individual
aspect show that, in intents classification, IBM Watson performs the best with
an F1-measure > 84%, but in confidence scores, Rasa comes on top with a median
confidence score higher than 0.91. Our results also show that all NLUs, except
for Dialogflow, generally provide trustable confidence scores. For entity
extraction, Microsoft LUIS and IBM Watson outperform other NLUs in the two SE
tasks. Our results provide guidance to software engineering practitioners when
deciding which NLU to use in their chatbots.

negative:
The goal of text generation models is to fit the underlying real probability
distribution of text. For performance evaluation, quality and diversity metrics
are usually applied. However, it is still not clear to what extend can the
quality-diversity evaluation reflect the distribution-fitting goal. In this
paper, we try to reveal such relation in a theoretical approach. We prove that
under certain conditions, a linear combination of quality and diversity
constitutes a divergence metric between the generated distribution and the real
distribution. We also show that the commonly used BLEU/Self-BLEU metric pair
fails to match any divergence metric, thus propose CR/NRR as a substitute for
quality/diversity metric pair.

negative:
In this paper we present the results of our experiments in training and
deploying a self-supervised retrieval-based chatbot trained with contrastive
learning for assisting customer support agents. In contrast to most existing
research papers in this area where the focus is on solving just one component
of a deployable chatbot, we present an end-to-end set of solutions to take the
reader from an unlabelled chatlogs to a deployed chatbot. This set of solutions
includes creating a self-supervised dataset and a weakly labelled dataset from
chatlogs, as well as a systematic approach to selecting a fixed list of canned
responses. We present a hierarchical-based RNN architecture for the response
selection model, chosen for its ability to cache intermediate utterance
embeddings, which helped to meet deployment inference speed requirements. We
compare the performance of this architecture across 3 different learning
objectives: self-supervised contrastive learning, binary classification, and
multi-class classification. We find that using a self-supervised contrastive
learning model outperforms training the binary and multi-class classification
models on a weakly labelled dataset. Our results validate that the
self-supervised contrastive learning approach can be effectively used for a
real-world chatbot scenario.

negative:
We present ParaBank, a large-scale English paraphrase dataset that surpasses
prior work in both quantity and quality. Following the approach of ParaNMT, we
train a Czech-English neural machine translation (NMT) system to generate novel
paraphrases of English reference sentences. By adding lexical constraints to
the NMT decoding procedure, however, we are able to produce multiple
high-quality sentential paraphrases per source sentence, yielding an English
paraphrase resource with more than 4 billion generated tokens and exhibiting
greater lexical diversity. Using human judgments, we also demonstrate that
ParaBank's paraphrases improve over ParaNMT on both semantic similarity and
fluency. Finally, we use ParaBank to train a monolingual NMT model with the
same support for lexically-constrained decoding for sentence rewriting tasks.

negative:
The majority of conversations a dialogue agent sees over its lifetime occur
after it has already been trained and deployed, leaving a vast store of
potential training signal untapped. In this work, we propose the self-feeding
chatbot, a dialogue agent with the ability to extract new training examples
from the conversations it participates in. As our agent engages in
conversation, it also estimates user satisfaction in its responses. When the
conversation appears to be going well, the user's responses become new training
examples to imitate. When the agent believes it has made a mistake, it asks for
feedback; learning to predict the feedback that will be given improves the
chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with
over 131k training examples, we find that learning from dialogue with a
self-feeding chatbot significantly improves performance, regardless of the
amount of traditional supervision.

negative:
In this paper, we focus on the personalized response generation for
conversational systems. Based on the sequence to sequence learning, especially
the encoder-decoder framework, we propose a two-phase approach, namely
initialization then adaptation, to model the responding style of human and then
generate personalized responses. For evaluation, we propose a novel human aided
method to evaluate the performance of the personalized response generation
models by online real-time conversation and offline human judgement. Moreover,
the lexical divergence of the responses generated by the 5 personalized models
indicates that the proposed two-phase approach achieves good results on
modeling the responding style of human and generating personalized responses
for the conversational systems.

negative:
In this paper, we present a real-world conversational AI system to search for
and book hotels through text messaging. Our architecture consists of a
frame-based dialogue management system, which calls machine learning models for
intent classification, named entity recognition, and information retrieval
subtasks. Our chatbot has been deployed on a commercial scale, handling tens of
thousands of hotel searches every day. We describe the various opportunities
and challenges of developing a chatbot in the travel industry.

negative:
Interview chatbots engage users in a text-based conversation to draw out
their views and opinions. It is, however, challenging to build effective
interview chatbots that can handle user free-text responses to open-ended
questions and deliver engaging user experience. As the first step, we are
investigating the feasibility and effectiveness of using publicly available,
practical AI technologies to build effective interview chatbots. To demonstrate
feasibility, we built a prototype scoped to enable interview chatbots with a
subset of active listening skills - the abilities to comprehend a user's input
and respond properly. To evaluate the effectiveness of our prototype, we
compared the performance of interview chatbots with or without active listening
skills on four common interview topics in a live evaluation with 206 users. Our
work presents practical design implications for building effective interview
chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview
tasks.

negative:
We present TruthBot, an all-in-one multilingual conversational chatbot
designed for seeking truth (trustworthy and verified information) on specific
topics. It helps users to obtain information specific to certain topics,
fact-check information, and get recent news. The chatbot learns the intent of a
query by training a deep neural network from the data of the previous intents
and responds appropriately when it classifies the intent in one of the classes
above. Each class is implemented as a separate module that uses either its own
curated knowledge-base or searches the web to obtain the correct information.
The topic of the chatbot is currently set to COVID-19. However, the bot can be
easily customized to any topic-specific responses. Our experimental results
show that each module performs significantly better than its closest
competitor, which is verified both quantitatively and through several
user-based surveys in multiple languages. TruthBot has been deployed in June
2020 and is currently running.

negative:
Creating open-domain chatbots requires large amounts of conversational data
and related benchmark tasks to evaluate them. Standardized evaluation tasks are
crucial for creating automatic evaluation metrics for model development;
otherwise, comparing the models would require resource-expensive human
evaluation. While chatbot challenges have recently managed to provide a
plethora of such resources for English, resources in other languages are not
yet available. In this work, we provide a starting point for Finnish
open-domain chatbot research. We describe our collection efforts to create the
Finnish chat conversation corpus FinChat, which is made available publicly.
FinChat includes unscripted conversations on seven topics from people of
different ages. Using this corpus, we also construct a retrieval-based
evaluation task for Finnish chatbot development. We observe that off-the-shelf
chatbot models trained on conversational corpora do not perform better than
chance at choosing the right answer based on automatic metrics, while humans
can do the same task almost perfectly. Similarly, in a human evaluation,
responses to questions from the evaluation set generated by the chatbots are
predominantly marked as incoherent. Thus, FinChat provides a challenging
evaluation set, meant to encourage chatbot development in Finnish.

negative:
Conversational systems typically focus on functional tasks such as scheduling
appointments or creating todo lists. Instead we design and evaluate SlugBot
(SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support
casual open-domain social inter-action. This novel application requires both
broad topic coverage and engaging interactive skills. We developed a new
technical approach to meet this demanding situation by crowd-sourcing novel
content and introducing playful conversational strategies based on storytelling
and games. We collected over 10,000 conversations during August 2018 as part of
the Alexa Prize competition. We also conducted an in-lab follow-up qualitative
evaluation. Over-all users found SB moderately engaging; conversations averaged
3.6 minutes and involved 26 user turns. However, users reacted very differently
to different conversation subtypes. Storytelling and games were evaluated
positively; these were seen as entertaining with predictable interactive
structure. They also led users to impute personality and intelligence to SB. In
contrast, search and general Chit-Chat induced coverage problems; here users
found it hard to infer what topics SB could understand, with these
conversations seen as being too system-driven. Theoretical and design
implications suggest a move away from conversational systems that simply
provide factual information. Future systems should be designed to have their
own opinions with personal stories to share, and SB provides an example of how
we might achieve this.

negative:
Data scarcity is a long-standing and crucial challenge that hinders quick
development of task-oriented dialogue systems across multiple domains:
task-oriented dialogue models are expected to learn grammar, syntax, dialogue
reasoning, decision making, and language generation from absurdly small amounts
of task-specific data. In this paper, we demonstrate that recent progress in
language modeling pre-training and transfer learning shows promise to overcome
this problem. We propose a task-oriented dialogue model that operates solely on
text input: it effectively bypasses explicit policy and language generation
modules. Building on top of the TransferTransfo framework (Wolf et al., 2019)
and generative model pre-training (Radford et al., 2019), we validate the
approach on complex multi-domain task-oriented dialogues from the MultiWOZ
dataset. Our automatic and human evaluations show that the proposed model is on
par with a strong task-specific neural baseline. In the long run, our approach
holds promise to mitigate the data scarcity problem, and to support the
construction of more engaging and more eloquent task-oriented conversational
agents.

negative:
An on-device DNN-HMM speech recognition system efficiently works with a
limited vocabulary in the presence of a variety of predictable noise. In such a
case, vocabulary and environment adaptation is highly effective. In this paper,
we propose a novel method of end-to-end (E2E) adaptation, which adjusts not
only an acoustic model (AM) but also a weighted finite-state transducer (WFST).
We convert a pretrained WFST to a trainable neural network and adapt the system
to target environments/vocabulary by E2E joint training with an AM. We
replicate Viterbi decoding with forward--backward neural network computation,
which is similar to recurrent neural networks (RNNs). By pooling output score
sequences, a vocabulary posterior for each utterance is obtained and used for
discriminative loss computation. Experiments using 2--10 hours of
English/Japanese adaptation datasets indicate that the fine-tuning of only
WFSTs and that of only AMs are both comparable to a state-of-the-art adaptation
method, and E2E joint training of the two components achieves the best
recognition performance. We also adapt each language system to the other
language using the adaptation data, and the results show that the proposed
method also works well for language adaptations.

negative:
We investigate the effect of different model architectures, training
objectives, hyperparameter settings and decoding procedures on the diversity of
automatically generated image captions. Our results show that 1) simple
decoding by naive sampling, coupled with low temperature is a competitive and
fast method to produce diverse and accurate caption sets; 2) training with
CIDEr-based reward using Reinforcement learning harms the diversity properties
of the resulting generator, which cannot be mitigated by manipulating decoding
parameters. In addition, we propose a new metric AllSPICE for evaluating both
accuracy and diversity of a set of captions by a single value.

negative:
This paper introduces a document grounded dataset for text conversations. We
define "Document Grounded Conversations" as conversations that are about the
contents of a specified document. In this dataset the specified documents were
Wikipedia articles about popular movies. The dataset contains 4112
conversations with an average of 21.43 turns per conversation. This positions
this dataset to not only provide a relevant chat history while generating
responses but also provide a source of information that the models could use.
We describe two neural architectures that provide benchmark performance on the
task of generating the next response. We also evaluate our models for
engagement and fluency, and find that the information from the document helps
in generating more engaging and fluent responses.

negative:
Customer service chatbots are conversational systems designed to provide
information to customers about products/services offered by different
companies. Particularly, intent recognition is one of the core components in
the natural language understating capabilities of a chatbot system. Among the
different intents that a chatbot is trained to recognize, there is a set of
them that is universal to any customer service chatbot. Universal intents may
include salutation, switch the conversation to a human agent, farewells, among
others. A system to recognize those universal intents will be very helpful to
optimize the training process of specific customer service chatbots. We propose
the development of a universal intent recognition system, which is trained to
recognize a selected group of 11 intents that are common in 28 different
chatbots. The proposed system is trained considering state-of-the-art
word-embedding models such as word2vec and BERT, and deep classifiers based on
convolutional and recurrent neural networks. The proposed model is able to
discriminate between those universal intents with a balanced accuracy up to
80.4\%. In addition, the proposed system is equally accurate to recognize
intents expressed both in short and long text requests. At the same time,
misclassification errors often occurs between intents with very similar
semantic fields such as farewells and positive comments. The proposed system
will be very helpful to optimize the training process of a customer service
chatbot because some of the intents will be already available and detected by
our system. At the same time, the proposed approach will be a suitable base
model to train more specific chatbots by applying transfer learning strategies.

negative:
Smart systems for Universities powered by Artificial Intelligence have been
massively developed to help humans in various tasks. The chatbot concept is not
something new in today society which is developing with recent technology.
College students or candidates of college students often need actual
information like asking for something to customer service, especially during
this pandemic, when it is difficult to have an immediate face-to-face meeting.
Chatbots are functionally helping in several things such as curriculum
information, admission for new students, schedule info for any lecture courses,
students grade information, and some adding features for Muslim worships
schedule, also weather forecast information. This Chatbot is developed by Deep
Learning models, which was adopted by an artificial intelligence model that
replicates human intelligence with some specific training schemes. This kind of
Deep Learning is based on RNN which has some specific memory savings scheme for
the Deep Learning Model, specifically this chatbot using LSTM which already
integrates by RASA framework. LSTM is also known as Long Short Term Memory
which efficiently saves some required memory but will remove some memory that
is not needed. This Chatbot uses the FB platform because of the FB users have
already reached up to 60.8% of its entire population in Indonesia. Here's the
chatbot only focuses on case studies at campus of the Magister Informatics FTI
University of Islamic Indonesia. This research is a first stage development
within fairly sufficient simulate data.

negative:
The lack of time-efficient and reliable evaluation methods hamper the
development of conversational dialogue systems (chatbots). Evaluations
requiring humans to converse with chatbots are time and cost-intensive, put
high cognitive demands on the human judges, and yield low-quality results. In
this work, we introduce \emph{Spot The Bot}, a cost-efficient and robust
evaluation framework that replaces human-bot conversations with conversations
between bots. Human judges then only annotate for each entity in a conversation
whether they think it is human or not (assuming there are humans participants
in these conversations). These annotations then allow us to rank chatbots
regarding their ability to mimic the conversational behavior of humans. Since
we expect that all bots are eventually recognized as such, we incorporate a
metric that measures which chatbot can uphold human-like behavior the longest,
i.e., \emph{Survival Analysis}. This metric has the ability to correlate a
bot's performance to certain of its characteristics (e.g., \ fluency or
sensibleness), yielding interpretable results. The comparably low cost of our
framework allows for frequent evaluations of chatbots during their evaluation
cycle. We empirically validate our claims by applying \emph{Spot The Bot} to
three domains, evaluating several state-of-the-art chatbots, and drawing
comparisons to related work. The framework is released as a ready-to-use tool.

negative:
In this work we explore a deep learning-based dialogue system that generates
sarcastic and humorous responses from a conversation design perspective. We
trained a seq2seq model on a carefully curated dataset of 3000
question-answering pairs, the core of our mean, grumpy, sarcastic chatbot. We
show that end-to-end systems learn patterns very quickly from small datasets
and thus, are able to transfer simple linguistic structures representing
abstract concepts to unseen settings. We also deploy our LSTM-based
encoder-decoder model in the browser, where users can directly interact with
the chatbot. Human raters evaluated linguistic quality, creativity and
human-like traits, revealing the system's strengths, limitations and potential
for future research.

negative:
As major progress is made in open-ended text generation, measuring how close
machine-generated text is to human language remains a critical open problem. We
introduce MAUVE, a comparison measure for open-ended text generation, which
directly compares the learnt distribution from a text generation model to the
distribution of human-written text using divergence frontiers. MAUVE scales up
to modern text generation models by computing information divergences in a
quantized embedding space. Through an extensive empirical study on three
open-ended generation tasks, we find that MAUVE identifies known properties of
generated text, scales naturally with model size, and correlates with human
judgments, with fewer restrictions than existing distributional evaluation
metrics.

negative:
We investigate neural techniques for end-to-end computational argumentation
mining (AM). We frame AM both as a token-based dependency parsing and as a
token-based sequence tagging problem, including a multi-task learning setup.
Contrary to models that operate on the argument component level, we find that
framing AM as dependency parsing leads to subpar performance results. In
contrast, less complex (local) tagging models based on BiLSTMs perform robustly
across classification scenarios, being able to catch long-range dependencies
inherent to the AM problem. Moreover, we find that jointly learning 'natural'
subtasks, in a multi-task learning setup, improves performance.

negative:
We propose a new method to detect when users express the intent to leave a
service, also known as churn. While previous work focuses solely on social
media, we show that this intent can be detected in chatbot conversations. As
companies increasingly rely on chatbots they need an overview of potentially
churny users. To this end, we crowdsource and publish a dataset of churn intent
expressions in chatbot interactions in German and English. We show that
classifiers trained on social media data can detect the same intent in the
context of chatbots.
  We introduce a classification architecture that outperforms existing work on
churn intent detection in social media. Moreover, we show that, using bilingual
word embeddings, a system trained on combined English and German data
outperforms monolingual approaches. As the only existing dataset is in English,
we crowdsource and publish a novel dataset of German tweets. We thus underline
the universal aspect of the problem, as examples of churn intent in English
help us identify churn in German tweets and chatbot conversations.

negative:
For words, rank-frequency distributions have long been heralded for adherence
to a potentially-universal phenomenon known as Zipf's law. The hypothetical
form of this empirical phenomenon was refined by Ben\^{i}ot Mandelbrot to that
which is presently referred to as the Zipf-Mandelbrot law. Parallel to this,
Herbet Simon proposed a selection model potentially explaining Zipf's law.
However, a significant dispute between Simon and Mandelbrot, notable empirical
exceptions, and the lack of a strong empirical connection between Simon's model
and the Zipf-Mandelbrot law have left the questions of universality and
mechanistic generation open. We offer a resolution to these issues by
exhibiting how the dark matter of word segmentation, i.e., space, punctuation,
etc., connect the Zipf-Mandelbrot law to Simon's mechanistic process. This
explains Mandelbrot's refinement as no more than a fudge factor, accommodating
the effects of the exclusion of the rank-frequency dark matter. Thus,
integrating these non-word objects resolves a more-generalized rank-frequency
law. Since this relies upon the integration of space, etc., we find support for
the hypothesis that $all$ are generated by common processes, indicating from a
physical perspective that space is a word, too.

negative:
Multilingual speakers tend to alternate between languages within a
conversation, a phenomenon referred to as "code-switching" (CS). CS is a
complex phenomenon that not only encompasses linguistic challenges, but also
contains a great deal of complexity in terms of its dynamic behaviour across
speakers. This dynamic behaviour has been studied by sociologists and
psychologists, identifying factors affecting CS. In this paper, we provide an
empirical user study on Arabic-English CS, where we show the correlation
between users' CS frequency and character traits. We use machine learning (ML)
to validate the findings, informing and confirming existing theories. The
predictive models were able to predict users' CS frequency with an accuracy
higher than 55%, where travel experiences and personality traits played the
biggest role in the modeling process.

negative:
Question: I have five fingers but I am not alive. What am I? Answer: a glove.
Answering such a riddle-style question is a challenging cognitive process, in
that it requires complex commonsense reasoning abilities, an understanding of
figurative language, and counterfactual reasoning skills, which are all
important abilities for advanced natural language understanding (NLU). However,
there are currently no dedicated datasets aiming to test these abilities.
Herein, we present RiddleSense, a new multiple-choice question answering task,
which comes with the first large dataset (5.7k examples) for answering
riddle-style commonsense questions. We systematically evaluate a wide range of
models over the challenge, and point out that there is a large gap between the
best-supervised model and human performance -- suggesting intriguing future
research in the direction of higher-order commonsense reasoning and linguistic
creativity towards building advanced NLU systems.

negative:
In this paper we present an approach for estimating the quality of machine
translation system. There are various methods for estimating the quality of
output sentences, but in this paper we focus on Na\"ive Bayes classifier to
build model using features which are extracted from the input sentences. These
features are used for finding the likelihood of each of the sentences of the
training data which are then further used for determining the scores of the
test data. On the basis of these scores we determine the class labels of the
test data.

negative:
This paper proposes a novel pipeline for automatic grammar augmentation that
provides a significant improvement in the voice command recognition accuracy
for systems with small footprint acoustic model (AM). The improvement is
achieved by augmenting the user-defined voice command set, also called grammar
set, with alternate grammar expressions. For a given grammar set, a set of
potential grammar expressions (candidate set) for augmentation is constructed
from an AM-specific statistical pronunciation dictionary that captures the
consistent patterns and errors in the decoding of AM induced by variations in
pronunciation, pitch, tempo, accent, ambiguous spellings, and noise conditions.
Using this candidate set, greedy optimization based and cross-entropy-method
(CEM) based algorithms are considered to search for an augmented grammar set
with improved recognition accuracy utilizing a command-specific dataset. Our
experiments show that the proposed pipeline along with algorithms considered in
this paper significantly reduce the mis-detection and mis-classification rate
without increasing the false-alarm rate. Experiments also demonstrate the
consistent superior performance of CEM method over greedy-based algorithms.

negative:
Emotion stimulus extraction is a fine-grained subtask of emotion analysis
that focuses on identifying the description of the cause behind an emotion
expression from a text passage (e.g., in the sentence "I am happy that I passed
my exam" the phrase "passed my exam" corresponds to the stimulus.). Previous
work mainly focused on Mandarin and English, with no resources or models for
German. We fill this research gap by developing a corpus of 2006 German news
headlines annotated with emotions and 811 instances with annotations of
stimulus phrases. Given that such corpus creation efforts are time-consuming
and expensive, we additionally work on an approach for projecting the existing
English GoodNewsEveryone (GNE) corpus to a machine-translated German version.
We compare the performance of a conditional random field (CRF) model (trained
monolingually on German and cross-lingually via projection) with a multilingual
XLM-RoBERTa (XLM-R) model. Our results show that training with the German
corpus achieves higher F1 scores than projection. Experiments with XLM-R
outperform their respective CRF counterparts.

negative:
Argumentation mining (AM) requires the identification of complex discourse
structures and has lately been applied with success monolingually. In this
work, we show that the existing resources are, however, not adequate for
assessing cross-lingual AM, due to their heterogeneity or lack of complexity.
We therefore create suitable parallel corpora by (human and machine)
translating a popular AM dataset consisting of persuasive student essays into
German, French, Spanish, and Chinese. We then compare (i) annotation projection
and (ii) bilingual word embeddings based direct transfer strategies for
cross-lingual AM, finding that the former performs considerably better and
almost eliminates the loss from cross-lingual transfer. Moreover, we find that
annotation projection works equally well when using either costly human or
cheap machine translations. Our code and data are available at
\url{http://github.com/UKPLab/coling2018-xling_argument_mining}.

negative:
We investigate whether and where multi-task learning (MTL) can improve
performance on NLP problems related to argumentation mining (AM), in particular
argument component identification. Our results show that MTL performs
particularly well (and better than single-task learning) when little training
data is available for the main task, a common scenario in AM. Our findings
challenge previous assumptions that conceptualizations across AM datasets are
divergent and that MTL is difficult for semantic or higher-level tasks.