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When does MAML Work the Best? An Empirical Study on Model-Agnostic
Meta-Learning in NLP Applications | Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method,
is successfully employed in NLP applications including few-shot text
classification and multi-domain low-resource language generation. Many
impacting factors, including data quantity, similarity among tasks, and the
balance between general language model and task-specific adaptation, can affect
the performance of MAML in NLP, but few works have thoroughly studied them. In
this paper, we conduct an empirical study to investigate these impacting
factors and conclude when MAML works the best based on the experimental
results.
| 2,020 | Computation and Language |
A Novel Distributed Representation of News (DRNews) for Stock Market
Predictions | In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.
| 2,022 | Computation and Language |
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning | A chatbot that converses like a human should be goal-oriented (i.e., be
purposeful in conversation), which is beyond language generation. However,
existing dialogue systems often heavily rely on cumbersome hand-crafted rules
or costly labelled datasets to reach the goals. In this paper, we propose
Goal-oriented Chatbots (GoChat), a framework for end-to-end training chatbots
to maximize the longterm return from offline multi-turn dialogue datasets. Our
framework utilizes hierarchical reinforcement learning (HRL), where the
high-level policy guides the conversation towards the final goal by determining
some sub-goals, and the low-level policy fulfills the sub-goals by generating
the corresponding utterance for response. In our experiments on a real-world
dialogue dataset for anti-fraud in financial, our approach outperforms previous
methods on both the quality of response generation as well as the success rate
of accomplishing the goal.
| 2,020 | Computation and Language |
Adversarial NLI for Factual Correctness in Text Summarisation Models | We apply the Adversarial NLI dataset to train the NLI model and show that the
model has the potential to enhance factual correctness in abstract
summarization. We follow the work of Falke et al. (2019), which rank multiple
generated summaries based on the entailment probabilities between an source
document and summaries and select the summary that has the highest entailment
probability. The authors' earlier study concluded that current NLI models are
not sufficiently accurate for the ranking task. We show that the Transformer
models fine-tuned on the new dataset achieve significantly higher accuracy and
have the potential of selecting a coherent summary.
| 2,020 | Computation and Language |
KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for
Comprehension And Generation | This paper presents our strategies in SemEval 2020 Task 4: Commonsense
Validation and Explanation. We propose a novel way to search for evidence and
choose the different large-scale pre-trained models as the backbone for three
subtasks. The results show that our evidence-searching approach improves model
performance on commonsense explanation task. Our team ranks 2nd in subtask C
according to human evaluation score.
| 2,020 | Computation and Language |
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge
Injection into Pretrained Transformers | Following the major success of neural language models (LMs) such as BERT or
GPT-2 on a variety of language understanding tasks, recent work focused on
injecting (structured) knowledge from external resources into these models.
While on the one hand, joint pretraining (i.e., training from scratch, adding
objectives based on external knowledge to the primary LM objective) may be
prohibitively computationally expensive, post-hoc fine-tuning on external
knowledge, on the other hand, may lead to the catastrophic forgetting of
distributional knowledge. In this work, we investigate models for complementing
the distributional knowledge of BERT with conceptual knowledge from ConceptNet
and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using
adapter training. While overall results on the GLUE benchmark paint an
inconclusive picture, a deeper analysis reveals that our adapter-based models
substantially outperform BERT (up to 15-20 performance points) on inference
tasks that require the type of conceptual knowledge explicitly present in
ConceptNet and OMCS. All code and experiments are open sourced under:
https://github.com/wluper/retrograph .
| 2,020 | Computation and Language |
How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using
Speech Generation and Deep Learning | Searching for information about a specific person is an online activity
frequently performed by many users. In most cases, users are aided by queries
containing a name and sending back to the web search engines for finding their
will. Typically, Web search engines provide just a few accurate results
associated with a name-containing query. Currently, most solutions for
suggesting synonyms in online search are based on pattern matching and phonetic
encoding, however very often, the performance of such solutions is less than
optimal. In this paper, we propose SpokenName2Vec, a novel and generic approach
which addresses the similar name suggestion problem by utilizing automated
speech generation, and deep learning to produce spoken name embeddings. This
sophisticated and innovative embeddings captures the way people pronounce names
in any language and accent. Utilizing the name pronunciation can be helpful for
both differentiating and detecting names that sound alike, but are written
differently. The proposed approach was demonstrated on a large-scale dataset
consisting of 250,000 forenames and evaluated using a machine learning
classifier and 7,399 names with their verified synonyms. The performance of the
proposed approach was found to be superior to 10 other algorithms evaluated in
this study, including well used phonetic and string similarity algorithms, and
two recently proposed algorithms. The results obtained suggest that the
proposed approach could serve as a useful and valuable tool for solving the
similar name suggestion problem.
| 2,020 | Computation and Language |
Stronger Baselines for Grammatical Error Correction Using Pretrained
Encoder-Decoder Model | Studies on grammatical error correction (GEC) have reported the effectiveness
of pretraining a Seq2Seq model with a large amount of pseudodata. However, this
approach requires time-consuming pretraining for GEC because of the size of the
pseudodata. In this study, we explore the utility of bidirectional and
auto-regressive transformers (BART) as a generic pretrained encoder-decoder
model for GEC. With the use of this generic pretrained model for GEC, the
time-consuming pretraining can be eliminated. We find that monolingual and
multilingual BART models achieve high performance in GEC, with one of the
results being comparable to the current strong results in English GEC. Our
implementations are publicly available at GitHub
(https://github.com/Katsumata420/generic-pretrained-GEC).
| 2,020 | Computation and Language |
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation
Challenge Tasks at IWSLT 2020 | This paper describes the ON-TRAC Consortium translation systems developed for
two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline
speech translation and simultaneous speech translation. ON-TRAC Consortium is
composed of researchers from three French academic laboratories: LIA (Avignon
Universit\'e), LIG (Universit\'e Grenoble Alpes), and LIUM (Le Mans
Universit\'e). Attention-based encoder-decoder models, trained end-to-end, were
used for our submissions to the offline speech translation track. Our
contributions focused on data augmentation and ensembling of multiple models.
In the simultaneous speech translation track, we build on Transformer-based
wait-k models for the text-to-text subtask. For speech-to-text simultaneous
translation, we attach a wait-k MT system to a hybrid ASR system. We propose an
algorithm to control the latency of the ASR+MT cascade and achieve a good
latency-quality trade-off on both subtasks.
| 2,020 | Computation and Language |
Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other
Affectual States from Text | Recent advances in machine learning have led to computer systems that are
human-like in behaviour. Sentiment analysis, the automatic determination of
emotions in text, is allowing us to capitalize on substantial previously
unattainable opportunities in commerce, public health, government policy,
social sciences, and art. Further, analysis of emotions in text, from news to
social media posts, is improving our understanding of not just how people
convey emotions through language but also how emotions shape our behaviour.
This article presents a sweeping overview of sentiment analysis research that
includes: the origins of the field, the rich landscape of tasks, challenges, a
survey of the methods and resources used, and applications. We also discuss
discuss how, without careful fore-thought, sentiment analysis has the potential
for harmful outcomes. We outline the latest lines of research in pursuit of
fairness in sentiment analysis.
| 2,021 | Computation and Language |
Deep Learning Models for Automatic Summarization | Text summarization is an NLP task which aims to convert a textual document
into a shorter one while keeping as much meaning as possible. This pedagogical
article reviews a number of recent Deep Learning architectures that have helped
to advance research in this field. We will discuss in particular applications
of pointer networks, hierarchical Transformers and Reinforcement Learning. We
assume basic knowledge of Seq2Seq architecture and Transformer networks within
NLP.
| 2,020 | Computation and Language |
Pointwise Paraphrase Appraisal is Potentially Problematic | The prevailing approach for training and evaluating paraphrase identification
models is constructed as a binary classification problem: the model is given a
pair of sentences, and is judged by how accurately it classifies pairs as
either paraphrases or non-paraphrases. This pointwise-based evaluation method
does not match well the objective of most real world applications, so the goal
of our work is to understand how models which perform well under pointwise
evaluation may fail in practice and find better methods for evaluating
paraphrase identification models. As a first step towards that goal, we show
that although the standard way of fine-tuning BERT for paraphrase
identification by pairing two sentences as one sequence results in a model with
state-of-the-art performance, that model may perform poorly on simple tasks
like identifying pairs with two identical sentences. Moreover, we show that
these models may even predict a pair of randomly-selected sentences with higher
paraphrase score than a pair of identical ones.
| 2,020 | Computation and Language |
Knowledge Graph Simple Question Answering for Unseen Domains | Knowledge graph simple question answering (KGSQA), in its standard form, does
not take into account that human-curated question answering training data only
cover a small subset of the relations that exist in a Knowledge Graph (KG), or
even worse, that new domains covering unseen and rather different to existing
domains relations are added to the KG. In this work, we study KGSQA in a
previously unstudied setting where new, unseen domains are added during test
time. In this setting, question-answer pairs of the new domain do not appear
during training, thus making the task more challenging. We propose a
data-centric domain adaptation framework that consists of a KGSQA system that
is applicable to new domains, and a sequence to sequence question generation
method that automatically generates question-answer pairs for the new domain.
Since the effectiveness of question generation for KGSQA can be restricted by
the limited lexical variety of the generated questions, we use distant
supervision to extract a set of keywords that express each relation of the
unseen domain and incorporate those in the question generation method.
Experimental results demonstrate that our framework significantly improves over
zero-shot baselines and is robust across domains.
| 2,020 | Computation and Language |
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach
to Grade Essays Using Gaze Behaviour | The gaze behaviour of a reader is helpful in solving several NLP tasks such
as automatic essay grading. However, collecting gaze behaviour from readers is
costly in terms of time and money. In this paper, we propose a way to improve
automatic essay grading using gaze behaviour, which is learnt at run time using
a multi-task learning framework. To demonstrate the efficacy of this multi-task
learning based approach to automatic essay grading, we collect gaze behaviour
for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the
essays, numbering over 7000 essays. Using the learnt gaze behaviour, we can
achieve a statistically significant improvement in performance over the
state-of-the-art system for the essay sets where we have gaze data. We also
achieve a statistically significant improvement for 4 other essay sets,
numbering about 6000 essays, where we have no gaze behaviour data available.
Our approach establishes that learning gaze behaviour improves automatic essay
grading.
| 2,021 | Computation and Language |
Stable Style Transformer: Delete and Generate Approach with
Encoder-Decoder for Text Style Transfer | Text style transfer is the task that generates a sentence by preserving the
content of the input sentence and transferring the style. Most existing studies
are progressing on non-parallel datasets because parallel datasets are limited
and hard to construct. In this work, we introduce a method that follows two
stages in non-parallel datasets. The first stage is to delete attribute markers
of a sentence directly through a classifier. The second stage is to generate a
transferred sentence by combining the content tokens and the target style. We
experiment on two benchmark datasets and evaluate context, style, fluency, and
semantic. It is difficult to select the best system using only these automatic
metrics, but it is possible to select stable systems. We consider only robust
systems in all automatic evaluation metrics to be the minimum conditions that
can be used in real applications. Many previous systems are difficult to use in
certain situations because performance is significantly lower in several
evaluation metrics. However, our system is stable in all automatic evaluation
metrics and has results comparable to other models. Also, we compare the
performance results of our system and the unstable system through human
evaluation. Our code and data are available at the link
(https://github.com/rungjoo/Stable-Style-Transformer).
| 2,020 | Computation and Language |
K{\o}psala: Transition-Based Graph Parsing via Efficient Training and
Effective Encoding | We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced
Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline
consisting of off-the-shelf models for everything but enhanced graph parsing,
and for the latter, a transition-based graph parser adapted from Che et al.
(2019). We train a single enhanced parser model per language, using gold
sentence splitting and tokenization for training, and rely only on tokenized
surface forms and multilingual BERT for encoding. While a bug introduced just
before submission resulted in a severe drop in precision, its post-submission
fix would bring us to 4th place in the official ranking, according to average
ELAS. Our parser demonstrates that a unified pipeline is effective for both
Meaning Representation Parsing and Enhanced Universal Dependencies.
| 2,020 | Computation and Language |
NILE : Natural Language Inference with Faithful Natural Language
Explanations | The recent growth in the popularity and success of deep learning models on
NLP classification tasks has accompanied the need for generating some form of
natural language explanation of the predicted labels. Such generated natural
language (NL) explanations are expected to be faithful, i.e., they should
correlate well with the model's internal decision making. In this work, we
focus on the task of natural language inference (NLI) and address the following
question: can we build NLI systems which produce labels with high accuracy,
while also generating faithful explanations of its decisions? We propose
Natural-language Inference over Label-specific Explanations (NILE), a novel NLI
method which utilizes auto-generated label-specific NL explanations to produce
labels along with its faithful explanation. We demonstrate NILE's effectiveness
over previously reported methods through automated and human evaluation of the
produced labels and explanations. Our evaluation of NILE also supports the
claim that accurate systems capable of providing testable explanations of their
decisions can be designed. We discuss the faithfulness of NILE's explanations
in terms of sensitivity of the decisions to the corresponding explanations. We
argue that explicit evaluation of faithfulness, in addition to label and
explanation accuracy, is an important step in evaluating model's explanations.
Further, we demonstrate that task-specific probes are necessary to establish
such sensitivity.
| 2,020 | Computation and Language |
An Audio-enriched BERT-based Framework for Spoken Multiple-choice
Question Answering | In a spoken multiple-choice question answering (SMCQA) task, given a passage,
a question, and multiple choices all in the form of speech, the machine needs
to pick the correct choice to answer the question. While the audio could
contain useful cues for SMCQA, usually only the auto-transcribed text is
utilized in system development. Thanks to the large-scaled pre-trained language
representation models, such as the bidirectional encoder representations from
transformers (BERT), systems with only auto-transcribed text can still achieve
a certain level of performance. However, previous studies have evidenced that
acoustic-level statistics can offset text inaccuracies caused by the automatic
speech recognition systems or representation inadequacy lurking in word
embedding generators, thereby making the SMCQA system robust. Along the line of
research, this study concentrates on designing a BERT-based SMCQA framework,
which not only inherits the advantages of contextualized language
representations learned by BERT, but integrates the complementary
acoustic-level information distilled from audio with the text-level
information. Consequently, an audio-enriched BERT-based SMCQA framework is
proposed. A series of experiments demonstrates remarkable improvements in
accuracy over selected baselines and SOTA systems on a published Chinese SMCQA
dataset.
| 2,020 | Computation and Language |
Adapting End-to-End Speech Recognition for Readable Subtitles | Automatic speech recognition (ASR) systems are primarily evaluated on
transcription accuracy. However, in some use cases such as subtitling, verbatim
transcription would reduce output readability given limited screen size and
reading time. Therefore, this work focuses on ASR with output compression, a
task challenging for supervised approaches due to the scarcity of training
data. We first investigate a cascaded system, where an unsupervised compression
model is used to post-edit the transcribed speech. We then compare several
methods of end-to-end speech recognition under output length constraints. The
experiments show that with limited data far less than needed for training a
model from scratch, we can adapt a Transformer-based ASR model to incorporate
both transcription and compression capabilities. Furthermore, the best
performance in terms of WER and ROUGE scores is achieved by explicitly modeling
the length constraints within the end-to-end ASR system.
| 2,020 | Computation and Language |
AMR Quality Rating with a Lightweight CNN | Structured semantic sentence representations such as Abstract Meaning
Representations (AMRs) are potentially useful in various NLP tasks. However,
the quality of automatic parses can vary greatly and jeopardizes their
usefulness. This can be mitigated by models that can accurately rate AMR
quality in the absence of costly gold data, allowing us to inform downstream
systems about an incorporated parse's trustworthiness or select among different
candidate parses.
In this work, we propose to transfer the AMR graph to the domain of images.
This allows us to create a simple convolutional neural network (CNN) that
imitates a human judge tasked with rating graph quality. Our experiments show
that the method can rate quality more accurately than strong baselines, in
several quality dimensions. Moreover, the method proves to be efficient and
reduces the incurred energy consumption.
| 2,020 | Computation and Language |
A review of sentiment analysis research in Arabic language | Sentiment analysis is a task of natural language processing which has
recently attracted increasing attention. However, sentiment analysis research
has mainly been carried out for the English language. Although Arabic is
ramping up as one of the most used languages on the Internet, only a few
studies have focused on Arabic sentiment analysis so far. In this paper, we
carry out an in-depth qualitative study of the most important research works in
this context by presenting limits and strengths of existing approaches. In
particular, we survey both approaches that leverage machine translation or
transfer learning to adapt English resources to Arabic and approaches that stem
directly from the Arabic language.
| 2,020 | Computation and Language |
Demoting Racial Bias in Hate Speech Detection | In current hate speech datasets, there exists a high correlation between
annotators' perceptions of toxicity and signals of African American English
(AAE). This bias in annotated training data and the tendency of machine
learning models to amplify it cause AAE text to often be mislabeled as
abusive/offensive/hate speech with a high false positive rate by current hate
speech classifiers. In this paper, we use adversarial training to mitigate this
bias, introducing a hate speech classifier that learns to detect toxic
sentences while demoting confounds corresponding to AAE texts. Experimental
results on a hate speech dataset and an AAE dataset suggest that our method is
able to substantially reduce the false positive rate for AAE text while only
minimally affecting the performance of hate speech classification.
| 2,020 | Computation and Language |
FT Speech: Danish Parliament Speech Corpus | This paper introduces FT Speech, a new speech corpus created from the
recorded meetings of the Danish Parliament, otherwise known as the Folketing
(FT). The corpus contains over 1,800 hours of transcribed speech by a total of
434 speakers. It is significantly larger in duration, vocabulary, and amount of
spontaneous speech than the existing public speech corpora for Danish, which
are largely limited to read-aloud and dictation data. We outline design
considerations, including the preprocessing methods and the alignment
procedure. To evaluate the quality of the corpus, we train automatic speech
recognition systems on the new resource and compare them to the systems trained
on the Danish part of Spr\r{a}kbanken, the largest public ASR corpus for Danish
to date. Our baseline results show that we achieve a 14.01 WER on the new
corpus. A combination of FT Speech with in-domain language data provides
comparable results to models trained specifically on Spr\r{a}kbanken, showing
that FT Speech transfers well to this data set. Interestingly, our results
demonstrate that the opposite is not the case. This shows that FT Speech
provides a valuable resource for promoting research on Danish ASR with more
spontaneous speech.
| 2,020 | Computation and Language |
The Unreasonable Volatility of Neural Machine Translation Models | Recent works have shown that Neural Machine Translation (NMT) models achieve
impressive performance, however, questions about understanding the behavior of
these models remain unanswered. We investigate the unexpected volatility of NMT
models where the input is semantically and syntactically correct. We discover
that with trivial modifications of source sentences, we can identify cases
where \textit{unexpected changes} happen in the translation and in the worst
case lead to mistranslations. This volatile behavior of translating extremely
similar sentences in surprisingly different ways highlights the underlying
generalization problem of current NMT models. We find that both RNN and
Transformer models display volatile behavior in 26% and 19% of sentence
variations, respectively.
| 2,020 | Computation and Language |
The IMS-CUBoulder System for the SIGMORPHON 2020 Shared Task on
Unsupervised Morphological Paradigm Completion | In this paper, we present the systems of the University of Stuttgart IMS and
the University of Colorado Boulder (IMS-CUBoulder) for SIGMORPHON 2020 Task 2
on unsupervised morphological paradigm completion (Kann et al., 2020). The task
consists of generating the morphological paradigms of a set of lemmas, given
only the lemmas themselves and unlabeled text. Our proposed system is a
modified version of the baseline introduced together with the task. In
particular, we experiment with substituting the inflection generation component
with an LSTM sequence-to-sequence model and an LSTM pointer-generator network.
Our pointer-generator system obtains the best score of all seven submitted
systems on average over all languages, and outperforms the official baseline,
which was best overall, on Bulgarian and Kannada.
| 2,020 | Computation and Language |
MaintNet: A Collaborative Open-Source Library for Predictive Maintenance
Language Resources | Maintenance record logbooks are an emerging text type in NLP. They typically
consist of free text documents with many domain specific technical terms,
abbreviations, as well as non-standard spelling and grammar, which poses
difficulties to NLP pipelines trained on standard corpora. Analyzing and
annotating such documents is of particular importance in the development of
predictive maintenance systems, which aim to provide operational efficiencies,
prevent accidents and save lives. In order to facilitate and encourage research
in this area, we have developed MaintNet, a collaborative open-source library
of technical and domain-specific language datasets. MaintNet provides novel
logbook data from the aviation, automotive, and facilities domains along with
tools to aid in their (pre-)processing and clustering. Furthermore, it provides
a way to encourage discussion on and sharing of new datasets and tools for
logbook data analysis.
| 2,020 | Computation and Language |
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational
Machine Reading | The goal of conversational machine reading is to answer user questions given
a knowledge base text which may require asking clarification questions.
Existing approaches are limited in their decision making due to struggles in
extracting question-related rules and reasoning about them. In this paper, we
present a new framework of conversational machine reading that comprises a
novel Explicit Memory Tracker (EMT) to track whether conditions listed in the
rule text have already been satisfied to make a decision. Moreover, our
framework generates clarification questions by adopting a coarse-to-fine
reasoning strategy, utilizing sentence-level entailment scores to weight
token-level distributions. On the ShARC benchmark (blind, held-out) testset,
EMT achieves new state-of-the-art results of 74.6% micro-averaged decision
accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by
visualizing the entailment-oriented reasoning process as the conversation
flows. Code and models are released at
https://github.com/Yifan-Gao/explicit_memory_tracker.
| 2,020 | Computation and Language |
BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection | Toxic comments in online platforms are an unavoidable social issue under the
cloak of anonymity. Hate speech detection has been actively done for languages
such as English, German, or Italian, where manually labeled corpus has been
released. In this work, we first present 9.4K manually labeled entertainment
news comments for identifying Korean toxic speech, collected from a widely used
online news platform in Korea. The comments are annotated regarding social bias
and hate speech since both aspects are correlated. The inter-annotator
agreement Krippendorff's alpha score is 0.492 and 0.496, respectively. We
provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the
highest score on all tasks. The models generally display better performance on
bias identification, since the hate speech detection is a more subjective
issue. Additionally, when BERT is trained with bias label for hate speech
detection, the prediction score increases, implying that bias and hate are
intertwined. We make our dataset publicly available and open competitions with
the corpus and benchmarks.
| 2,020 | Computation and Language |
ParsBERT: Transformer-based Model for Persian Language Understanding | The surge of pre-trained language models has begun a new era in the field of
Natural Language Processing (NLP) by allowing us to build powerful language
models. Among these models, Transformer-based models such as BERT have become
increasingly popular due to their state-of-the-art performance. However, these
models are usually focused on English, leaving other languages to multilingual
models with limited resources. This paper proposes a monolingual BERT for the
Persian language (ParsBERT), which shows its state-of-the-art performance
compared to other architectures and multilingual models. Also, since the amount
of data available for NLP tasks in Persian is very restricted, a massive
dataset for different NLP tasks as well as pre-training the model is composed.
ParsBERT obtains higher scores in all datasets, including existing ones as well
as composed ones and improves the state-of-the-art performance by outperforming
both multilingual BERT and other prior works in Sentiment Analysis, Text
Classification and Named Entity Recognition tasks.
| 2,021 | Computation and Language |
What Are People Asking About COVID-19? A Question Classification Dataset | We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources,
which we annotate into 15 question categories and 207 question clusters. The
most common questions in our dataset asked about transmission, prevention, and
societal effects of COVID, and we found that many questions that appeared in
multiple sources were not answered by any FAQ websites of reputable
organizations such as the CDC and FDA. We post our dataset publicly at
https://github.com/JerryWeiAI/COVID-Q. For classifying questions into 15
categories, a BERT baseline scored 58.1% accuracy when trained on 20 examples
per category, and for a question clustering task, a BERT + triplet loss
baseline achieved 49.5% accuracy. We hope COVID-Q can help either for direct
use in developing applied systems or as a domain-specific resource for model
evaluation.
| 2,023 | Computation and Language |
Guiding Symbolic Natural Language Grammar Induction via
Transformer-Based Sequence Probabilities | A novel approach to automated learning of syntactic rules governing natural
languages is proposed, based on using probabilities assigned to sentences (and
potentially longer word sequences) by transformer neural network language
models to guide symbolic learning processes like clustering and rule induction.
This method exploits the learned linguistic knowledge in transformers, without
any reference to their inner representations; hence, the technique is readily
adaptable to the continuous appearance of more powerful language models. We
show a proof-of-concept example of our proposed technique, using it to guide
unsupervised symbolic link-grammar induction methods drawn from our prior
research.
| 2,020 | Computation and Language |
A Data-driven Approach for Noise Reduction in Distantly Supervised
Biomedical Relation Extraction | Fact triples are a common form of structured knowledge used within the
biomedical domain. As the amount of unstructured scientific texts continues to
grow, manual annotation of these texts for the task of relation extraction
becomes increasingly expensive. Distant supervision offers a viable approach to
combat this by quickly producing large amounts of labeled, but considerably
noisy, data. We aim to reduce such noise by extending an entity-enriched
relation classification BERT model to the problem of multiple instance
learning, and defining a simple data encoding scheme that significantly reduces
noise, reaching state-of-the-art performance for distantly-supervised
biomedical relation extraction. Our approach further encodes knowledge about
the direction of relation triples, allowing for increased focus on relation
learning by reducing noise and alleviating the need for joint learning with
knowledge graph completion.
| 2,020 | Computation and Language |
Verification and Validation of Convex Optimization Algorithms for Model
Predictive Control | Advanced embedded algorithms are growing in complexity and they are an
essential contributor to the growth of autonomy in many areas. However, the
promise held by these algorithms cannot be kept without proper attention to the
considerably stronger design constraints that arise when the applications of
interest, such as aerospace systems, are safety-critical. Formal verification
is the process of proving or disproving the ''correctness'' of an algorithm
with respect to a certain mathematical description of it by means of a
computer. This article discusses the formal verification of the Ellipsoid
method, a convex optimization algorithm, and its code implementation as it
applies to receding horizon control. Options for encoding code properties and
their proofs are detailed. The applicability and limitations of those code
properties and proofs are presented as well. Finally, floating-point errors are
taken into account in a numerical analysis of the Ellipsoid algorithm.
Modifications to the algorithm are presented which can be used to control its
numerical stability.
| 2,020 | Computation and Language |
GECToR -- Grammatical Error Correction: Tag, Not Rewrite | In this paper, we present a simple and efficient GEC sequence tagger using a
Transformer encoder. Our system is pre-trained on synthetic data and then
fine-tuned in two stages: first on errorful corpora, and second on a
combination of errorful and error-free parallel corpora. We design custom
token-level transformations to map input tokens to target corrections. Our best
single-model/ensemble GEC tagger achieves an $F_{0.5}$ of 65.3/66.5 on
CoNLL-2014 (test) and $F_{0.5}$ of 72.4/73.6 on BEA-2019 (test). Its inference
speed is up to 10 times as fast as a Transformer-based seq2seq GEC system. The
code and trained models are publicly available.
| 2,020 | Computation and Language |
Generating Semantically Valid Adversarial Questions for TableQA | Adversarial attack on question answering systems over tabular data (TableQA)
can help evaluate to what extent they can understand natural language questions
and reason with tables. However, generating natural language adversarial
questions is difficult, because even a single character swap could lead to huge
semantic difference in human perception. In this paper, we propose SAGE
(Semantically valid Adversarial GEnerator), a Wasserstein sequence-to-sequence
model for TableQA white-box attack. To preserve meaning of original questions,
we apply minimum risk training with SIMILE and entity delexicalization. We use
Gumbel-Softmax to incorporate adversarial loss for end-to-end training. Our
experiments show that SAGE outperforms existing local attack models on semantic
validity and fluency while achieving a good attack success rate. Finally, we
demonstrate that adversarial training with SAGE augmented data can improve
performance and robustness of TableQA systems.
| 2,020 | Computation and Language |
Exploring aspects of similarity between spoken personal narratives by
disentangling them into narrative clause types | Sharing personal narratives is a fundamental aspect of human social behavior
as it helps share our life experiences. We can tell stories and rely on our
background to understand their context, similarities, and differences. A
substantial effort has been made towards developing storytelling machines or
inferring characters' features. However, we don't usually find models that
compare narratives. This task is remarkably challenging for machines since
they, as sometimes we do, lack an understanding of what similarity means. To
address this challenge, we first introduce a corpus of real-world spoken
personal narratives comprising 10,296 narrative clauses from 594 video
transcripts. Second, we ask non-narrative experts to annotate those clauses
under Labov's sociolinguistic model of personal narratives (i.e., action,
orientation, and evaluation clause types) and train a classifier that reaches
84.7% F-score for the highest-agreed clauses. Finally, we match stories and
explore whether people implicitly rely on Labov's framework to compare
narratives. We show that actions followed by the narrator's evaluation of these
are the aspects non-experts consider the most. Our approach is intended to help
inform machine learning methods aimed at studying or representing personal
narratives.
| 2,020 | Computation and Language |
CERT: Contrastive Self-supervised Learning for Language Understanding | Pretrained language models such as BERT, GPT have shown great effectiveness
in language understanding. The auxiliary predictive tasks in existing
pretraining approaches are mostly defined on tokens, thus may not be able to
capture sentence-level semantics very well. To address this issue, we propose
CERT: Contrastive self-supervised Encoder Representations from Transformers,
which pretrains language representation models using contrastive
self-supervised learning at the sentence level. CERT creates augmentations of
original sentences using back-translation. Then it finetunes a pretrained
language encoder (e.g., BERT) by predicting whether two augmented sentences
originate from the same sentence. CERT is simple to use and can be flexibly
plugged into any pretraining-finetuning NLP pipeline. We evaluate CERT on 11
natural language understanding tasks in the GLUE benchmark where CERT
outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks,
and performs worse than BERT on 2 tasks. On the averaged score of the 11 tasks,
CERT outperforms BERT. The data and code are available at
https://github.com/UCSD-AI4H/CERT
| 2,020 | Computation and Language |
Med-BERT: pre-trained contextualized embeddings on large-scale
structured electronic health records for disease prediction | Deep learning (DL) based predictive models from electronic health records
(EHR) deliver impressive performance in many clinical tasks. Large training
cohorts, however, are often required to achieve high accuracy, hindering the
adoption of DL-based models in scenarios with limited training data size.
Recently, bidirectional encoder representations from transformers (BERT) and
related models have achieved tremendous successes in the natural language
processing domain. The pre-training of BERT on a very large training corpus
generates contextualized embeddings that can boost the performance of models
trained on smaller datasets. We propose Med-BERT, which adapts the BERT
framework for pre-training contextualized embedding models on structured
diagnosis data from 28,490,650 patients EHR dataset. Fine-tuning experiments
are conducted on two disease-prediction tasks: (1) prediction of heart failure
in patients with diabetes and (2) prediction of pancreatic cancer from two
clinical databases. Med-BERT substantially improves prediction accuracy,
boosting the area under receiver operating characteristics curve (AUC) by
2.02-7.12%. In particular, pre-trained Med-BERT substantially improves the
performance of tasks with very small fine-tuning training sets (300-500
samples) boosting the AUC by more than 20% or equivalent to the AUC of 10 times
larger training set. We believe that Med-BERT will benefit disease-prediction
studies with small local training datasets, reduce data collection expenses,
and accelerate the pace of artificial intelligence aided healthcare.
| 2,020 | Computation and Language |
Refining Implicit Argument Annotation for UCCA | Predicate-argument structure analysis is a central component in meaning
representations of text. The fact that some arguments are not explicitly
mentioned in a sentence gives rise to ambiguity in language understanding, and
renders it difficult for machines to interpret text correctly. However, only
few resources represent implicit roles for NLU, and existing studies in NLP
only make coarse distinctions between categories of arguments omitted from
linguistic form. This paper proposes a typology for fine-grained implicit
argument annotation on top of Universal Conceptual Cognitive Annotation's
foundational layer. The proposed implicit argument categorisation is driven by
theories of implicit role interpretation and consists of six types: Deictic,
Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We
exemplify our design by revisiting part of the UCCA EWT corpus, providing a new
dataset annotated with the refinement layer, and making a comparative analysis
with other schemes.
| 2,021 | Computation and Language |
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD):
Manual Revision to Build Robust Parsing Model in Korean | In this paper, we first open on important issues regarding the Penn Korean
Universal Treebank (PKT-UD) and address these issues by revising the entire
corpus manually with the aim of producing cleaner UD annotations that are more
faithful to Korean grammar. For compatibility to the rest of UD corpora, we
follow the UDv2 guidelines, and extensively revise the part-of-speech tags and
the dependency relations to reflect morphological features and flexible
word-order aspects in Korean. The original and the revised versions of PKT-UD
are experimented with transformer-based parsing models using biaffine
attention. The parsing model trained on the revised corpus shows a significant
improvement of 3.0% in labeled attachment score over the model trained on the
previous corpus. Our error analysis demonstrates that this revision allows the
parsing model to learn relations more robustly, reducing several critical
errors that used to be made by the previous model.
| 2,020 | Computation and Language |
Comparing BERT against traditional machine learning text classification | The BERT model has arisen as a popular state-of-the-art machine learning
model in the recent years that is able to cope with multiple NLP tasks such as
supervised text classification without human supervision. Its flexibility to
cope with any type of corpus delivering great results has make this approach
very popular not only in academia but also in the industry. Although, there are
lots of different approaches that have been used throughout the years with
success. In this work, we first present BERT and include a little review on
classical NLP approaches. Then, we empirically test with a suite of experiments
dealing different scenarios the behaviour of BERT against the traditional
TF-IDF vocabulary fed to machine learning algorithms. Our purpose of this work
is to add empirical evidence to support or refuse the use of BERT as a default
on NLP tasks. Experiments show the superiority of BERT and its independence of
features of the NLP problem such as the language of the text adding empirical
evidence to use BERT as a default technique to be used in NLP problems.
| 2,023 | Computation and Language |
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual
Transfer Too | Intermediate-task training---fine-tuning a pretrained model on an
intermediate task before fine-tuning again on the target task---often improves
model performance substantially on language understanding tasks in monolingual
English settings. We investigate whether English intermediate-task training is
still helpful on non-English target tasks. Using nine intermediate
language-understanding tasks, we evaluate intermediate-task transfer in a
zero-shot cross-lingual setting on the XTREME benchmark. We see large
improvements from intermediate training on the BUCC and Tatoeba sentence
retrieval tasks and moderate improvements on question-answering target tasks.
MNLI, SQuAD and HellaSwag achieve the best overall results as intermediate
tasks, while multi-task intermediate offers small additional improvements.
Using our best intermediate-task models for each target task, we obtain a 5.4
point improvement over XLM-R Large on the XTREME benchmark, setting the state
of the art as of June 2020. We also investigate continuing multilingual MLM
during intermediate-task training and using machine-translated
intermediate-task data, but neither consistently outperforms simply performing
English intermediate-task training.
| 2,020 | Computation and Language |
Examining Racial Bias in an Online Abuse Corpus with Structural Topic
Modeling | We use structural topic modeling to examine racial bias in data collected to
train models to detect hate speech and abusive language in social media posts.
We augment the abusive language dataset by adding an additional feature
indicating the predicted probability of the tweet being written in
African-American English. We then use structural topic modeling to examine the
content of the tweets and how the prevalence of different topics is related to
both abusiveness annotation and dialect prediction. We find that certain topics
are disproportionately racialized and considered abusive. We discuss how topic
modeling may be a useful approach for identifying bias in annotated data.
| 2,020 | Computation and Language |
Learning with Weak Supervision for Email Intent Detection | Email remains one of the most frequently used means of online communication.
People spend a significant amount of time every day on emails to exchange
information, manage tasks and schedule events. Previous work has studied
different ways for improving email productivity by prioritizing emails,
suggesting automatic replies or identifying intents to recommend appropriate
actions. The problem has been mostly posed as a supervised learning problem
where models of different complexities were proposed to classify an email
message into a predefined taxonomy of intents or classes. The need for labeled
data has always been one of the largest bottlenecks in training supervised
models. This is especially the case for many real-world tasks, such as email
intent classification, where large scale annotated examples are either hard to
acquire or unavailable due to privacy or data access constraints. Email users
often take actions in response to intents expressed in an email (e.g., setting
up a meeting in response to an email with a scheduling request). Such actions
can be inferred from user interaction logs. In this paper, we propose to
leverage user actions as a source of weak supervision, in addition to a limited
set of annotated examples, to detect intents in emails. We develop an
end-to-end robust deep neural network model for email intent identification
that leverages both clean annotated data and noisy weak supervision along with
a self-paced learning mechanism. Extensive experiments on three different
intent detection tasks show that our approach can effectively leverage the
weakly supervised data to improve intent detection in emails.
| 2,020 | Computation and Language |
Predict-then-Decide: A Predictive Approach for Wait or Answer Task in
Dialogue Systems | Different people have different habits of describing their intents in
conversations. Some people tend to deliberate their intents in several
successive utterances, i.e., they use several consistent messages for
readability instead of a long sentence to express their question. This creates
a predicament faced by the application of dialogue systems, especially in
real-world industry scenarios, in which the dialogue system is unsure whether
it should answer the query of user immediately or wait for further
supplementary input. Motivated by such an interesting predicament, we define a
novel Wait-or-Answer task for dialogue systems. We shed light on a new research
topic about how the dialogue system can be more intelligent to behave in this
Wait-or-Answer quandary. Further, we propose a predictive approach named
Predict-then-Decide (PTD) to tackle this Wait-or-Answer task. More
specifically, we take advantage of a decision model to help the dialogue system
decide whether to wait or answer. The decision of decision model is made with
the assistance of two ancillary prediction models: a user prediction and an
agent prediction. The user prediction model tries to predict what the user
would supplement and uses its prediction to persuade the decision model that
the user has some information to add, so the dialogue system should wait. The
agent prediction model tries to predict the answer of the dialogue system and
convince the decision model that it is a superior choice to answer the query of
user immediately since the input of user has come to an end. We conduct our
experiments on two real-life scenarios and three public datasets. Experimental
results on five datasets show our proposed PTD approach significantly
outperforms the existing models in solving this Wait-or-Answer problem.
| 2,021 | Computation and Language |
Counterfactual Detection meets Transfer Learning | We can consider Counterfactuals as belonging in the domain of Discourse
structure and semantics, A core area in Natural Language Understanding and in
this paper, we introduce an approach to resolving counterfactual detection as
well as the indexing of the antecedents and consequents of Counterfactual
statements. While Transfer learning is already being applied to several NLP
tasks, It has the characteristics to excel in a novel number of tasks. We show
that detecting Counterfactuals is a straightforward Binary Classification Task
that can be implemented with minimal adaptation on already existing model
Architectures, thanks to a well annotated training data set,and we introduce a
new end to end pipeline to process antecedents and consequents as an entity
recognition task, thus adapting them into Token Classification.
| 2,020 | Computation and Language |
MT-Adapted Datasheets for Datasets: Template and Repository | In this report we are taking the standardized model proposed by Gebru et al.
(2018) for documenting the popular machine translation datasets of the EuroParl
(Koehn, 2005) and News-Commentary (Barrault et al., 2019). Within this
documentation process, we have adapted the original datasheet to the particular
case of data consumers within the Machine Translation area. We are also
proposing a repository for collecting the adapted datasheets in this research
area
| 2,020 | Computation and Language |
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue
Models | Recently, neural network based dialogue systems have become ubiquitous in our
increasingly digitalized society. However, due to their inherent opaqueness,
some recently raised concerns about using neural models are starting to be
taken seriously. In fact, intentional or unintentional behaviors could lead to
a dialogue system to generate inappropriate responses. Thus, in this paper, we
investigate whether we can learn to craft input sentences that result in a
black-box neural dialogue model being manipulated into having its outputs
contain target words or match target sentences. We propose a reinforcement
learning based model that can generate such desired inputs automatically.
Extensive experiments on a popular well-trained state-of-the-art neural
dialogue model show that our method can successfully seek out desired inputs
that lead to the target outputs in a considerable portion of cases.
Consequently, our work reveals the potential of neural dialogue models to be
manipulated, which inspires and opens the door towards developing strategies to
defend them.
| 2,020 | Computation and Language |
Give Me Convenience and Give Her Death: Who Should Decide What Uses of
NLP are Appropriate, and on What Basis? | As part of growing NLP capabilities, coupled with an awareness of the ethical
dimensions of research, questions have been raised about whether particular
datasets and tasks should be deemed off-limits for NLP research. We examine
this question with respect to a paper on automatic legal sentencing from EMNLP
2019 which was a source of some debate, in asking whether the paper should have
been allowed to be published, who should have been charged with making such a
decision, and on what basis. We focus in particular on the role of data
statements in ethically assessing research, but also discuss the topic of dual
use, and examine the outcomes of similar debates in other scientific
disciplines.
| 2,020 | Computation and Language |
Establishing a New State-of-the-Art for French Named Entity Recognition | The French TreeBank developed at the University Paris 7 is the main source of
morphosyntactic and syntactic annotations for French. However, it does not
include explicit information related to named entities, which are among the
most useful information for several natural language processing tasks and
applications. Moreover, no large-scale French corpus with named entity
annotations contain referential information, which complement the type and the
span of each mention with an indication of the entity it refers to. We have
manually annotated the French TreeBank with such information, after an
automatic pre-annotation step. We sketch the underlying annotation guidelines
and we provide a few figures about the resulting annotations.
| 2,020 | Computation and Language |
Catching Attention with Automatic Pull Quote Selection | To advance understanding on how to engage readers, we advocate the novel task
of automatic pull quote selection. Pull quotes are a component of articles
specifically designed to catch the attention of readers with spans of text
selected from the article and given more salient presentation. This task
differs from related tasks such as summarization and clickbait identification
by several aspects. We establish a spectrum of baseline approaches to the task,
ranging from handcrafted features to a neural mixture-of-experts to cross-task
models. By examining the contributions of individual features and embedding
dimensions from these models, we uncover unexpected properties of pull quotes
to help answer the important question of what engages readers. Human evaluation
also supports the uniqueness of this task and the suitability of our selection
models. The benefits of exploring this problem further are clear: pull quotes
increase enjoyment and readability, shape reader perceptions, and facilitate
learning. Code to reproduce this work is available at
https://github.com/tannerbohn/AutomaticPullQuoteSelection.
| 2,020 | Computation and Language |
Tracking, exploring and analyzing recent developments in German-language
online press in the face of the coronavirus crisis: cOWIDplus Analysis and
cOWIDplus Viewer | The coronavirus pandemic may be the largest crisis the world has had to face
since World War II. It does not come as a surprise that it is also having an
impact on language as our primary communication tool. We present three
inter-connected resources that are designed to capture and illustrate these
effects on a subset of the German language: An RSS corpus of German-language
newsfeeds (with freely available untruncated unigram frequency lists), a static
but continuously updated HTML page tracking the diversity of the used
vocabulary and a web application that enables other researchers and the broader
public to explore these effects without any or with little knowledge of corpus
representation/exploration or statistical analyses.
| 2,020 | Computation and Language |
Enriched In-Order Linearization for Faster Sequence-to-Sequence
Constituent Parsing | Sequence-to-sequence constituent parsing requires a linearization to
represent trees as sequences. Top-down tree linearizations, which can be based
on brackets or shift-reduce actions, have achieved the best accuracy to date.
In this paper, we show that these results can be improved by using an in-order
linearization instead. Based on this observation, we implement an enriched
in-order shift-reduce linearization inspired by Vinyals et al. (2015)'s
approach, achieving the best accuracy to date on the English PTB dataset among
fully-supervised single-model sequence-to-sequence constituent parsers.
Finally, we apply deterministic attention mechanisms to match the speed of
state-of-the-art transition-based parsers, thus showing that
sequence-to-sequence models can match them, not only in accuracy, but also in
speed.
| 2,020 | Computation and Language |
Transition-based Semantic Dependency Parsing with Pointer Networks | Transition-based parsers implemented with Pointer Networks have become the
new state of the art in dependency parsing, excelling in producing labelled
syntactic trees and outperforming graph-based models in this task. In order to
further test the capabilities of these powerful neural networks on a harder NLP
problem, we propose a transition system that, thanks to Pointer Networks, can
straightforwardly produce labelled directed acyclic graphs and perform semantic
dependency parsing. In addition, we enhance our approach with deep
contextualized word embeddings extracted from BERT. The resulting system not
only outperforms all existing transition-based models, but also matches the
best fully-supervised accuracy to date on the SemEval 2015 Task 18 English
datasets among previous state-of-the-art graph-based parsers.
| 2,020 | Computation and Language |
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews | Despite the recent advances in opinion mining for written reviews, few works
have tackled the problem on other sources of reviews. In light of this issue,
we propose a multi-modal approach for mining fine-grained opinions from video
reviews that is able to determine the aspects of the item under review that are
being discussed and the sentiment orientation towards them. Our approach works
at the sentence level without the need for time annotations and uses features
derived from the audio, video and language transcriptions of its contents. We
evaluate our approach on two datasets and show that leveraging the video and
audio modalities consistently provides increased performance over text-only
baselines, providing evidence these extra modalities are key in better
understanding video reviews.
| 2,020 | Computation and Language |
The First Shared Task on Discourse Representation Structure Parsing | The paper presents the IWCS 2019 shared task on semantic parsing where the
goal is to produce Discourse Representation Structures (DRSs) for English
sentences. DRSs originate from Discourse Representation Theory and represent
scoped meaning representations that capture the semantics of negation, modals,
quantification, and presupposition triggers. Additionally, concepts and
event-participants in DRSs are described with WordNet synsets and the thematic
roles from VerbNet. To measure similarity between two DRSs, they are
represented in a clausal form, i.e. as a set of tuples. Participant systems
were expected to produce DRSs in this clausal form. Taking into account the
rich lexical information, explicit scope marking, a high number of shared
variables among clauses, and highly-constrained format of valid DRSs, all these
makes the DRS parsing a challenging NLP task. The results of the shared task
displayed improvements over the existing state-of-the-art parser.
| 2,019 | Computation and Language |
CausaLM: Causal Model Explanation Through Counterfactual Language Models | Understanding predictions made by deep neural networks is notoriously
difficult, but also crucial to their dissemination. As all machine learning
based methods, they are as good as their training data, and can also capture
unwanted biases. While there are tools that can help understand whether such
biases exist, they do not distinguish between correlation and causation, and
might be ill-suited for text-based models and for reasoning about high level
language concepts. A key problem of estimating the causal effect of a concept
of interest on a given model is that this estimation requires the generation of
counterfactual examples, which is challenging with existing generation
technology. To bridge that gap, we propose CausaLM, a framework for producing
causal model explanations using counterfactual language representation models.
Our approach is based on fine-tuning of deep contextualized embedding models
with auxiliary adversarial tasks derived from the causal graph of the problem.
Concretely, we show that by carefully choosing auxiliary adversarial
pre-training tasks, language representation models such as BERT can effectively
learn a counterfactual representation for a given concept of interest, and be
used to estimate its true causal effect on model performance. A byproduct of
our method is a language representation model that is unaffected by the tested
concept, which can be useful in mitigating unwanted bias ingrained in the data.
| 2,022 | Computation and Language |
Thirty Musts for Meaning Banking | Meaning banking--creating a semantically annotated corpus for the purpose of
semantic parsing or generation--is a challenging task. It is quite simple to
come up with a complex meaning representation, but it is hard to design a
simple meaning representation that captures many nuances of meaning. This paper
lists some lessons learned in nearly ten years of meaning annotation during the
development of the Groningen Meaning Bank (Bos et al., 2017) and the Parallel
Meaning Bank (Abzianidze et al., 2017). The paper's format is rather
unconventional: there is no explicit related work, no methodology section, no
results, and no discussion (and the current snippet is not an abstract but
actually an introductory preface). Instead, its structure is inspired by work
of Traum (2000) and Bender (2013). The list starts with a brief overview of the
existing meaning banks (Section 1) and the rest of the items are roughly
divided into three groups: corpus collection (Section 2 and 3, annotation
methods (Section 4-11), and design of meaning representations (Section 12-30).
We hope this overview will give inspiration and guidance in creating improved
meaning banks in the future.
| 2,019 | Computation and Language |
Self-Training for Unsupervised Parsing with PRPN | Neural unsupervised parsing (UP) models learn to parse without access to
syntactic annotations, while being optimized for another task like language
modeling. In this work, we propose self-training for neural UP models: we
leverage aggregated annotations predicted by copies of our model as supervision
for future copies. To be able to use our model's predictions during training,
we extend a recent neural UP architecture, the PRPN (Shen et al., 2018a) such
that it can be trained in a semi-supervised fashion. We then add examples with
parses predicted by our model to our unlabeled UP training data. Our
self-trained model outperforms the PRPN by 8.1% F1 and the previous state of
the art by 1.6% F1. In addition, we show that our architecture can also be
helpful for semi-supervised parsing in ultra-low-resource settings.
| 2,020 | Computation and Language |
Syntactic Structure Distillation Pretraining For Bidirectional Encoders | Textual representation learners trained on large amounts of data have
achieved notable success on downstream tasks; intriguingly, they have also
performed well on challenging tests of syntactic competence. Given this
success, it remains an open question whether scalable learners like BERT can
become fully proficient in the syntax of natural language by virtue of data
scale alone, or whether they still benefit from more explicit syntactic biases.
To answer this question, we introduce a knowledge distillation strategy for
injecting syntactic biases into BERT pretraining, by distilling the
syntactically informative predictions of a hierarchical---albeit harder to
scale---syntactic language model. Since BERT models masked words in
bidirectional context, we propose to distill the approximate marginal
distribution over words in context from the syntactic LM. Our approach reduces
relative error by 2-21% on a diverse set of structured prediction tasks,
although we obtain mixed results on the GLUE benchmark. Our findings
demonstrate the benefits of syntactic biases, even in representation learners
that exploit large amounts of data, and contribute to a better understanding of
where syntactic biases are most helpful in benchmarks of natural language
understanding.
| 2,020 | Computation and Language |
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing | One daunting problem for semantic parsing is the scarcity of annotation.
Aiming to reduce nontrivial human labor, we propose a two-stage semantic
parsing framework, where the first stage utilizes an unsupervised paraphrase
model to convert an unlabeled natural language utterance into the canonical
utterance. The downstream naive semantic parser accepts the intermediate output
and returns the target logical form. Furthermore, the entire training process
is split into two phases: pre-training and cycle learning. Three tailored
self-supervised tasks are introduced throughout training to activate the
unsupervised paraphrase model. Experimental results on benchmarks Overnight and
GeoGranno demonstrate that our framework is effective and compatible with
supervised training.
| 2,020 | Computation and Language |
In search of isoglosses: continuous and discrete language embeddings in
Slavic historical phonology | This paper investigates the ability of neural network architectures to
effectively learn diachronic phonological generalizations in a multilingual
setting. We employ models using three different types of language embedding
(dense, sigmoid, and straight-through). We find that the Straight-Through model
outperforms the other two in terms of accuracy, but the Sigmoid model's
language embeddings show the strongest agreement with the traditional
subgrouping of the Slavic languages. We find that the Straight-Through model
has learned coherent, semi-interpretable information about sound change, and
outline directions for future research.
| 2,020 | Computation and Language |
Language Representation Models for Fine-Grained Sentiment Classification | Sentiment classification is a quickly advancing field of study with
applications in almost any field. While various models and datasets have shown
high accuracy inthe task of binary classification, the task of fine-grained
sentiment classification is still an area with room for significant
improvement. Analyzing the SST-5 dataset,previous work by Munikar et al. (2019)
showed that the embedding tool BERT allowed a simple model to achieve
state-of-the-art accuracy. Since that paper, several BERT alternatives have
been published, with three primary ones being AlBERT (Lan et al., 2019),
DistilBERT (Sanh et al. 2019), and RoBERTa (Liu etal. 2019). While these models
report some improvement over BERT on the popular benchmarks GLUE, SQuAD, and
RACE, they have not been applied to the fine-grained classification task. In
this paper, we examine whether the improvements hold true when applied to a
novel task, by replicating the BERT model from Munikar et al., and swapping the
embedding layer to the alternative models. Over the experiments, we found that
AlBERT suffers significantly more accuracy loss than reported on other tasks,
DistilBERT has accuracy loss similar to their reported loss on other tasks
while being the fastest model to train, and RoBERTa reaches anew
state-of-the-art accuracy for prediction on the SST-5 root level (60.2%).
| 2,020 | Computation and Language |
Phone Features Improve Speech Translation | End-to-end models for speech translation (ST) more tightly couple speech
recognition (ASR) and machine translation (MT) than a traditional cascade of
separate ASR and MT models, with simpler model architectures and the potential
for reduced error propagation. Their performance is often assumed to be
superior, though in many conditions this is not yet the case. We compare
cascaded and end-to-end models across high, medium, and low-resource
conditions, and show that cascades remain stronger baselines. Further, we
introduce two methods to incorporate phone features into ST models. We show
that these features improve both architectures, closing the gap between
end-to-end models and cascades, and outperforming previous academic work -- by
up to 9 BLEU on our low-resource setting.
| 2,020 | Computation and Language |
The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm
Completion | In this paper, we describe the findings of the SIGMORPHON 2020 shared task on
unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a
novel task in the field of inflectional morphology. Participants were asked to
submit systems which take raw text and a list of lemmas as input, and output
all inflected forms, i.e., the entire morphological paradigm, of each lemma. In
order to simulate a realistic use case, we first released data for 5
development languages. However, systems were officially evaluated on 9 surprise
languages, which were only revealed a few days before the submission deadline.
We provided a modular baseline system, which is a pipeline of 4 components. 3
teams submitted a total of 7 systems, but, surprisingly, none of the submitted
systems was able to improve over the baseline on average over all 9 test
languages. Only on 3 languages did a submitted system obtain the best results.
This shows that unsupervised morphological paradigm completion is still largely
unsolved. We present an analysis here, so that this shared task will ground
further research on the topic.
| 2,020 | Computation and Language |
ConCET: Entity-Aware Topic Classification for Open-Domain Conversational
Agents | Identifying the topic (domain) of each user's utterance in open-domain
conversational systems is a crucial step for all subsequent language
understanding and response tasks. In particular, for complex domains, an
utterance is often routed to a single component responsible for that domain.
Thus, correctly mapping a user utterance to the right domain is critical. To
address this problem, we introduce ConCET: a Concurrent Entity-aware
conversational Topic classifier, which incorporates entity-type information
together with the utterance content features. Specifically, ConCET utilizes
entity information to enrich the utterance representation, combining character,
word, and entity-type embeddings into a single representation. However, for
rich domains with millions of available entities, unrealistic amounts of
labeled training data would be required. To complement our model, we propose a
simple and effective method for generating synthetic training data, to augment
the typically limited amounts of labeled training data, using commonly
available knowledge bases to generate additional labeled utterances. We
extensively evaluate ConCET and our proposed training method first on an openly
available human-human conversational dataset called Self-Dialogue, to calibrate
our approach against previous state-of-the-art methods; second, we evaluate
ConCET on a large dataset of human-machine conversations with real users,
collected as part of the Amazon Alexa Prize. Our results show that ConCET
significantly improves topic classification performance on both datasets,
including 8-10% improvements over state-of-the-art deep learning methods. We
complement our quantitative results with detailed analysis of system
performance, which could be used for further improvements of conversational
agents.
| 2,020 | Computation and Language |
Would you Like to Talk about Sports Now? Towards Contextual Topic
Suggestion for Open-Domain Conversational Agents | To hold a true conversation, an intelligent agent should be able to
occasionally take initiative and recommend the next natural conversation topic.
This is a challenging task. A topic suggested by the agent should be relevant
to the person, appropriate for the conversation context, and the agent should
have something interesting to say about it. Thus, a scripted, or
one-size-fits-all, popularity-based topic suggestion is doomed to fail.
Instead, we explore different methods for a personalized, contextual topic
suggestion for open-domain conversations. We formalize the Conversational Topic
Suggestion problem (CTS) to more clearly identify the assumptions and
requirements. We also explore three possible approaches to solve this problem:
(1) model-based sequential topic suggestion to capture the conversation context
(CTS-Seq), (2) Collaborative Filtering-based suggestion to capture previous
successful conversations from similar users (CTS-CF), and (3) a hybrid approach
combining both conversation context and collaborative filtering. To evaluate
the effectiveness of these methods, we use real conversations collected as part
of the Amazon Alexa Prize 2018 Conversational AI challenge. The results are
promising: the CTS-Seq model suggests topics with 23% higher accuracy than the
baseline, and incorporating collaborative filtering signals into a hybrid
CTS-Seq-CF model further improves recommendation accuracy by 12%. Together, our
proposed models, experiments, and analysis significantly advance the study of
open-domain conversational agents, and suggest promising directions for future
improvements.
| 2,020 | Computation and Language |
Contextual Dialogue Act Classification for Open-Domain Conversational
Agents | Classifying the general intent of the user utterance in a conversation, also
known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or
request for an opinion, is a key step in Natural Language Understanding (NLU)
for conversational agents. While DA classification has been extensively studied
in human-human conversations, it has not been sufficiently explored for the
emerging open-domain automated conversational agents. Moreover, despite
significant advances in utterance-level DA classification, full understanding
of dialogue utterances requires conversational context. Another challenge is
the lack of available labeled data for open-domain human-machine conversations.
To address these problems, we propose a novel method, CDAC (Contextual Dialogue
Act Classifier), a simple yet effective deep learning approach for contextual
dialogue act classification. Specifically, we use transfer learning to adapt
models trained on human-human conversations to predict dialogue acts in
human-machine dialogues. To investigate the effectiveness of our method, we
train our model on the well-known Switchboard human-human dialogue dataset, and
fine-tune it for predicting dialogue acts in human-machine conversation data,
collected as part of the Amazon Alexa Prize 2018 competition. The results show
that the CDAC model outperforms an utterance-level state of the art baseline by
8.0% on the Switchboard dataset, and is comparable to the latest reported
state-of-the-art contextual DA classification results. Furthermore, our results
show that fine-tuning the CDAC model on a small sample of manually labeled
human-machine conversations allows CDAC to more accurately predict dialogue
acts in real users' conversations, suggesting a promising direction for future
improvements.
| 2,020 | Computation and Language |
Subword RNNLM Approximations for Out-Of-Vocabulary Keyword Search | In spoken Keyword Search, the query may contain out-of-vocabulary (OOV) words
not observed when training the speech recognition system. Using subword
language models (LMs) in the first-pass recognition makes it possible to
recognize the OOV words, but even the subword n-gram LMs suffer from data
sparsity. Recurrent Neural Network (RNN) LMs alleviate the sparsity problems
but are not suitable for first-pass recognition as such. One way to solve this
is to approximate the RNNLMs by back-off n-gram models. In this paper, we
propose to interpolate the conventional n-gram models and the RNNLM
approximation for better OOV recognition. Furthermore, we develop a new RNNLM
approximation method suitable for subword units: It produces variable-order
n-grams to include long-span approximations and considers also n-grams that
were not originally observed in the training corpus. To evaluate these models
on OOVs, we setup Arabic and Finnish Keyword Search tasks concentrating only on
OOV words. On these tasks, interpolating the baseline RNNLM approximation and a
conventional LM outperforms the conventional LM in terms of the Maximum Term
Weighted Value for single-character subwords. Moreover, replacing the baseline
approximation with the proposed method achieves the best performance on both
multi- and single-character subwords.
| 2,020 | Computation and Language |
Generating Diverse and Consistent QA pairs from Contexts with
Information-Maximizing Hierarchical Conditional VAEs | One of the most crucial challenges in question answering (QA) is the scarcity
of labeled data, since it is costly to obtain question-answer (QA) pairs for a
target text domain with human annotation. An alternative approach to tackle the
problem is to use automatically generated QA pairs from either the problem
context or from large amount of unstructured texts (e.g. Wikipedia). In this
work, we propose a hierarchical conditional variational autoencoder (HCVAE) for
generating QA pairs given unstructured texts as contexts, while maximizing the
mutual information between generated QA pairs to ensure their consistency. We
validate our Information Maximizing Hierarchical Conditional Variational
AutoEncoder (Info-HCVAE) on several benchmark datasets by evaluating the
performance of the QA model (BERT-base) using only the generated QA pairs
(QA-based evaluation) or by using both the generated and human-labeled pairs
(semi-supervised learning) for training, against state-of-the-art baseline
models. The results show that our model obtains impressive performance gains
over all baselines on both tasks, using only a fraction of data for training.
| 2,020 | Computation and Language |
A Corpus for Large-Scale Phonetic Typology | A major hurdle in data-driven research on typology is having sufficient data
in many languages to draw meaningful conclusions. We present VoxClamantis v1.0,
the first large-scale corpus for phonetic typology, with aligned segments and
estimated phoneme-level labels in 690 readings spanning 635 languages, along
with acoustic-phonetic measures of vowels and sibilants. Access to such data
can greatly facilitate investigation of phonetic typology at a large scale and
across many languages. However, it is non-trivial and computationally intensive
to obtain such alignments for hundreds of languages, many of which have few to
no resources presently available. We describe the methodology to create our
corpus, discuss caveats with current methods and their impact on the utility of
this data, and illustrate possible research directions through a series of case
studies on the 48 highest-quality readings. Our corpus and scripts are publicly
available for non-commercial use at https://voxclamantisproject.github.io.
| 2,020 | Computation and Language |
Variational Neural Machine Translation with Normalizing Flows | Variational Neural Machine Translation (VNMT) is an attractive framework for
modeling the generation of target translations, conditioned not only on the
source sentence but also on some latent random variables. The latent variable
modeling may introduce useful statistical dependencies that can improve
translation accuracy. Unfortunately, learning informative latent variables is
non-trivial, as the latent space can be prohibitively large, and the latent
codes are prone to be ignored by many translation models at training time.
Previous works impose strong assumptions on the distribution of the latent code
and limit the choice of the NMT architecture. In this paper, we propose to
apply the VNMT framework to the state-of-the-art Transformer and introduce a
more flexible approximate posterior based on normalizing flows. We demonstrate
the efficacy of our proposal under both in-domain and out-of-domain conditions,
significantly outperforming strong baselines.
| 2,020 | Computation and Language |
Joint Modelling of Emotion and Abusive Language Detection | The rise of online communication platforms has been accompanied by some
undesirable effects, such as the proliferation of aggressive and abusive
behaviour online. Aiming to tackle this problem, the natural language
processing (NLP) community has experimented with a range of techniques for
abuse detection. While achieving substantial success, these methods have so far
only focused on modelling the linguistic properties of the comments and the
online communities of users, disregarding the emotional state of the users and
how this might affect their language. The latter is, however, inextricably
linked to abusive behaviour. In this paper, we present the first joint model of
emotion and abusive language detection, experimenting in a multi-task learning
framework that allows one task to inform the other. Our results demonstrate
that incorporating affective features leads to significant improvements in
abuse detection performance across datasets.
| 2,020 | Computation and Language |
Language (Technology) is Power: A Critical Survey of "Bias" in NLP | We survey 146 papers analyzing "bias" in NLP systems, finding that their
motivations are often vague, inconsistent, and lacking in normative reasoning,
despite the fact that analyzing "bias" is an inherently normative process. We
further find that these papers' proposed quantitative techniques for measuring
or mitigating "bias" are poorly matched to their motivations and do not engage
with the relevant literature outside of NLP. Based on these findings, we
describe the beginnings of a path forward by proposing three recommendations
that should guide work analyzing "bias" in NLP systems. These recommendations
rest on a greater recognition of the relationships between language and social
hierarchies, encouraging researchers and practitioners to articulate their
conceptualizations of "bias"---i.e., what kinds of system behaviors are
harmful, in what ways, to whom, and why, as well as the normative reasoning
underlying these statements---and to center work around the lived experiences
of members of communities affected by NLP systems, while interrogating and
reimagining the power relations between technologists and such communities.
| 2,020 | Computation and Language |
Adversarial Attacks and Defense on Texts: A Survey | Deep learning models have been used widely for various purposes in recent
years in object recognition, self-driving cars, face recognition, speech
recognition, sentiment analysis, and many others. However, in recent years it
has been shown that these models possess weakness to noises which force the
model to misclassify. This issue has been studied profoundly in the image and
audio domain. Very little has been studied on this issue concerning textual
data. Even less survey on this topic has been performed to understand different
types of attacks and defense techniques. In this manuscript, we accumulated and
analyzed different attacking techniques and various defense models to provide a
more comprehensive idea. Later we point out some of the interesting findings of
all papers and challenges that need to be overcome to move forward in this
field.
| 2,020 | Computation and Language |
Cats climb entails mammals move: preserving hyponymy in compositional
distributional semantics | To give vector-based representations of meaning more structure, one approach
is to use positive semidefinite (psd) matrices. These allow us to model
similarity of words as well as the hyponymy or is-a relationship. Psd matrices
can be learnt relatively easily in a given vector space $M\otimes M^*$, but to
compose words to form phrases and sentences, we need representations in larger
spaces. In this paper, we introduce a generic way of composing the psd matrices
corresponding to words. We propose that psd matrices for verbs, adjectives, and
other functional words be lifted to completely positive (CP) maps that match
their grammatical type. This lifting is carried out by our composition rule
called Compression, Compr. In contrast to previous composition rules like Fuzz
and Phaser (a.k.a. KMult and BMult), Compr preserves hyponymy. Mathematically,
Compr is itself a CP map, and is therefore linear and generally
non-commutative. We give a number of proposals for the structure of Compr,
based on spiders, cups and caps, and generate a range of composition rules. We
test these rules on a small sentence entailment dataset, and see some
improvements over the performance of Fuzz and Phaser.
| 2,020 | Computation and Language |
Language Models are Few-Shot Learners | Recent work has demonstrated substantial gains on many NLP tasks and
benchmarks by pre-training on a large corpus of text followed by fine-tuning on
a specific task. While typically task-agnostic in architecture, this method
still requires task-specific fine-tuning datasets of thousands or tens of
thousands of examples. By contrast, humans can generally perform a new language
task from only a few examples or from simple instructions - something which
current NLP systems still largely struggle to do. Here we show that scaling up
language models greatly improves task-agnostic, few-shot performance, sometimes
even reaching competitiveness with prior state-of-the-art fine-tuning
approaches. Specifically, we train GPT-3, an autoregressive language model with
175 billion parameters, 10x more than any previous non-sparse language model,
and test its performance in the few-shot setting. For all tasks, GPT-3 is
applied without any gradient updates or fine-tuning, with tasks and few-shot
demonstrations specified purely via text interaction with the model. GPT-3
achieves strong performance on many NLP datasets, including translation,
question-answering, and cloze tasks, as well as several tasks that require
on-the-fly reasoning or domain adaptation, such as unscrambling words, using a
novel word in a sentence, or performing 3-digit arithmetic. At the same time,
we also identify some datasets where GPT-3's few-shot learning still struggles,
as well as some datasets where GPT-3 faces methodological issues related to
training on large web corpora. Finally, we find that GPT-3 can generate samples
of news articles which human evaluators have difficulty distinguishing from
articles written by humans. We discuss broader societal impacts of this finding
and of GPT-3 in general.
| 2,020 | Computation and Language |
HAT: Hardware-Aware Transformers for Efficient Natural Language
Processing | Transformers are ubiquitous in Natural Language Processing (NLP) tasks, but
they are difficult to be deployed on hardware due to the intensive computation.
To enable low-latency inference on resource-constrained hardware platforms, we
propose to design Hardware-Aware Transformers (HAT) with neural architecture
search. We first construct a large design space with $\textit{arbitrary
encoder-decoder attention}$ and $\textit{heterogeneous layers}$. Then we train
a $\textit{SuperTransformer}$ that covers all candidates in the design space,
and efficiently produces many $\textit{SubTransformers}$ with weight sharing.
Finally, we perform an evolutionary search with a hardware latency constraint
to find a specialized $\textit{SubTransformer}$ dedicated to run fast on the
target hardware. Extensive experiments on four machine translation tasks
demonstrate that HAT can discover efficient models for different hardware (CPU,
GPU, IoT device). When running WMT'14 translation task on Raspberry Pi-4, HAT
can achieve $\textbf{3}\times$ speedup, $\textbf{3.7}\times$ smaller size over
baseline Transformer; $\textbf{2.7}\times$ speedup, $\textbf{3.6}\times$
smaller size over Evolved Transformer with $\textbf{12,041}\times$ less search
cost and no performance loss. HAT code is
https://github.com/mit-han-lab/hardware-aware-transformers.git
| 2,020 | Computation and Language |
Empirical Evaluation of Pretraining Strategies for Supervised Entity
Linking | In this work, we present an entity linking model which combines a Transformer
architecture with large scale pretraining from Wikipedia links. Our model
achieves the state-of-the-art on two commonly used entity linking datasets:
96.7% on CoNLL and 94.9% on TAC-KBP. We present detailed analyses to understand
what design choices are important for entity linking, including choices of
negative entity candidates, Transformer architecture, and input perturbations.
Lastly, we present promising results on more challenging settings such as
end-to-end entity linking and entity linking without in-domain training data.
| 2,020 | Computation and Language |
What is SemEval evaluating? A Systematic Analysis of Evaluation
Campaigns in NLP | SemEval is the primary venue in the NLP community for the proposal of new
challenges and for the systematic empirical evaluation of NLP systems. This
paper provides a systematic quantitative analysis of SemEval aiming to evidence
the patterns of the contributions behind SemEval. By understanding the
distribution of task types, metrics, architectures, participation and citations
over time we aim to answer the question on what is being evaluated by SemEval.
| 2,021 | Computation and Language |
On Incorporating Structural Information to improve Dialogue Response
Generation | We consider the task of generating dialogue responses from background
knowledge comprising of domain specific resources. Specifically, given a
conversation around a movie, the task is to generate the next response based on
background knowledge about the movie such as the plot, review, Reddit comments
etc. This requires capturing structural, sequential and semantic information
from the conversation context and the background resources. This is a new task
and has not received much attention from the community. We propose a new
architecture that uses the ability of BERT to capture deep contextualized
representations in conjunction with explicit structure and sequence
information. More specifically, we use (i) Graph Convolutional Networks (GCNs)
to capture structural information, (ii) LSTMs to capture sequential information
and (iii) BERT for the deep contextualized representations that capture
semantic information. We analyze the proposed architecture extensively. To this
end, we propose a plug-and-play Semantics-Sequences-Structures (SSS) framework
which allows us to effectively combine such linguistic information. Through a
series of experiments we make some interesting observations. First, we observe
that the popular adaptation of the GCN model for NLP tasks where structural
information (GCNs) was added on top of sequential information (LSTMs) performs
poorly on our task. This leads us to explore interesting ways of combining
semantic and structural information to improve the performance. Second, we
observe that while BERT already outperforms other deep contextualized
representations such as ELMo, it still benefits from the additional structural
information explicitly added using GCNs. This is a bit surprising given the
recent claims that BERT already captures structural information. Lastly, the
proposed SSS framework gives an improvement of 7.95% over the baseline.
| 2,020 | Computation and Language |
Noise Robust Named Entity Understanding for Voice Assistants | Named Entity Recognition (NER) and Entity Linking (EL) play an essential role
in voice assistant interaction, but are challenging due to the special
difficulties associated with spoken user queries. In this paper, we propose a
novel architecture that jointly solves the NER and EL tasks by combining them
in a joint reranking module. We show that our proposed framework improves NER
accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features
used also lead to better accuracies in other natural language understanding
tasks, such as domain classification and semantic parsing.
| 2,021 | Computation and Language |
Neural Simultaneous Speech Translation Using Alignment-Based Chunking | In simultaneous machine translation, the objective is to determine when to
produce a partial translation given a continuous stream of source words, with a
trade-off between latency and quality. We propose a neural machine translation
(NMT) model that makes dynamic decisions when to continue feeding on input or
generate output words. The model is composed of two main components: one to
dynamically decide on ending a source chunk, and another that translates the
consumed chunk. We train the components jointly and in a manner consistent with
the inference conditions. To generate chunked training data, we propose a
method that utilizes word alignment while also preserving enough context. We
compare models with bidirectional and unidirectional encoders of different
depths, both on real speech and text input. Our results on the IWSLT 2020
English-to-German task outperform a wait-k baseline by 2.6 to 3.7% BLEU
absolute.
| 2,020 | Computation and Language |
Using Large Pretrained Language Models for Answering User Queries from
Product Specifications | While buying a product from the e-commerce websites, customers generally have
a plethora of questions. From the perspective of both the e-commerce service
provider as well as the customers, there must be an effective question
answering system to provide immediate answers to the user queries. While
certain questions can only be answered after using the product, there are many
questions which can be answered from the product specification itself. Our work
takes a first step in this direction by finding out the relevant product
specifications, that can help answering the user questions. We propose an
approach to automatically create a training dataset for this problem. We
utilize recently proposed XLNet and BERT architectures for this problem and
find that they provide much better performance than the Siamese model,
previously applied for this problem. Our model gives a good performance even
when trained on one vertical and tested across different verticals.
| 2,020 | Computation and Language |
Detection of Bangla Fake News using MNB and SVM Classifier | Fake news has been coming into sight in significant numbers for numerous
business and political reasons and has become frequent in the online world.
People can get contaminated easily by these fake news for its fabricated words
which have enormous effects on the offline community. Thus, interest in
research in this area has risen. Significant research has been conducted on the
detection of fake news from English texts and other languages but a few in
Bangla Language. Our work reflects the experimental analysis on the detection
of Bangla fake news from social media as this field still requires much focus.
In this research work, we have used two supervised machine learning algorithms,
Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM) classifiers to
detect Bangla fake news with CountVectorizer and Term Frequency - Inverse
Document Frequency Vectorizer as feature extraction. Our proposed framework
detects fake news depending on the polarity of the corresponding article.
Finally, our analysis shows SVM with the linear kernel with an accuracy of
96.64% outperform MNB with an accuracy of 93.32%.
| 2,020 | Computation and Language |
SLAM-Inspired Simultaneous Contextualization and Interpreting for
Incremental Conversation Sentences | Distributed representation of words has improved the performance for many
natural language tasks. In many methods, however, only one meaning is
considered for one label of a word, and multiple meanings of polysemous words
depending on the context are rarely handled. Although research works have dealt
with polysemous words, they determine the meanings of such words according to a
batch of large documents. Hence, there are two problems with applying these
methods to sequential sentences, as in a conversation that contains ambiguous
expressions. The first problem is that the methods cannot sequentially deal
with the interdependence between context and word interpretation, in which
context is decided by word interpretations and the word interpretations are
decided by the context. Context estimation must thus be performed in parallel
to pursue multiple interpretations. The second problem is that the previous
methods use large-scale sets of sentences for offline learning of new
interpretations, and the steps of learning and inference are clearly separated.
Such methods using offline learning cannot obtain new interpretations during a
conversation. Hence, to dynamically estimate the conversation context and
interpretations of polysemous words in sequential sentences, we propose a
method of Simultaneous Contextualization And INterpreting (SCAIN) based on the
traditional Simultaneous Localization And Mapping (SLAM) algorithm. By using
the SCAIN algorithm, we can sequentially optimize the interdependence between
context and word interpretation while obtaining new interpretations online. For
experimental evaluation, we created two datasets: one from Wikipedia's
disambiguation pages and the other from real conversations. For both datasets,
the results confirmed that SCAIN could effectively achieve sequential
optimization of the interdependence and acquisition of new interpretations.
| 2,020 | Computation and Language |
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning
in NLP | Transfer learning, particularly approaches that combine multi-task learning
with pre-trained contextualized embeddings and fine-tuning, have advanced the
field of Natural Language Processing tremendously in recent years. In this
paper we present MaChAmp, a toolkit for easy fine-tuning of contextualized
embeddings in multi-task settings. The benefits of MaChAmp are its flexible
configuration options, and the support of a variety of natural language
processing tasks in a uniform toolkit, from text classification and sequence
labeling to dependency parsing, masked language modeling, and text generation.
| 2,021 | Computation and Language |
Investigating Deep Learning Approaches for Hate Speech Detection in
Social Media | The phenomenal growth on the internet has helped in empowering individual's
expressions, but the misuse of freedom of expression has also led to the
increase of various cyber crimes and anti-social activities. Hate speech is one
such issue that needs to be addressed very seriously as otherwise, this could
pose threats to the integrity of the social fabrics.
In this paper, we proposed deep learning approaches utilizing various
embeddings for detecting various types of hate speeches in social media.
Detecting hate speech from a large volume of text, especially tweets which
contains limited contextual information also poses several practical
challenges.
Moreover, the varieties in user-generated data and the presence of various
forms of hate speech makes it very challenging to identify the degree and
intention of the message. Our experiments on three publicly available datasets
of different domains shows a significant improvement in accuracy and F1-score.
| 2,020 | Computation and Language |
Beyond Leaderboards: A survey of methods for revealing weaknesses in
Natural Language Inference data and models | Recent years have seen a growing number of publications that analyse Natural
Language Inference (NLI) datasets for superficial cues, whether they undermine
the complexity of the tasks underlying those datasets and how they impact those
models that are optimised and evaluated on this data. This structured survey
provides an overview of the evolving research area by categorising reported
weaknesses in models and datasets and the methods proposed to reveal and
alleviate those weaknesses for the English language. We summarise and discuss
the findings and conclude with a set of recommendations for possible future
research directions. We hope it will be a useful resource for researchers who
propose new datasets, to have a set of tools to assess the suitability and
quality of their data to evaluate various phenomena of interest, as well as
those who develop novel architectures, to further understand the implications
of their improvements with respect to their model's acquired capabilities.
| 2,020 | Computation and Language |
The Importance of Suppressing Domain Style in Authorship Analysis | The prerequisite of many approaches to authorship analysis is a
representation of writing style. But despite decades of research, it still
remains unclear to what extent commonly used and widely accepted
representations like character trigram frequencies actually represent an
author's writing style, in contrast to more domain-specific style components or
even topic. We address this shortcoming for the first time in a novel
experimental setup of fixed authors but swapped domains between training and
testing. With this setup, we reveal that approaches using character trigram
features are highly susceptible to favor domain information when applied
without attention to domains, suffering drops of up to 55.4 percentage points
in classification accuracy under domain swapping. We further propose a new
remedy based on domain-adversarial learning and compare it to ones from the
literature based on heuristic rules. Both can work well, reducing accuracy
losses under domain swapping to 3.6% and 3.9%, respectively.
| 2,020 | Computation and Language |
Prosody leaks into the memories of words | The average predictability (aka informativity) of a word in context has been
shown to condition word duration (Seyfarth, 2014). All else being equal, words
that tend to occur in more predictable environments are shorter than words that
tend to occur in less predictable environments. One account of the
informativity effect on duration is that the acoustic details of probabilistic
reduction are stored as part of a word's mental representation. Other research
has argued that predictability effects are tied to prosodic structure in
integral ways. With the aim of assessing a potential prosodic basis for
informativity effects in speech production, this study extends past work in two
directions; it investigated informativity effects in another large language,
Mandarin Chinese, and broadened the study beyond word duration to additional
acoustic dimensions, pitch and intensity, known to index prosodic prominence.
The acoustic information of content words was extracted from a large telephone
conversation speech corpus with over 400,000 tokens and 6,000 word types spoken
by 1,655 individuals and analyzed for the effect of informativity using
frequency statistics estimated from a 431 million word subtitle corpus. Results
indicated that words with low informativity have shorter durations, replicating
the effect found in English. In addition, informativity had significant effects
on maximum pitch and intensity, two phonetic dimensions related to prosodic
prominence. Extending this interpretation, these results suggest that
predictability is closely linked to prosodic prominence, and that the lexical
representation of a word includes phonetic details associated with its average
prosodic prominence in discourse. In other words, the lexicon absorbs prosodic
influences on speech production.
| 2,021 | Computation and Language |
A Comparative Study of Lexical Substitution Approaches based on Neural
Language Models | Lexical substitution in context is an extremely powerful technology that can
be used as a backbone of various NLP applications, such as word sense
induction, lexical relation extraction, data augmentation, etc. In this paper,
we present a large-scale comparative study of popular neural language and
masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, XLNet,
applied to the task of lexical substitution. We show that already competitive
results achieved by SOTA LMs/MLMs can be further improved if information about
the target word is injected properly, and compare several target injection
methods. In addition, we provide analysis of the types of semantic relations
between the target and substitutes generated by different models providing
insights into what kind of words are really generated or given by annotators as
substitutes.
| 2,020 | Computation and Language |
Stance Prediction for Contemporary Issues: Data and Experiments | We investigate whether pre-trained bidirectional transformers with sentiment
and emotion information improve stance detection in long discussions of
contemporary issues. As a part of this work, we create a novel stance detection
dataset covering 419 different controversial issues and their related pros and
cons collected by procon.org in nonpartisan format. Experimental results show
that a shallow recurrent neural network with sentiment or emotion information
can reach competitive results compared to fine-tuned BERT with 20x fewer
parameters. We also use a simple approach that explains which input phrases
contribute to stance detection.
| 2,020 | Computation and Language |
iCapsNets: Towards Interpretable Capsule Networks for Text
Classification | Many text classification applications require models with satisfying
performance as well as good interpretability. Traditional machine learning
methods are easy to interpret but have low accuracies. The development of deep
learning models boosts the performance significantly. However, deep learning
models are typically hard to interpret. In this work, we propose interpretable
capsule networks (iCapsNets) to bridge this gap. iCapsNets use capsules to
model semantic meanings and explore novel methods to increase interpretability.
The design of iCapsNets is consistent with human intuition and enables it to
produce human-understandable interpretation results. Notably, iCapsNets can be
interpreted both locally and globally. In terms of local interpretability,
iCapsNets offer a simple yet effective method to explain the predictions for
each data sample. On the other hand, iCapsNets explore a novel way to explain
the model's general behavior, achieving global interpretability. Experimental
studies show that our iCapsNets yield meaningful local and global
interpretation results, without suffering from significant performance loss
compared to non-interpretable methods.
| 2,020 | Computation and Language |
A frame semantics based approach to comparative study of digitized
corpus | in this paper, we present a corpus linguistics based approach applied to
analyzing digitized classical multilingual novels and narrative texts, from a
semantic point of view. Digitized novels such as "the hobbit (Tolkien J. R. R.,
1937)" and "the hound of the Baskervilles (Doyle A. C. 1901-1902)", which were
widely translated to dozens of languages, provide rich materials for analyzing
languages differences from several perspectives and within a number of
disciplines like linguistics, philosophy and cognitive science. Taking motion
events conceptualization as a case study, this paper, focus on the morphologic,
syntactic, and semantic annotation process of English-Arabic aligned corpus
created from a digitized novels, in order to re-examine the linguistic
encodings of motion events in English and Arabic in terms of Frame Semantics.
The present study argues that differences in motion events conceptualization
across languages can be described with frame structure and frame-to-frame
relations.
| 2,020 | Computation and Language |
Design and Implementation of a Virtual 3D Educational Environment to
improve Deaf Education | Advances in NLP, knowledge representation and computer graphic technologies
can provide us insights into the development of educational tool for Deaf
people. Actual education materials and tools for deaf pupils present several
problems, since textbooks are designed to support normal students in the
classroom and most of them are not suitable for people with hearing
disabilities. Virtual Reality (VR) technologies appear to be a good tool and a
promising framework in the education of pupils with hearing disabilities. In
this paper, we present a current research tasks surrounding the design and
implementation of a virtual 3D educational environment based on X3D and H-Anim
standards. The system generates and animates automatically Sign language
sentence from a semantic representation that encode the whole meaning of the
Arabic input text. Some aspects and issues in Sign language generation will be
discussed, including the model of Sign representation that facilitate reuse and
reduces the time of Sign generation, conversion of semantic components to sign
features representation with regard to Sign language linguistics
characteristics and how to generate realistic smooth gestural sequences using
X3D content to performs transition between signs for natural-looking of
animated avatar. Sign language sentences were evaluated by Algerian native Deaf
people. The goal of the project is the development of a machine translation
system from Arabic to Algerian Sign Language that can be used as educational
tool for Deaf children in algerian primary schools.
| 2,020 | Computation and Language |
Constructing Explainable Opinion Graphs from Review | The Web is a major resource of both factual and subjective information. While
there are significant efforts to organize factual information into knowledge
bases, there is much less work on organizing opinions, which are abundant in
subjective data, into a structured format.
We present ExplainIt, a system that extracts and organizes opinions into an
opinion graph, which are useful for downstream applications such as generating
explainable review summaries and facilitating search over opinion phrases. In
such graphs, a node represents a set of semantically similar opinions extracted
from reviews and an edge between two nodes signifies that one node explains the
other. ExplainIt mines explanations in a supervised method and groups similar
opinions together in a weakly supervised way before combining the clusters of
opinions together with their explanation relationships into an opinion graph.
We experimentally demonstrate that the explanation relationships generated in
the opinion graph are of good quality and our labeled datasets for explanation
mining and grouping opinions are publicly available.
| 2,021 | Computation and Language |
Topic Detection and Summarization of User Reviews | A massive amount of reviews are generated daily from various platforms. It is
impossible for people to read through tons of reviews and to obtain useful
information. Automatic summarizing customer reviews thus is important for
identifying and extracting the essential information to help users to obtain
the gist of the data. However, as customer reviews are typically short,
informal, and multifaceted, it is extremely challenging to generate topic-wise
summarization.While there are several studies aims to solve this issue, they
are heuristic methods that are developed only utilizing customer reviews.
Unlike existing method, we propose an effective new summarization method by
analyzing both reviews and summaries.To do that, we first segment reviews and
summaries into individual sentiments. As the sentiments are typically short, we
combine sentiments talking about the same aspect into a single document and
apply topic modeling method to identify hidden topics among customer reviews
and summaries. Sentiment analysis is employed to distinguish positive and
negative opinions among each detected topic. A classifier is also introduced to
distinguish the writing pattern of summaries and that of customer reviews.
Finally, sentiments are selected to generate the summarization based on their
topic relevance, sentiment analysis score and the writing pattern. To test our
method, a new dataset comprising product reviews and summaries about 1028
products are collected from Amazon and CNET. Experimental results show the
effectiveness of our method compared with other methods.
| 2,020 | Computation and Language |
User Memory Reasoning for Conversational Recommendation | We study a conversational recommendation model which dynamically manages
users' past (offline) preferences and current (online) requests through a
structured and cumulative user memory knowledge graph, to allow for natural
interactions and accurate recommendations. For this study, we create a new
Memory Graph (MG) <--> Conversational Recommendation parallel corpus called
MGConvRex with 7K+ human-to-human role-playing dialogs, grounded on a
large-scale user memory bootstrapped from real-world user scenarios. MGConvRex
captures human-level reasoning over user memory and has disjoint
training/testing sets of users for zero-shot (cold-start) reasoning for
recommendation. We propose a simple yet expandable formulation for constructing
and updating the MG, and a reasoning model that predicts optimal dialog
policies and recommendation items in unconstrained graph space. The prediction
of our proposed model inherits the graph structure, providing a natural way to
explain the model's recommendation. Experiments are conducted for both offline
metrics and online simulation, showing competitive results.
| 2,020 | Computation and Language |
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text | Understanding the sentiment of a comment from a video or an image is an
essential task in many applications. Sentiment analysis of a text can be useful
for various decision-making processes. One such application is to analyse the
popular sentiments of videos on social media based on viewer comments. However,
comments from social media do not follow strict rules of grammar, and they
contain mixing of more than one language, often written in non-native scripts.
Non-availability of annotated code-mixed data for a low-resourced language like
Tamil also adds difficulty to this problem. To overcome this, we created a gold
standard Tamil-English code-switched, sentiment-annotated corpus containing
15,744 comment posts from YouTube. In this paper, we describe the process of
creating the corpus and assigning polarities. We present inter-annotator
agreement and show the results of sentiment analysis trained on this corpus as
a benchmark.
| 2,020 | Computation and Language |
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