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Prta: A System to Support the Analysis of Propaganda Techniques in the
News | Recent events, such as the 2016 US Presidential Campaign, Brexit and the
COVID-19 "infodemic", have brought into the spotlight the dangers of online
disinformation. There has been a lot of research focusing on fact-checking and
disinformation detection. However, little attention has been paid to the
specific rhetorical and psychological techniques used to convey propaganda
messages. Revealing the use of such techniques can help promote media literacy
and critical thinking, and eventually contribute to limiting the impact of
"fake news" and disinformation campaigns. Prta (Propaganda Persuasion
Techniques Analyzer) allows users to explore the articles crawled on a regular
basis by highlighting the spans in which propaganda techniques occur and to
compare them on the basis of their use of propaganda techniques. The system
further reports statistics about the use of such techniques, overall and over
time, or according to filtering criteria specified by the user based on time
interval, keywords, and/or political orientation of the media. Moreover, it
allows users to analyze any text or URL through a dedicated interface or via an
API. The system is available online: https://www.tanbih.org/prta
| 2,020 | Computation and Language |
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic
Distance Approach | It is commonly believed that knowledge of syntactic structure should improve
language modeling. However, effectively and computationally efficiently
incorporating syntactic structure into neural language models has been a
challenging topic. In this paper, we make use of a multi-task objective, i.e.,
the models simultaneously predict words as well as ground truth parse trees in
a form called "syntactic distances", where information between these two
separate objectives shares the same intermediate representation. Experimental
results on the Penn Treebank and Chinese Treebank datasets show that when
ground truth parse trees are provided as additional training signals, the model
is able to achieve lower perplexity and induce trees with better quality.
| 2,020 | Computation and Language |
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and
Adversarial Training in NLP | While there has been substantial research using adversarial attacks to
analyze NLP models, each attack is implemented in its own code repository. It
remains challenging to develop NLP attacks and utilize them to improve model
performance. This paper introduces TextAttack, a Python framework for
adversarial attacks, data augmentation, and adversarial training in NLP.
TextAttack builds attacks from four components: a goal function, a set of
constraints, a transformation, and a search method. TextAttack's modular design
enables researchers to easily construct attacks from combinations of novel and
existing components. TextAttack provides implementations of 16 adversarial
attacks from the literature and supports a variety of models and datasets,
including BERT and other transformers, and all GLUE tasks. TextAttack also
includes data augmentation and adversarial training modules for using
components of adversarial attacks to improve model accuracy and robustness.
TextAttack is democratizing NLP: anyone can try data augmentation and
adversarial training on any model or dataset, with just a few lines of code.
Code and tutorials are available at https://github.com/QData/TextAttack.
| 2,020 | Computation and Language |
Intersectional Bias in Hate Speech and Abusive Language Datasets | Algorithms are widely applied to detect hate speech and abusive language in
social media. We investigated whether the human-annotated data used to train
these algorithms are biased. We utilized a publicly available annotated Twitter
dataset (Founta et al. 2018) and classified the racial, gender, and party
identification dimensions of 99,996 tweets. The results showed that African
American tweets were up to 3.7 times more likely to be labeled as abusive, and
African American male tweets were up to 77% more likely to be labeled as
hateful compared to the others. These patterns were statistically significant
and robust even when party identification was added as a control variable. This
study provides the first systematic evidence on intersectional bias in datasets
of hate speech and abusive language.
| 2,020 | Computation and Language |
Semantic Scaffolds for Pseudocode-to-Code Generation | We propose a method for program generation based on semantic scaffolds,
lightweight structures representing the high-level semantic and syntactic
composition of a program. By first searching over plausible scaffolds then
using these as constraints for a beam search over programs, we achieve better
coverage of the search space when compared with existing techniques. We apply
our hierarchical search method to the SPoC dataset for pseudocode-to-code
generation, in which we are given line-level natural language pseudocode
annotations and aim to produce a program satisfying execution-based test cases.
By using semantic scaffolds during inference, we achieve a 10% absolute
improvement in top-100 accuracy over the previous state-of-the-art.
Additionally, we require only 11 candidates to reach the top-3000 performance
of the previous best approach when tested against unseen problems,
demonstrating a substantial improvement in efficiency.
| 2,020 | Computation and Language |
Cross-Modality Relevance for Reasoning on Language and Vision | This work deals with the challenge of learning and reasoning over language
and vision data for the related downstream tasks such as visual question
answering (VQA) and natural language for visual reasoning (NLVR). We design a
novel cross-modality relevance module that is used in an end-to-end framework
to learn the relevance representation between components of various input
modalities under the supervision of a target task, which is more generalizable
to unobserved data compared to merely reshaping the original representation
space. In addition to modeling the relevance between the textual entities and
visual entities, we model the higher-order relevance between entity relations
in the text and object relations in the image. Our proposed approach shows
competitive performance on two different language and vision tasks using public
benchmarks and improves the state-of-the-art published results. The learned
alignments of input spaces and their relevance representations by NLVR task
boost the training efficiency of VQA task.
| 2,020 | Computation and Language |
That is a Known Lie: Detecting Previously Fact-Checked Claims | The recent proliferation of "fake news" has triggered a number of responses,
most notably the emergence of several manual fact-checking initiatives. As a
result and over time, a large number of fact-checked claims have been
accumulated, which increases the likelihood that a new claim in social media or
a new statement by a politician might have already been fact-checked by some
trusted fact-checking organization, as viral claims often come back after a
while in social media, and politicians like to repeat their favorite
statements, true or false, over and over again. As manual fact-checking is very
time-consuming (and fully automatic fact-checking has credibility issues), it
is important to try to save this effort and to avoid wasting time on claims
that have already been fact-checked. Interestingly, despite the importance of
the task, it has been largely ignored by the research community so far. Here,
we aim to bridge this gap. In particular, we formulate the task and we discuss
how it relates to, but also differs from, previous work. We further create a
specialized dataset, which we release to the research community. Finally, we
present learning-to-rank experiments that demonstrate sizable improvements over
state-of-the-art retrieval and textual similarity approaches.
| 2,020 | Computation and Language |
A computational model implementing subjectivity with the 'Room Theory'.
The case of detecting Emotion from Text | This work introduces a new method to consider subjectivity and general
context dependency in text analysis and uses as example the detection of
emotions conveyed in text. The proposed method takes into account subjectivity
using a computational version of the Framework Theory by Marvin Minsky (1974)
leveraging on the Word2Vec approach to text vectorization by Mikolov et al.
(2013), used to generate distributed representation of words based on the
context where they appear. Our approach is based on three components: 1. a
framework/'room' representing the point of view; 2. a benchmark representing
the criteria for the analysis - in this case the emotion classification, from a
study of human emotions by Robert Plutchik (1980); and 3. the document to be
analyzed. By using similarity measure between words, we are able to extract the
relative relevance of the elements in the benchmark - intensities of emotions
in our case study - for the document to be analyzed. Our method provides a
measure that take into account the point of view of the entity reading the
document. This method could be applied to all the cases where evaluating
subjectivity is relevant to understand the relative value or meaning of a text.
Subjectivity can be not limited to human reactions, but it could be used to
provide a text with an interpretation related to a given domain ("room"). To
evaluate our method, we used a test case in the political domain.
| 2,021 | Computation and Language |
Automated Extraction of Socio-political Events from News (AESPEN):
Workshop and Shared Task Report | We describe our effort on automated extraction of socio-political events from
news in the scope of a workshop and a shared task we organized at Language
Resources and Evaluation Conference (LREC 2020). We believe the event
extraction studies in computational linguistics and social and political
sciences should further support each other in order to enable large scale
socio-political event information collection across sources, countries, and
languages. The event consists of regular research papers and a shared task,
which is about event sentence coreference identification (ESCI), tracks. All
submissions were reviewed by five members of the program committee. The
workshop attracted research papers related to evaluation of machine learning
methodologies, language resources, material conflict forecasting, and a shared
task participation report in the scope of socio-political event information
collection. It has shown us the volume and variety of both the data sources and
event information collection approaches related to socio-political events and
the need to fill the gap between automated text processing techniques and
requirements of social and political sciences.
| 2,020 | Computation and Language |
Large Scale Multi-Actor Generative Dialog Modeling | Non-goal oriented dialog agents (i.e. chatbots) aim to produce varying and
engaging conversations with a user; however, they typically exhibit either
inconsistent personality across conversations or the average personality of all
users. This paper addresses these issues by controlling an agent's persona upon
generation via conditioning on prior conversations of a target actor. In doing
so, we are able to utilize more abstract patterns within a person's speech and
better emulate them in generated responses. This work introduces the Generative
Conversation Control model, an augmented and fine-tuned GPT-2 language model
that conditions on past reference conversations to probabilistically model
multi-turn conversations in the actor's persona. We introduce an accompanying
data collection procedure to obtain 10.3M conversations from 6 months worth of
Reddit comments. We demonstrate that scaling model sizes from 117M to 8.3B
parameters yields an improvement from 23.14 to 13.14 perplexity on 1.7M held
out Reddit conversations. Increasing model scale yielded similar improvements
in human evaluations that measure preference of model samples to the held out
target distribution in terms of realism (31% increased to 37% preference),
style matching (37% to 42%), grammar and content quality (29% to 42%), and
conversation coherency (32% to 40%). We find that conditionally modeling past
conversations improves perplexity by 0.47 in automatic evaluations. Through
human trials we identify positive trends between conditional modeling and style
matching and outline steps to further improve persona control.
| 2,020 | Computation and Language |
INFOTABS: Inference on Tables as Semi-structured Data | In this paper, we observe that semi-structured tabulated text is ubiquitous;
understanding them requires not only comprehending the meaning of text
fragments, but also implicit relationships between them. We argue that such
data can prove as a testing ground for understanding how we reason about
information. To study this, we introduce a new dataset called INFOTABS,
comprising of human-written textual hypotheses based on premises that are
tables extracted from Wikipedia info-boxes. Our analysis shows that the
semi-structured, multi-domain and heterogeneous nature of the premises admits
complex, multi-faceted reasoning. Experiments reveal that, while human
annotators agree on the relationships between a table-hypothesis pair, several
standard modeling strategies are unsuccessful at the task, suggesting that
reasoning about tables can pose a difficult modeling challenge.
| 2,020 | Computation and Language |
Screenplay Quality Assessment: Can We Predict Who Gets Nominated? | Deciding which scripts to turn into movies is a costly and time-consuming
process for filmmakers. Thus, building a tool to aid script selection, an
initial phase in movie production, can be very beneficial. Toward that goal, in
this work, we present a method to evaluate the quality of a screenplay based on
linguistic cues. We address this in a two-fold approach: (1) we define the task
as predicting nominations of scripts at major film awards with the hypothesis
that the peer-recognized scripts should have a greater chance to succeed. (2)
based on industry opinions and narratology, we extract and integrate
domain-specific features into common classification techniques. We face two
challenges (1) scripts are much longer than other document datasets (2)
nominated scripts are limited and thus difficult to collect. However, with
narratology-inspired modeling and domain features, our approach offers clear
improvements over strong baselines. Our work provides a new approach for future
work in screenplay analysis.
| 2,020 | Computation and Language |
Response-Anticipated Memory for On-Demand Knowledge Integration in
Response Generation | Neural conversation models are known to generate appropriate but
non-informative responses in general. A scenario where informativeness can be
significantly enhanced is Conversing by Reading (CbR), where conversations take
place with respect to a given external document. In previous work, the external
document is utilized by (1) creating a context-aware document memory that
integrates information from the document and the conversational context, and
then (2) generating responses referring to the memory. In this paper, we
propose to create the document memory with some anticipated responses in mind.
This is achieved using a teacher-student framework. The teacher is given the
external document, the context, and the ground-truth response, and learns how
to build a response-aware document memory from three sources of information.
The student learns to construct a response-anticipated document memory from the
first two sources, and the teacher's insight on memory creation. Empirical
results show that our model outperforms the previous state-of-the-art for the
CbR task.
| 2,020 | Computation and Language |
Parallel Corpus Filtering via Pre-trained Language Models | Web-crawled data provides a good source of parallel corpora for training
machine translation models. It is automatically obtained, but extremely noisy,
and recent work shows that neural machine translation systems are more
sensitive to noise than traditional statistical machine translation methods. In
this paper, we propose a novel approach to filter out noisy sentence pairs from
web-crawled corpora via pre-trained language models. We measure sentence
parallelism by leveraging the multilingual capability of BERT and use the
Generative Pre-training (GPT) language model as a domain filter to balance data
domains. We evaluate the proposed method on the WMT 2018 Parallel Corpus
Filtering shared task, and on our own web-crawled Japanese-Chinese parallel
corpus. Our method significantly outperforms baselines and achieves a new
state-of-the-art. In an unsupervised setting, our method achieves comparable
performance to the top-1 supervised method. We also evaluate on a web-crawled
Japanese-Chinese parallel corpus that we make publicly available.
| 2,020 | Computation and Language |
Machine Reading Comprehension: The Role of Contextualized Language
Models and Beyond | Machine reading comprehension (MRC) aims to teach machines to read and
comprehend human languages, which is a long-standing goal of natural language
processing (NLP). With the burst of deep neural networks and the evolution of
contextualized language models (CLMs), the research of MRC has experienced two
significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on
the NLP community. In this survey, we provide a comprehensive and comparative
review on MRC covering overall research topics about 1) the origin and
development of MRC and CLM, with a particular focus on the role of CLMs; 2) the
impact of MRC and CLM to the NLP community; 3) the definition, datasets, and
evaluation of MRC; 4) general MRC architecture and technical methods in the
view of two-stage Encoder-Decoder solving architecture from the insights of the
cognitive process of humans; 5) previous highlights, emerging topics, and our
empirical analysis, among which we especially focus on what works in different
periods of MRC researches. We propose a full-view categorization and new
taxonomies on these topics. The primary views we have arrived at are that 1)
MRC boosts the progress from language processing to understanding; 2) the rapid
improvement of MRC systems greatly benefits from the development of CLMs; 3)
the theme of MRC is gradually moving from shallow text matching to cognitive
reasoning.
| 2,020 | Computation and Language |
Mitigating Gender Bias Amplification in Distribution by Posterior
Regularization | Advanced machine learning techniques have boosted the performance of natural
language processing. Nevertheless, recent studies, e.g., Zhao et al. (2017)
show that these techniques inadvertently capture the societal bias hidden in
the corpus and further amplify it. However, their analysis is conducted only on
models' top predictions. In this paper, we investigate the gender bias
amplification issue from the distribution perspective and demonstrate that the
bias is amplified in the view of predicted probability distribution over
labels. We further propose a bias mitigation approach based on posterior
regularization. With little performance loss, our method can almost remove the
bias amplification in the distribution. Our study sheds the light on
understanding the bias amplification.
| 2,020 | Computation and Language |
Smart To-Do : Automatic Generation of To-Do Items from Emails | Intelligent features in email service applications aim to increase
productivity by helping people organize their folders, compose their emails and
respond to pending tasks. In this work, we explore a new application,
Smart-To-Do, that helps users with task management over emails. We introduce a
new task and dataset for automatically generating To-Do items from emails where
the sender has promised to perform an action. We design a two-stage process
leveraging recent advances in neural text generation and sequence-to-sequence
learning, obtaining BLEU and ROUGE scores of 0:23 and 0:63 for this task. To
the best of our knowledge, this is the first work to address the problem of
composing To-Do items from emails.
| 2,020 | Computation and Language |
Reasoning with Latent Structure Refinement for Document-Level Relation
Extraction | Document-level relation extraction requires integrating information within
and across multiple sentences of a document and capturing complex interactions
between inter-sentence entities. However, effective aggregation of relevant
information in the document remains a challenging research question. Existing
approaches construct static document-level graphs based on syntactic trees,
co-references or heuristics from the unstructured text to model the
dependencies. Unlike previous methods that may not be able to capture rich
non-local interactions for inference, we propose a novel model that empowers
the relational reasoning across sentences by automatically inducing the latent
document-level graph. We further develop a refinement strategy, which enables
the model to incrementally aggregate relevant information for multi-hop
reasoning. Specifically, our model achieves an F1 score of 59.05 on a
large-scale document-level dataset (DocRED), significantly improving over the
previous results, and also yields new state-of-the-art results on the CDR and
GDA dataset. Furthermore, extensive analyses show that the model is able to
discover more accurate inter-sentence relations.
| 2,020 | Computation and Language |
Towards Hate Speech Detection at Large via Deep Generative Modeling | Hate speech detection is a critical problem in social media platforms, being
often accused for enabling the spread of hatred and igniting physical violence.
Hate speech detection requires overwhelming resources including
high-performance computing for online posts and tweets monitoring as well as
thousands of human experts for daily screening of suspected posts or tweets.
Recently, Deep Learning (DL)-based solutions have been proposed for automatic
detection of hate speech, using modest-sized training datasets of few thousands
of hate speech sequences. While these methods perform well on the specific
datasets, their ability to detect new hate speech sequences is limited and has
not been investigated. Being a data-driven approach, it is well known that DL
surpasses other methods whenever a scale-up in train dataset size and diversity
is achieved. Therefore, we first present a dataset of 1 million realistic hate
and non-hate sequences, produced by a deep generative language model. We
further utilize the generated dataset to train a well-studied DL-based hate
speech detector, and demonstrate consistent and significant performance
improvements across five public hate speech datasets. Therefore, the proposed
solution enables high sensitivity detection of a very large variety of hate
speech sequences, paving the way to a fully automatic solution.
| 2,020 | Computation and Language |
BIOMRC: A Dataset for Biomedical Machine Reading Comprehension | We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care
was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas
et al. (2018). Experiments show that simple heuristics do not perform well on
the new dataset, and that two neural MRC models that had been tested on BIOREAD
perform much better on BIOMRC, indicating that the new dataset is indeed less
noisy or at least that its task is more feasible. Non-expert human performance
is also higher on the new dataset compared to BIOREAD, and biomedical experts
perform even better. We also introduce a new BERT-based MRC model, the best
version of which substantially outperforms all other methods tested, reaching
or surpassing the accuracy of biomedical experts in some experiments. We make
the new dataset available in three different sizes, also releasing our code,
and providing a leaderboard.
| 2,020 | Computation and Language |
SueNes: A Weakly Supervised Approach to Evaluating Single-Document
Summarization via Negative Sampling | Canonical automatic summary evaluation metrics, such as ROUGE, focus on
lexical similarity which cannot well capture semantics nor linguistic quality
and require a reference summary which is costly to obtain. Recently, there have
been a growing number of efforts to alleviate either or both of the two
drawbacks. In this paper, we present a proof-of-concept study to a weakly
supervised summary evaluation approach without the presence of reference
summaries. Massive data in existing summarization datasets are transformed for
training by pairing documents with corrupted reference summaries. In
cross-domain tests, our strategy outperforms baselines with promising
improvements, and show a great advantage in gauging linguistic qualities over
all metrics.
| 2,022 | Computation and Language |
Sanskrit Segmentation Revisited | Computationally analyzing Sanskrit texts requires proper segmentation in the
initial stages. There have been various tools developed for Sanskrit text
segmentation. Of these, G\'erard Huet's Reader in the Sanskrit Heritage Engine
analyzes the input text and segments it based on the word parameters - phases
like iic, ifc, Pr, Subst, etc., and sandhi (or transition) that takes place at
the end of a word with the initial part of the next word. And it enlists all
the possible solutions differentiating them with the help of the phases. The
phases and their analyses have their use in the domain of sentential parsers.
In segmentation, though, they are not used beyond deciding whether the words
formed with the phases are morphologically valid. This paper tries to modify
the above segmenter by ignoring the phase details (except for a few cases), and
also proposes a probability function to prioritize the list of solutions to
bring up the most valid solutions at the top.
| 2,020 | Computation and Language |
Dense-Caption Matching and Frame-Selection Gating for Temporal
Localization in VideoQA | Videos convey rich information. Dynamic spatio-temporal relationships between
people/objects, and diverse multimodal events are present in a video clip.
Hence, it is important to develop automated models that can accurately extract
such information from videos. Answering questions on videos is one of the tasks
which can evaluate such AI abilities. In this paper, we propose a video
question answering model which effectively integrates multi-modal input sources
and finds the temporally relevant information to answer questions.
Specifically, we first employ dense image captions to help identify objects and
their detailed salient regions and actions, and hence give the model useful
extra information (in explicit textual format to allow easier matching) for
answering questions. Moreover, our model is also comprised of dual-level
attention (word/object and frame level), multi-head self/cross-integration for
different sources (video and dense captions), and gates which pass more
relevant information to the classifier. Finally, we also cast the frame
selection problem as a multi-label classification task and introduce two loss
functions, In-andOut Frame Score Margin (IOFSM) and Balanced Binary
Cross-Entropy (BBCE), to better supervise the model with human importance
annotations. We evaluate our model on the challenging TVQA dataset, where each
of our model components provides significant gains, and our overall model
outperforms the state-of-the-art by a large margin (74.09% versus 70.52%). We
also present several word, object, and frame level visualization studies. Our
code is publicly available at:
https://github.com/hyounghk/VideoQADenseCapFrameGate-ACL2020
| 2,020 | Computation and Language |
The Unstoppable Rise of Computational Linguistics in Deep Learning | In this paper, we trace the history of neural networks applied to natural
language understanding tasks, and identify key contributions which the nature
of language has made to the development of neural network architectures. We
focus on the importance of variable binding and its instantiation in
attention-based models, and argue that Transformer is not a sequence model but
an induced-structure model. This perspective leads to predictions of the
challenges facing research in deep learning architectures for natural language
understanding.
| 2,020 | Computation and Language |
A Survey on Temporal Reasoning for Temporal Information Extraction from
Text (Extended Abstract) | Time is deeply woven into how people perceive, and communicate about the
world. Almost unconsciously, we provide our language utterances with temporal
cues, like verb tenses, and we can hardly produce sentences without such cues.
Extracting temporal cues from text, and constructing a global temporal view
about the order of described events is a major challenge of automatic natural
language understanding. Temporal reasoning, the process of combining different
temporal cues into a coherent temporal view, plays a central role in temporal
information extraction. This article presents a comprehensive survey of the
research from the past decades on temporal reasoning for automatic temporal
information extraction from text, providing a case study on the integration of
symbolic reasoning with machine learning-based information extraction systems.
| 2,020 | Computation and Language |
A Mixture of $h-1$ Heads is Better than $h$ Heads | Multi-head attentive neural architectures have achieved state-of-the-art
results on a variety of natural language processing tasks. Evidence has shown
that they are overparameterized; attention heads can be pruned without
significant performance loss. In this work, we instead "reallocate" them -- the
model learns to activate different heads on different inputs. Drawing
connections between multi-head attention and mixture of experts, we propose the
mixture of attentive experts model (MAE). MAE is trained using a block
coordinate descent algorithm that alternates between updating (1) the
responsibilities of the experts and (2) their parameters. Experiments on
machine translation and language modeling show that MAE outperforms strong
baselines on both tasks. Particularly, on the WMT14 English to German
translation dataset, MAE improves over "transformer-base" by 0.8 BLEU, with a
comparable number of parameters. Our analysis shows that our model learns to
specialize different experts to different inputs.
| 2,020 | Computation and Language |
Deep Learning for Political Science | Political science, and social science in general, have traditionally been
using computational methods to study areas such as voting behavior, policy
making, international conflict, and international development. More recently,
increasingly available quantities of data are being combined with improved
algorithms and affordable computational resources to predict, learn, and
discover new insights from data that is large in volume and variety. New
developments in the areas of machine learning, deep learning, natural language
processing (NLP), and, more generally, artificial intelligence (AI) are opening
up new opportunities for testing theories and evaluating the impact of
interventions and programs in a more dynamic and effective way. Applications
using large volumes of structured and unstructured data are becoming common in
government and industry, and increasingly also in social science research. This
chapter offers an introduction to such methods drawing examples from political
science. Focusing on the areas where the strengths of the methods coincide with
challenges in these fields, the chapter first presents an introduction to AI
and its core technology - machine learning, with its rapidly developing
subfield of deep learning. The discussion of deep neural networks is
illustrated with the NLP tasks that are relevant to political science. The
latest advances in deep learning methods for NLP are also reviewed, together
with their potential for improving information extraction and pattern
recognition from political science texts.
| 2,020 | Computation and Language |
Validation and Normalization of DCS corpus using Sanskrit Heritage tools
to build a tagged Gold Corpus | The Digital Corpus of Sanskrit records around 650,000 sentences along with
their morphological and lexical tagging. But inconsistencies in morphological
analysis, and in providing crucial information like the segmented word, urges
the need for standardization and validation of this corpus. Automating the
validation process requires efficient analyzers which also provide the missing
information. The Sanskrit Heritage Engine's Reader produces all possible
segmentations with morphological and lexical analyses. Aligning these systems
would help us in recording the linguistic differences, which can be used to
update these systems to produce standardized results and will also provide a
Gold corpus tagged with complete morphological and lexical information along
with the segmented words. Krishna et al. (2017) aligned 115,000 sentences,
considering some of the linguistic differences. As both these systems have
evolved significantly, the alignment is done again considering all the
remaining linguistic differences between these systems. This paper describes
the modified alignment process in detail and records the additional linguistic
differences observed.
Reference: Amrith Krishna, Pavankumar Satuluri, and Pawan Goyal. 2017. A
dataset for Sanskrit word segmentation. In Proceedings of the Joint SIGHUM
Workshop on Computational Linguistics for Cultural Heritage, Social Sciences,
Humanities and Literature, page 105-114. Association for Computational
Linguistics, August.
| 2,020 | Computation and Language |
Arabic Dialect Identification in the Wild | We present QADI, an automatically collected dataset of tweets belonging to a
wide range of country-level Arabic dialects -covering 18 different countries in
the Middle East and North Africa region. Our method for building this dataset
relies on applying multiple filters to identify users who belong to different
countries based on their account descriptions and to eliminate tweets that are
either written in Modern Standard Arabic or contain inappropriate language. The
resultant dataset contains 540k tweets from 2,525 users who are evenly
distributed across 18 Arab countries. Using intrinsic evaluation, we show that
the labels of a set of randomly selected tweets are 91.5% accurate. For
extrinsic evaluation, we are able to build effective country-level dialect
identification on tweets with a macro-averaged F1-score of 60.6% across 18
classes.
| 2,020 | Computation and Language |
Document-Level Event Role Filler Extraction using Multi-Granularity
Contextualized Encoding | Few works in the literature of event extraction have gone beyond individual
sentences to make extraction decisions. This is problematic when the
information needed to recognize an event argument is spread across multiple
sentences. We argue that document-level event extraction is a difficult task
since it requires a view of a larger context to determine which spans of text
correspond to event role fillers. We first investigate how end-to-end neural
sequence models (with pre-trained language model representations) perform on
document-level role filler extraction, as well as how the length of context
captured affects the models' performance. To dynamically aggregate information
captured by neural representations learned at different levels of granularity
(e.g., the sentence- and paragraph-level), we propose a novel multi-granularity
reader. We evaluate our models on the MUC-4 event extraction dataset, and show
that our best system performs substantially better than prior work. We also
report findings on the relationship between context length and neural model
performance on the task.
| 2,020 | Computation and Language |
PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction | Relation extraction is the task of extracting semantic relations between
entities in a sentence. It is an essential part of some natural language
processing tasks such as information extraction, knowledge extraction, and
knowledge base population. The main motivations of this research stem from a
lack of a dataset for relation extraction in the Persian language as well as
the necessity of extracting knowledge from the growing big-data in the Persian
language for different applications. In this paper, we present "PERLEX" as the
first Persian dataset for relation extraction, which is an expert-translated
version of the "Semeval-2010-Task-8" dataset. Moreover, this paper addresses
Persian relation extraction utilizing state-of-the-art language-agnostic
algorithms. We employ six different models for relation extraction on the
proposed bilingual dataset, including a non-neural model (as the baseline),
three neural models, and two deep learning models fed by multilingual-BERT
contextual word representations. The experiments result in the maximum f-score
77.66% (provided by BERTEM-MTB method) as the state-of-the-art of relation
extraction in the Persian language.
| 2,020 | Computation and Language |
A Comprehensive Survey of Grammar Error Correction | Grammar error correction (GEC) is an important application aspect of natural
language processing techniques. The past decade has witnessed significant
progress achieved in GEC for the sake of increasing popularity of machine
learning and deep learning, especially in late 2010s when near human-level GEC
systems are available. However, there is no prior work focusing on the whole
recapitulation of the progress. We present the first survey in GEC for a
comprehensive retrospect of the literature in this area. We first give the
introduction of five public datasets, data annotation schema, two important
shared tasks and four standard evaluation metrics. More importantly, we discuss
four kinds of basic approaches, including statistical machine translation based
approach, neural machine translation based approach, classification based
approach and language model based approach, six commonly applied performance
boosting techniques for GEC systems and two data augmentation methods. Since
GEC is typically viewed as a sister task of machine translation, many GEC
systems are based on neural machine translation (NMT) approaches, where the
neural sequence-to-sequence model is applied. Similarly, some performance
boosting techniques are adapted from machine translation and are successfully
combined with GEC systems for enhancement on the final performance.
Furthermore, we conduct an analysis in level of basic approaches, performance
boosting techniques and integrated GEC systems based on their experiment
results respectively for more clear patterns and conclusions. Finally, we
discuss five prospective directions for future GEC researches.
| 2,020 | Computation and Language |
Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER
Identification | The PICO framework (Population, Intervention, Comparison, and Outcome) is
usually used to formulate evidence in the medical domain. The major task of
PICO extraction is to extract sentences from medical literature and classify
them into each class. However, in most circumstances, there will be more than
one evidences in an extracted sentence even it has been categorized to a
certain class. In order to address this problem, we propose a step-wise disease
Named Entity Recognition (DNER) extraction and PICO identification method. With
our method, sentences in paper title and abstract are first classified into
different classes of PICO, and medical entities are then identified and
classified into P and O. Different kinds of deep learning frameworks are used
and experimental results show that our method will achieve high performance and
fine-grained extraction results comparing with conventional PICO extraction
works.
| 2,020 | Computation and Language |
CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and
Context-Dependent Word Representations | This paper describes the winning contribution to SemEval-2020 Task 1:
Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG
Student Intern. We present an ensemble model that makes predictions based on
context-free and context-dependent word representations. The key findings are
that (1) context-free word representations are a powerful and robust baseline,
(2) a sentence classification objective can be used to obtain useful
context-dependent word representations, and (3) combining those representations
increases performance on some datasets while decreasing performance on others.
| 2,020 | Computation and Language |
POSNoise: An Effective Countermeasure Against Topic Biases in Authorship
Analysis | Authorship verification (AV) is a fundamental research task in digital text
forensics, which addresses the problem of whether two texts were written by the
same person. In recent years, a variety of AV methods have been proposed that
focus on this problem and can be divided into two categories: The first
category refers to such methods that are based on explicitly defined features,
where one has full control over which features are considered and what they
actually represent. The second category, on the other hand, relates to such AV
methods that are based on implicitly defined features, where no control
mechanism is involved, so that any character sequence in a text can serve as a
potential feature. However, AV methods belonging to the second category bear
the risk that the topic of the texts may bias their classification predictions,
which in turn may lead to misleading conclusions regarding their results. To
tackle this problem, we propose a preprocessing technique called POSNoise,
which effectively masks topic-related content in a given text. In this way, AV
methods are forced to focus on such text units that are more related to the
writing style. Our empirical evaluation based on six AV methods (falling into
the second category) and seven corpora shows that POSNoise leads to better
results compared to a well-known topic masking approach in 34 out of 42 cases,
with an increase in accuracy of up to 10%.
| 2,021 | Computation and Language |
Dynamic Programming Encoding for Subword Segmentation in Neural Machine
Translation | This paper introduces Dynamic Programming Encoding (DPE), a new segmentation
algorithm for tokenizing sentences into subword units. We view the subword
segmentation of output sentences as a latent variable that should be
marginalized out for learning and inference. A mixed character-subword
transformer is proposed, which enables exact log marginal likelihood estimation
and exact MAP inference to find target segmentations with maximum posterior
probability. DPE uses a lightweight mixed character-subword transformer as a
means of pre-processing parallel data to segment output sentences using dynamic
programming. Empirical results on machine translation suggest that DPE is
effective for segmenting output sentences and can be combined with BPE dropout
for stochastic segmentation of source sentences. DPE achieves an average
improvement of 0.9 BLEU over BPE (Sennrich et al., 2016) and an average
improvement of 0.55 BLEU over BPE dropout (Provilkov et al., 2019) on several
WMT datasets including English <=> (German, Romanian, Estonian, Finnish,
Hungarian).
| 2,020 | Computation and Language |
Improving Aspect-Level Sentiment Analysis with Aspect Extraction | Aspect-based sentiment analysis (ABSA), a popular research area in NLP has
two distinct parts -- aspect extraction (AE) and labeling the aspects with
sentiment polarity (ALSA). Although distinct, these two tasks are highly
correlated. The work primarily hypothesize that transferring knowledge from a
pre-trained AE model can benefit the performance of ALSA models. Based on this
hypothesis, word embeddings are obtained during AE and subsequently, feed that
to the ALSA model. Empirically, this work show that the added information
significantly improves the performance of three different baseline ALSA models
on two distinct domains. This improvement also translates well across domains
between AE and ALSA tasks.
| 2,020 | Computation and Language |
Understanding and Detecting Dangerous Speech in Social Media | Social media communication has become a significant part of daily activity in
modern societies. For this reason, ensuring safety in social media platforms is
a necessity. Use of dangerous language such as physical threats in online
environments is a somewhat rare, yet remains highly important. Although several
works have been performed on the related issue of detecting offensive and
hateful language, dangerous speech has not previously been treated in any
significant way. Motivated by these observations, we report our efforts to
build a labeled dataset for dangerous speech. We also exploit our dataset to
develop highly effective models to detect dangerous content. Our best model
performs at 59.60% macro F1, significantly outperforming a competitive
baseline.
| 2,020 | Computation and Language |
Neural Machine Translation for South Africa's Official Languages | Recent advances in neural machine translation (NMT) have led to
state-of-the-art results for many European-based translation tasks. However,
despite these advances, there is has been little focus in applying these
methods to African languages. In this paper, we seek to address this gap by
creating an NMT benchmark BLEU score between English and the ten remaining
official languages in South Africa.
| 2,020 | Computation and Language |
ImpactCite: An XLNet-based method for Citation Impact Analysis | Citations play a vital role in understanding the impact of scientific
literature. Generally, citations are analyzed quantitatively whereas
qualitative analysis of citations can reveal deeper insights into the impact of
a scientific artifact in the community. Therefore, citation impact analysis
(which includes sentiment and intent classification) enables us to quantify the
quality of the citations which can eventually assist us in the estimation of
ranking and impact. The contribution of this paper is two-fold. First, we
benchmark the well-known language models like BERT and ALBERT along with
several popular networks for both tasks of sentiment and intent classification.
Second, we provide ImpactCite, which is XLNet-based method for citation impact
analysis. All evaluations are performed on a set of publicly available citation
analysis datasets. Evaluation results reveal that ImpactCite achieves a new
state-of-the-art performance for both citation intent and sentiment
classification by outperforming the existing approaches by 3.44% and 1.33% in
F1-score. Therefore, we emphasize ImpactCite (XLNet-based solution) for both
tasks to better understand the impact of a citation. Additional efforts have
been performed to come up with CSC-Clean corpus, which is a clean and reliable
dataset for citation sentiment classification.
| 2,021 | Computation and Language |
Comparative Analysis of Text Classification Approaches in Electronic
Health Records | Text classification tasks which aim at harvesting and/or organizing
information from electronic health records are pivotal to support clinical and
translational research. However these present specific challenges compared to
other classification tasks, notably due to the particular nature of the medical
lexicon and language used in clinical records. Recent advances in embedding
methods have shown promising results for several clinical tasks, yet there is
no exhaustive comparison of such approaches with other commonly used word
representations and classification models. In this work, we analyse the impact
of various word representations, text pre-processing and classification
algorithms on the performance of four different text classification tasks. The
results show that traditional approaches, when tailored to the specific
language and structure of the text inherent to the classification task, can
achieve or exceed the performance of more recent ones based on contextual
embeddings such as BERT.
| 2,020 | Computation and Language |
Cyberbullying Detection with Fairness Constraints | Cyberbullying is a widespread adverse phenomenon among online social
interactions in today's digital society. While numerous computational studies
focus on enhancing the cyberbullying detection performance of machine learning
algorithms, proposed models tend to carry and reinforce unintended social
biases. In this study, we try to answer the research question of "Can we
mitigate the unintended bias of cyberbullying detection models by guiding the
model training with fairness constraints?". For this purpose, we propose a
model training scheme that can employ fairness constraints and validate our
approach with different datasets. We demonstrate that various types of
unintended biases can be successfully mitigated without impairing the model
quality. We believe our work contributes to the pursuit of unbiased,
transparent, and ethical machine learning solutions for cyber-social health.
| 2,021 | Computation and Language |
CrisisBERT: a Robust Transformer for Crisis Classification and
Contextual Crisis Embedding | Classification of crisis events, such as natural disasters, terrorist attacks
and pandemics, is a crucial task to create early signals and inform relevant
parties for spontaneous actions to reduce overall damage. Despite crisis such
as natural disasters can be predicted by professional institutions, certain
events are first signaled by civilians, such as the recent COVID-19 pandemics.
Social media platforms such as Twitter often exposes firsthand signals on such
crises through high volume information exchange over half a billion tweets
posted daily. Prior works proposed various crisis embeddings and classification
using conventional Machine Learning and Neural Network models. However, none of
the works perform crisis embedding and classification using state of the art
attention-based deep neural networks models, such as Transformers and
document-level contextual embeddings. This work proposes CrisisBERT, an
end-to-end transformer-based model for two crisis classification tasks, namely
crisis detection and crisis recognition, which shows promising results across
accuracy and f1 scores. The proposed model also demonstrates superior
robustness over benchmark, as it shows marginal performance compromise while
extending from 6 to 36 events with only 51.4% additional data points. We also
proposed Crisis2Vec, an attention-based, document-level contextual embedding
architecture for crisis embedding, which achieve better performance than
conventional crisis embedding methods such as Word2Vec and GloVe. To the best
of our knowledge, our works are first to propose using transformer-based crisis
classification and document-level contextual crisis embedding in the
literature.
| 2,020 | Computation and Language |
schuBERT: Optimizing Elements of BERT | Transformers \citep{vaswani2017attention} have gradually become a key
component for many state-of-the-art natural language representation models. A
recent Transformer based model- BERT \citep{devlin2018bert} achieved
state-of-the-art results on various natural language processing tasks,
including GLUE, SQuAD v1.1, and SQuAD v2.0. This model however is
computationally prohibitive and has a huge number of parameters. In this work
we revisit the architecture choices of BERT in efforts to obtain a lighter
model. We focus on reducing the number of parameters yet our methods can be
applied towards other objectives such FLOPs or latency. We show that much
efficient light BERT models can be obtained by reducing algorithmically chosen
correct architecture design dimensions rather than reducing the number of
Transformer encoder layers. In particular, our schuBERT gives $6.6\%$ higher
average accuracy on GLUE and SQuAD datasets as compared to BERT with three
encoder layers while having the same number of parameters.
| 2,020 | Computation and Language |
SCAT: Second Chance Autoencoder for Textual Data | We present a k-competitive learning approach for textual autoencoders named
Second Chance Autoencoder (SCAT). SCAT selects the $k$ largest and smallest
positive activations as the winner neurons, which gain the activation values of
the loser neurons during the learning process, and thus focus on retrieving
well-representative features for topics. Our experiments show that SCAT
achieves outstanding performance in classification, topic modeling, and
document visualization compared to LDA, K-Sparse, NVCTM, and KATE.
| 2,020 | Computation and Language |
Detecting Adverse Drug Reactions from Twitter through Domain-Specific
Preprocessing and BERT Ensembling | The automation of adverse drug reaction (ADR) detection in social media would
revolutionize the practice of pharmacovigilance, supporting drug regulators,
the pharmaceutical industry and the general public in ensuring the safety of
the drugs prescribed in daily practice. Following from the published
proceedings of the Social Media Mining for Health (SMM4H) Applications Workshop
& Shared Task in August 2019, we aimed to develop a deep learning model to
classify ADRs within Twitter tweets that contain drug mentions. Our approach
involved fine-tuning $BERT_{LARGE}$ and two domain-specific BERT
implementations, $BioBERT$ and $Bio + clinicalBERT$, applying a domain-specific
preprocessor, and developing a max-prediction ensembling approach. Our final
model resulted in state-of-the-art performance on both $F_1$-score (0.6681) and
recall (0.7700) outperforming all models submitted in SMM4H 2019 and during
post-evaluation to date.
| 2,020 | Computation and Language |
A Rate-Distortion view of human pragmatic reasoning | What computational principles underlie human pragmatic reasoning? A prominent
approach to pragmatics is the Rational Speech Act (RSA) framework, which
formulates pragmatic reasoning as probabilistic speakers and listeners
recursively reasoning about each other. While RSA enjoys broad empirical
support, it is not yet clear whether the dynamics of such recursive reasoning
may be governed by a general optimization principle. Here, we present a novel
analysis of the RSA framework that addresses this question. First, we show that
RSA recursion implements an alternating maximization for optimizing a tradeoff
between expected utility and communicative effort. On that basis, we study the
dynamics of RSA recursion and disconfirm the conjecture that expected utility
is guaranteed to improve with recursion depth. Second, we show that RSA can be
grounded in Rate-Distortion theory, while maintaining a similar ability to
account for human behavior and avoiding a bias of RSA toward random utterance
production. This work furthers the mathematical understanding of RSA models,
and suggests that general information-theoretic principles may give rise to
human pragmatic reasoning.
| 2,020 | Computation and Language |
Explaining Black Box Predictions and Unveiling Data Artifacts through
Influence Functions | Modern deep learning models for NLP are notoriously opaque. This has
motivated the development of methods for interpreting such models, e.g., via
gradient-based saliency maps or the visualization of attention weights. Such
approaches aim to provide explanations for a particular model prediction by
highlighting important words in the corresponding input text. While this might
be useful for tasks where decisions are explicitly influenced by individual
tokens in the input, we suspect that such highlighting is not suitable for
tasks where model decisions should be driven by more complex reasoning. In this
work, we investigate the use of influence functions for NLP, providing an
alternative approach to interpreting neural text classifiers. Influence
functions explain the decisions of a model by identifying influential training
examples. Despite the promise of this approach, influence functions have not
yet been extensively evaluated in the context of NLP, a gap addressed by this
work. We conduct a comparison between influence functions and common
word-saliency methods on representative tasks. As suspected, we find that
influence functions are particularly useful for natural language inference, a
task in which 'saliency maps' may not have clear interpretation. Furthermore,
we develop a new quantitative measure based on influence functions that can
reveal artifacts in training data.
| 2,020 | Computation and Language |
A Category Theory Approach to Interoperability | In this article, we propose a Category Theory approach to (syntactic)
interoperability between linguistic tools. The resulting category consists of
textual documents, including any linguistic annotations, NLP tools that analyze
texts and add additional linguistic information, and format converters. Format
converters are necessary to make the tools both able to read and to produce
different output formats, which is the key to interoperability. The idea behind
this document is the parallelism between the concepts of composition and
associativity in Category Theory with the NLP pipelines. We show how pipelines
of linguistic tools can be modeled into the conceptual framework of Category
Theory and we successfully apply this method to two real-life examples.
| 2,020 | Computation and Language |
Mitigating Gender Bias in Machine Learning Data Sets | Artificial Intelligence has the capacity to amplify and perpetuate societal
biases and presents profound ethical implications for society. Gender bias has
been identified in the context of employment advertising and recruitment tools,
due to their reliance on underlying language processing and recommendation
algorithms. Attempts to address such issues have involved testing learned
associations, integrating concepts of fairness to machine learning and
performing more rigorous analysis of training data. Mitigating bias when
algorithms are trained on textual data is particularly challenging given the
complex way gender ideology is embedded in language. This paper proposes a
framework for the identification of gender bias in training data for machine
learning.The work draws upon gender theory and sociolinguistics to
systematically indicate levels of bias in textual training data and associated
neural word embedding models, thus highlighting pathways for both removing bias
from training data and critically assessing its impact.
| 2,020 | Computation and Language |
DRTS Parsing with Structure-Aware Encoding and Decoding | Discourse representation tree structure (DRTS) parsing is a novel semantic
parsing task which has been concerned most recently. State-of-the-art
performance can be achieved by a neural sequence-to-sequence model, treating
the tree construction as an incremental sequence generation problem. Structural
information such as input syntax and the intermediate skeleton of the partial
output has been ignored in the model, which could be potentially useful for the
DRTS parsing. In this work, we propose a structural-aware model at both the
encoder and decoder phase to integrate the structural information, where graph
attention network (GAT) is exploited for effectively modeling. Experimental
results on a benchmark dataset show that our proposed model is effective and
can obtain the best performance in the literature.
| 2,020 | Computation and Language |
NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal
Classifiers to tackle Internet Humor | The paper describes the systems submitted to SemEval-2020 Task 8: Memotion by
the `NIT-Agartala-NLP-Team'. A dataset of 8879 memes was made available by the
task organizers to train and test our models. Our systems include a Logistic
Regression baseline, a BiLSTM + Attention-based learner and a transfer learning
approach with BERT. For the three sub-tasks A, B and C, we attained ranks
24/33, 11/29 and 15/26, respectively. We highlight our difficulties in
harnessing image information as well as some techniques and handcrafted
features we employ to overcome these issues. We also discuss various modelling
issues and theorize possible solutions and reasons as to why these problems
persist.
| 2,020 | Computation and Language |
4chan & 8chan embeddings | We have collected over 30M messages from the publicly available /pol/ message
boards on 4chan and 8chan, and compiled them into a model of toxic language
use. The trained word embeddings (0.4GB) are released for free and may be
useful for further study on toxic discourse or to boost hate speech detection
systems: https://textgain.com/8chan.
| 2,020 | Computation and Language |
Multi-agent Communication meets Natural Language: Synergies between
Functional and Structural Language Learning | We present a method for combining multi-agent communication and traditional
data-driven approaches to natural language learning, with an end goal of
teaching agents to communicate with humans in natural language. Our starting
point is a language model that has been trained on generic, not task-specific
language data. We then place this model in a multi-agent self-play environment
that generates task-specific rewards used to adapt or modulate the model,
turning it into a task-conditional language model. We introduce a new way for
combining the two types of learning based on the idea of reranking language
model samples, and show that this method outperforms others in communicating
with humans in a visual referential communication task. Finally, we present a
taxonomy of different types of language drift that can occur alongside a set of
measures to detect them.
| 2,020 | Computation and Language |
ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured
Webpages | In many documents, such as semi-structured webpages, textual semantics are
augmented with additional information conveyed using visual elements including
layout, font size, and color. Prior work on information extraction from
semi-structured websites has required learning an extraction model specific to
a given template via either manually labeled or distantly supervised data from
that template. In this work, we propose a solution for "zero-shot" open-domain
relation extraction from webpages with a previously unseen template, including
from websites with little overlap with existing sources of knowledge for
distant supervision and websites in entirely new subject verticals. Our model
uses a graph neural network-based approach to build a rich representation of
text fields on a webpage and the relationships between them, enabling
generalization to new templates. Experiments show this approach provides a 31%
F1 gain over a baseline for zero-shot extraction in a new subject vertical.
| 2,020 | Computation and Language |
Distilling neural networks into skipgram-level decision lists | Several previous studies on explanation for recurrent neural networks focus
on approaches that find the most important input segments for a network as its
explanations. In that case, the manner in which these input segments combine
with each other to form an explanatory pattern remains unknown. To overcome
this, some previous work tries to find patterns (called rules) in the data that
explain neural outputs. However, their explanations are often insensitive to
model parameters, which limits the scalability of text explanations. To
overcome these limitations, we propose a pipeline to explain RNNs by means of
decision lists (also called rules) over skipgrams. For evaluation of
explanations, we create a synthetic sepsis-identification dataset, as well as
apply our technique on additional clinical and sentiment analysis datasets. We
find that our technique persistently achieves high explanation fidelity and
qualitatively interpretable rules.
| 2,020 | Computation and Language |
Named Entity Recognition as Dependency Parsing | Named Entity Recognition (NER) is a fundamental task in Natural Language
Processing, concerned with identifying spans of text expressing references to
entities. NER research is often focused on flat entities only (flat NER),
ignoring the fact that entity references can be nested, as in [Bank of [China]]
(Finkel and Manning, 2009). In this paper, we use ideas from graph-based
dependency parsing to provide our model a global view on the input via a
biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of
start and end tokens in a sentence which we use to explore all spans, so that
the model is able to predict named entities accurately. We show that the model
works well for both nested and flat NER through evaluation on 8 corpora and
achieving SoTA performance on all of them, with accuracy gains of up to 2.2
percentage points.
| 2,020 | Computation and Language |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling | Sequence labeling systems should perform reliably not only under ideal
conditions but also with corrupted inputs - as these systems often process
user-generated text or follow an error-prone upstream component. To this end,
we formulate the noisy sequence labeling problem, where the input may undergo
an unknown noising process and propose two Noise-Aware Training (NAT)
objectives that improve robustness of sequence labeling performed on perturbed
input: Our data augmentation method trains a neural model using a mixture of
clean and noisy samples, whereas our stability training algorithm encourages
the model to create a noise-invariant latent representation. We employ a
vanilla noise model at training time. For evaluation, we use both the original
data and its variants perturbed with real OCR errors and misspellings.
Extensive experiments on English and German named entity recognition benchmarks
confirmed that NAT consistently improved robustness of popular sequence
labeling models, preserving accuracy on the original input. We make our code
and data publicly available for the research community.
| 2,020 | Computation and Language |
Estimating predictive uncertainty for rumour verification models | The inability to correctly resolve rumours circulating online can have
harmful real-world consequences. We present a method for incorporating model
and data uncertainty estimates into natural language processing models for
automatic rumour verification. We show that these estimates can be used to
filter out model predictions likely to be erroneous, so that these difficult
instances can be prioritised by a human fact-checker. We propose two methods
for uncertainty-based instance rejection, supervised and unsupervised. We also
show how uncertainty estimates can be used to interpret model performance as a
rumour unfolds.
| 2,020 | Computation and Language |
Pre-training technique to localize medical BERT and enhance biomedical
BERT | Pre-training large-scale neural language models on raw texts has made a
significant contribution to improving transfer learning in natural language
processing (NLP). With the introduction of transformer-based language models,
such as bidirectional encoder representations from transformers (BERT), the
performance of information extraction from a free text by NLP has significantly
improved for both the general domain and medical domain; however, it is
difficult to train specific BERT models that perform well for domains in which
there are few publicly available databases of high quality and large size. We
hypothesized that this problem can be addressed by up-sampling a
domain-specific corpus and using it for pre-training with a larger corpus in a
balanced manner. Our proposed method consists of a single intervention with one
option: simultaneous pre-training after up-sampling and amplified vocabulary.
We conducted three experiments and evaluated the resulting products. We
confirmed that our Japanese medical BERT outperformed conventional baselines
and the other BERT models in terms of the medical document classification task
and that our English BERT pre-trained using both the general and medical-domain
corpora performed sufficiently well for practical use in terms of the
biomedical language understanding evaluation (BLUE) benchmark. Moreover, our
enhanced biomedical BERT model, in which clinical notes were not used during
pre-training, showed that both the clinical and biomedical scores of the BLUE
benchmark were 0.3 points above that of the ablation model trained without our
proposed method. Well-balanced pre-training by up-sampling instances derived
from a corpus appropriate for the target task allows us to construct a
high-performance BERT model.
| 2,021 | Computation and Language |
VirAAL: Virtual Adversarial Active Learning For NLU | This paper presents VirAAL, an Active Learning framework based on Adversarial
Training. VirAAL aims to reduce the effort of annotation in Natural Language
Understanding (NLU). VirAAL is based on Virtual Adversarial Training (VAT), a
semi-supervised approach that regularizes the model through Local
Distributional Smoothness. With that, adversarial perturbations are added to
the inputs making the posterior distribution more consistent. Therefore,
entropy-based Active Learning becomes robust by querying more informative
samples without requiring additional components. The first set of experiments
studies the impact of an adapted VAT for joint-NLU tasks within low labeled
data regimes. The second set shows the effect of VirAAL in an Active Learning
(AL) process. Results demonstrate that VAT is robust even on multi-task
training, where the adversarial noise is computed from multiple loss functions.
Substantial improvements are observed with entropy-based AL with VirAAL for
querying data to annotate. VirAAL is an inexpensive method in terms of AL
computation with a positive impact on data sampling. Furthermore, VirAAL
decreases annotations in AL up to 80% and shows improvements over existing data
augmentation methods. The code is publicly available.
| 2,021 | Computation and Language |
OSACT4 Shared Task on Offensive Language Detection: Intensive
Preprocessing-Based Approach | The preprocessing phase is one of the key phases within the text
classification pipeline. This study aims at investigating the impact of the
preprocessing phase on text classification, specifically on offensive language
and hate speech classification for Arabic text. The Arabic language used in
social media is informal and written using Arabic dialects, which makes the
text classification task very complex. Preprocessing helps in dimensionality
reduction and removing useless content. We apply intensive preprocessing
techniques to the dataset before processing it further and feeding it into the
classification model. An intensive preprocessing-based approach demonstrates
its significant impact on offensive language detection and hate speech
detection shared tasks of the fourth workshop on Open-Source Arabic Corpora and
Corpora Processing Tools (OSACT). Our team wins the third place (3rd) in the
Sub-Task A Offensive Language Detection division and wins the first place (1st)
in the Sub-Task B Hate Speech Detection division, with an F1 score of 89% and
95%, respectively, by providing the state-of-the-art performance in terms of
F1, accuracy, recall, and precision for Arabic hate speech detection.
| 2,020 | Computation and Language |
A chatbot architecture for promoting youth resilience | E-health technologies have the potential to provide scalable and accessible
interventions for youth mental health. As part of a developing an ecosystem of
e-screening and e-therapy tools for New Zealand young people, a dialog agent,
Headstrong, has been designed to promote resilience with methods grounded in
cognitive behavioral therapy and positive psychology. This paper describes the
architecture underlying the chatbot. The architecture supports a range of over
20 activities delivered in a 4-week program by relatable personas. The
architecture provides a visual authoring interface to its content management
system. In addition to supporting the original adolescent resilience chatbot,
the architecture has been reused to create a 3-week 'stress-detox' intervention
for undergraduates, and subsequently for a chatbot to support young people with
the impacts of the COVID-19 pandemic, with all three systems having been used
in field trials. The Headstrong architecture illustrates the feasibility of
creating a domain-focused authoring environment in the context of e-therapy
that supports non-technical expert input and rapid deployment.
| 2,020 | Computation and Language |
Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical
Analysis of System-wise Evaluation | There is a growing interest in developing goal-oriented dialog systems which
serve users in accomplishing complex tasks through multi-turn conversations.
Although many methods are devised to evaluate and improve the performance of
individual dialog components, there is a lack of comprehensive empirical study
on how different components contribute to the overall performance of a dialog
system. In this paper, we perform a system-wise evaluation and present an
empirical analysis on different types of dialog systems which are composed of
different modules in different settings. Our results show that (1) a pipeline
dialog system trained using fine-grained supervision signals at different
component levels often obtains better performance than the systems that use
joint or end-to-end models trained on coarse-grained labels, (2)
component-wise, single-turn evaluation results are not always consistent with
the overall performance of a dialog system, and (3) despite the discrepancy
between simulators and human users, simulated evaluation is still a valid
alternative to the costly human evaluation especially in the early stage of
development.
| 2,020 | Computation and Language |
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network
Language Model | Videos uploaded on social media are often accompanied with textual
descriptions. In building automatic speech recognition (ASR) systems for
videos, we can exploit the contextual information provided by such video
metadata. In this paper, we explore ASR lattice rescoring by selectively
attending to the video descriptions. We first use an attention based method to
extract contextual vector representations of video metadata, and use these
representations as part of the inputs to a neural language model during lattice
rescoring. Secondly, we propose a hybrid pointer network approach to explicitly
interpolate the word probabilities of the word occurrences in metadata. We
perform experimental evaluations on both language modeling and ASR tasks, and
demonstrate that both proposed methods provide performance improvements by
selectively leveraging the video metadata.
| 2,020 | Computation and Language |
Spelling Error Correction with Soft-Masked BERT | Spelling error correction is an important yet challenging task because a
satisfactory solution of it essentially needs human-level language
understanding ability. Without loss of generality we consider Chinese spelling
error correction (CSC) in this paper. A state-of-the-art method for the task
selects a character from a list of candidates for correction (including
non-correction) at each position of the sentence on the basis of BERT, the
language representation model. The accuracy of the method can be sub-optimal,
however, because BERT does not have sufficient capability to detect whether
there is an error at each position, apparently due to the way of pre-training
it using mask language modeling. In this work, we propose a novel neural
architecture to address the aforementioned issue, which consists of a network
for error detection and a network for error correction based on BERT, with the
former being connected to the latter with what we call soft-masking technique.
Our method of using `Soft-Masked BERT' is general, and it may be employed in
other language detection-correction problems. Experimental results on two
datasets demonstrate that the performance of our proposed method is
significantly better than the baselines including the one solely based on BERT.
| 2,020 | Computation and Language |
Cross-lingual Transfer of Sentiment Classifiers | Word embeddings represent words in a numeric space so that semantic relations
between words are represented as distances and directions in the vector space.
Cross-lingual word embeddings transform vector spaces of different languages so
that similar words are aligned. This is done by constructing a mapping between
vector spaces of two languages or learning a joint vector space for multiple
languages. Cross-lingual embeddings can be used to transfer machine learning
models between languages, thereby compensating for insufficient data in
less-resourced languages. We use cross-lingual word embeddings to transfer
machine learning prediction models for Twitter sentiment between 13 languages.
We focus on two transfer mechanisms that recently show superior transfer
performance. The first mechanism uses the trained models whose input is the
joint numerical space for many languages as implemented in the LASER library.
The second mechanism uses large pretrained multilingual BERT language models.
Our experiments show that the transfer of models between similar languages is
sensible, even with no target language data. The performance of cross-lingual
models obtained with the multilingual BERT and LASER library is comparable, and
the differences are language-dependent. The transfer with CroSloEngual BERT,
pretrained on only three languages, is superior on these and some closely
related languages.
| 2,021 | Computation and Language |
Adaptive Transformers for Learning Multimodal Representations | The usage of transformers has grown from learning about language semantics to
forming meaningful visiolinguistic representations. These architectures are
often over-parametrized, requiring large amounts of computation. In this work,
we extend adaptive approaches to learn more about model interpretability and
computational efficiency. Specifically, we study attention spans, sparse, and
structured dropout methods to help understand how their attention mechanism
extends for vision and language tasks. We further show that these approaches
can help us learn more about how the network perceives the complexity of input
sequences, sparsity preferences for different modalities, and other related
phenomena.
| 2,020 | Computation and Language |
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse
COVID-19 Content on Twitter | In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based
model, pretrained on a large corpus of Twitter messages on the topic of
COVID-19. Our model shows a 10-30% marginal improvement compared to its base
model, BERT-Large, on five different classification datasets. The largest
improvements are on the target domain. Pretrained transformer models, such as
CT-BERT, are trained on a specific target domain and can be used for a wide
variety of natural language processing tasks, including classification,
question-answering and chatbots. CT-BERT is optimised to be used on COVID-19
content, in particular social media posts from Twitter.
| 2,020 | Computation and Language |
Corpus and Models for Lemmatisation and POS-tagging of Classical French
Theatre | This paper describes the process of building an annotated corpus and training
models for classical French literature, with a focus on theatre, and
particularly comedies in verse. It was originally developed as a preliminary
step to the stylometric analyses presented in Cafiero and Camps [2019]. The use
of a recent lemmatiser based on neural networks and a CRF tagger allows to
achieve accuracies beyond the current state-of-the art on the in-domain test,
and proves to be robust during out-of-domain tests, i.e.up to 20th c.novels.
| 2,021 | Computation and Language |
Parallel Data Augmentation for Formality Style Transfer | The main barrier to progress in the task of Formality Style Transfer is the
inadequacy of training data. In this paper, we study how to augment parallel
data and propose novel and simple data augmentation methods for this task to
obtain useful sentence pairs with easily accessible models and systems.
Experiments demonstrate that our augmented parallel data largely helps improve
formality style transfer when it is used to pre-train the model, leading to the
state-of-the-art results in the GYAFC benchmark dataset.
| 2,020 | Computation and Language |
Neural Entity Linking on Technical Service Tickets | Entity linking, the task of mapping textual mentions to known entities, has
recently been tackled using contextualized neural networks. We address the
question whether these results -- reported for large, high-quality datasets
such as Wikipedia -- transfer to practical business use cases, where labels are
scarce, text is low-quality, and terminology is highly domain-specific. Using
an entity linking model based on BERT, a popular transformer network in natural
language processing, we show that a neural approach outperforms and complements
hand-coded heuristics, with improvements of about 20% top-1 accuracy. Also, the
benefits of transfer learning on a large corpus are demonstrated, while
fine-tuning proves difficult. Finally, we compare different BERT-based
architectures and show that a simple sentence-wise encoding (Bi-Encoder) offers
a fast yet efficient search in practice.
| 2,020 | Computation and Language |
Challenges in Emotion Style Transfer: An Exploration with a Lexical
Substitution Pipeline | We propose the task of emotion style transfer, which is particularly
challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise)
are on the fence between content and style. To understand the particular
difficulties of this task, we design a transparent emotion style transfer
pipeline based on three steps: (1) select the words that are promising to be
substituted to change the emotion (with a brute-force approach and selection
based on the attention mechanism of an emotion classifier), (2) find sets of
words as candidates for substituting the words (based on lexical and
distributional semantics), and (3) select the most promising combination of
substitutions with an objective function which consists of components for
content (based on BERT sentence embeddings), emotion (based on an emotion
classifier), and fluency (based on a neural language model). This comparably
straight-forward setup enables us to explore the task and understand in what
cases lexical substitution can vary the emotional load of texts, how changes in
content and style interact and if they are at odds. We further evaluate our
pipeline quantitatively in an automated and an annotation study based on Tweets
and find, indeed, that simultaneous adjustments of content and emotion are
conflicting objectives: as we show in a qualitative analysis motivated by
Scherer's emotion component model, this is particularly the case for implicit
emotion expressions based on cognitive appraisal or descriptions of bodily
reactions.
| 2,020 | Computation and Language |
Analyzing Temporal Relationships between Trending Terms on Twitter and
Urban Dictionary Activity | As an online, crowd-sourced, open English-language slang dictionary, the
Urban Dictionary platform contains a wealth of opinions, jokes, and definitions
of terms, phrases, acronyms, and more. However, it is unclear exactly how
activity on this platform relates to larger conversations happening elsewhere
on the web, such as discussions on larger, more popular social media platforms.
In this research, we study the temporal activity trends on Urban Dictionary and
provide the first analysis of how this activity relates to content being
discussed on a major social network: Twitter. By collecting the whole of Urban
Dictionary, as well as a large sample of tweets over seven years, we explore
the connections between the words and phrases that are defined and searched for
on Urban Dictionary and the content that is talked about on Twitter. Through a
series of cross-correlation calculations, we identify cases in which Urban
Dictionary activity closely reflects the larger conversation happening on
Twitter. Then, we analyze the types of terms that have a stronger connection to
discussions on Twitter, finding that Urban Dictionary activity that is
positively correlated with Twitter is centered around terms related to memes,
popular public figures, and offline events. Finally, We explore the
relationship between periods of time when terms are trending on Twitter and the
corresponding activity on Urban Dictionary, revealing that new definitions are
more likely to be added to Urban Dictionary for terms that are currently
trending on Twitter.
| 2,020 | Computation and Language |
Recent Advances in SQL Query Generation: A Survey | Natural language is hypothetically the best user interface for many domains.
However, general models that provide an interface between natural language and
any other domain still do not exist. Providing natural language interface to
relational databases could possibly attract a vast majority of users that are
or are not proficient with query languages. With the rise of deep learning
techniques, there is extensive ongoing research in designing a suitable natural
language interface to relational databases.
This survey aims to overview some of the latest methods and models proposed
in the area of SQL query generation from natural language. We describe models
with various architectures such as convolutional neural networks, recurrent
neural networks, pointer networks, reinforcement learning, etc. Several
datasets intended to address the problem of SQL query generation are
interpreted and briefly overviewed. In the end, evaluation metrics utilized in
the field are presented mainly as a combination of execution accuracy and
logical form accuracy.
| 2,020 | Computation and Language |
Movement Pruning: Adaptive Sparsity by Fine-Tuning | Magnitude pruning is a widely used strategy for reducing model size in pure
supervised learning; however, it is less effective in the transfer learning
regime that has become standard for state-of-the-art natural language
processing applications. We propose the use of movement pruning, a simple,
deterministic first-order weight pruning method that is more adaptive to
pretrained model fine-tuning. We give mathematical foundations to the method
and compare it to existing zeroth- and first-order pruning methods. Experiments
show that when pruning large pretrained language models, movement pruning shows
significant improvements in high-sparsity regimes. When combined with
distillation, the approach achieves minimal accuracy loss with down to only 3%
of the model parameters.
| 2,020 | Computation and Language |
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named
Entity Recognition | Named entity recognition (NER) is an extensively studied task that extracts
and classifies named entities in a text. NER is crucial not only in downstream
language processing applications such as relation extraction and question
answering but also in large scale big data operations such as real-time
analysis of online digital media content. Recent research efforts on Turkish, a
less studied language with morphologically rich nature, have demonstrated the
effectiveness of neural architectures on well-formed texts and yielded
state-of-the art results by formulating the task as a sequence tagging problem.
In this work, we empirically investigate the use of recent neural architectures
(Bidirectional long short-term memory and Transformer-based networks) proposed
for Turkish NER tagging in the same setting. Our results demonstrate that
transformer-based networks which can model long-range context overcome the
limitations of BiLSTM networks where different input features at the character,
subword, and word levels are utilized. We also propose a transformer-based
network with a conditional random field (CRF) layer that leads to the
state-of-the-art result (95.95\% f-measure) on a common dataset. Our study
contributes to the literature that quantifies the impact of transfer learning
on processing morphologically rich languages.
| 2,020 | Computation and Language |
Uncovering Gender Bias in Media Coverage of Politicians with Machine
Learning | This paper presents research uncovering systematic gender bias in the
representation of political leaders in the media, using artificial
intelligence. Newspaper coverage of Irish ministers over a fifteen year period
was gathered and analysed with natural language processing techniques and
machine learning. Findings demonstrate evidence of gender bias in the portrayal
of female politicians, the kind of policies they were associated with and how
they were evaluated in terms of their performance as political leaders. This
paper also sets out a methodology whereby media content may be analysed on a
large scale utilising techniques from artificial intelligence within a
theoretical framework founded in gender theory and feminist linguistics.
| 2,019 | Computation and Language |
In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts | Information Extraction (IE) from scientific texts can be used to guide
readers to the central information in scientific documents. But narrow IE
systems extract only a fraction of the information captured, and Open IE
systems do not perform well on the long and complex sentences encountered in
scientific texts. In this work we combine the output of both types of systems
to achieve Semi-Open Relation Extraction, a new task that we explore in the
Biology domain. First, we present the Focused Open Biological Information
Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow
scientific IE system to extract trade-off relations and arguments that are
central to biology texts. We then run both the narrow IE system and a
state-of-the-art Open IE system on a corpus of 10k open-access scientific
biological texts. We show that a significant amount (65%) of erroneous and
uninformative Open IE extractions can be filtered using narrow IE extractions.
Furthermore, we show that the retained extractions are significantly more often
informative to a reader.
| 2,020 | Computation and Language |
A Scientific Information Extraction Dataset for Nature Inspired
Engineering | Nature has inspired various ground-breaking technological developments in
applications ranging from robotics to aerospace engineering and the
manufacturing of medical devices. However, accessing the information captured
in scientific biology texts is a time-consuming and hard task that requires
domain-specific knowledge. Improving access for outsiders can help
interdisciplinary research like Nature Inspired Engineering. This paper
describes a dataset of 1,500 manually-annotated sentences that express
domain-independent relations between central concepts in a scientific biology
text, such as trade-offs and correlations. The arguments of these relations can
be Multi Word Expressions and have been annotated with modifying phrases to
form non-projective graphs. The dataset allows for training and evaluating
Relation Extraction algorithms that aim for coarse-grained typing of scientific
biological documents, enabling a high-level filter for engineers.
| 2,020 | Computation and Language |
KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive
Tweets Using Weighted Ensemble and Fine-Tuned BERT | This research presents our team KEIS@JUST participation at SemEval-2020 Task
12 which represents shared task on multilingual offensive language. We
participated in all the provided languages for all subtasks except sub-task-A
for the English language. Two main approaches have been developed the first is
performed to tackle both languages Arabic and English, a weighted ensemble
consists of Bi-GRU and CNN followed by Gaussian noise and global pooling layer
multiplied by weights to improve the overall performance. The second is
performed for other languages, a transfer learning from BERT beside the
recurrent neural networks such as Bi-LSTM and Bi-GRU followed by a global
average pooling layer. Word embedding and contextual embedding have been used
as features, moreover, data augmentation has been used only for the Arabic
language.
| 2,020 | Computation and Language |
Neural Multi-Task Learning for Teacher Question Detection in Online
Classrooms | Asking questions is one of the most crucial pedagogical techniques used by
teachers in class. It not only offers open-ended discussions between teachers
and students to exchange ideas but also provokes deeper student thought and
critical analysis. Providing teachers with such pedagogical feedback will
remarkably help teachers improve their overall teaching quality over time in
classrooms. Therefore, in this work, we build an end-to-end neural framework
that automatically detects questions from teachers' audio recordings. Compared
with traditional methods, our approach not only avoids cumbersome feature
engineering, but also adapts to the task of multi-class question detection in
real education scenarios. By incorporating multi-task learning techniques, we
are able to strengthen the understanding of semantic relations among different
types of questions. We conducted extensive experiments on the question
detection tasks in a real-world online classroom dataset and the results
demonstrate the superiority of our model in terms of various evaluation
metrics.
| 2,020 | Computation and Language |
MicroNet for Efficient Language Modeling | It is important to design compact language models for efficient deployment.
We improve upon recent advances in both the language modeling domain and the
model-compression domain to construct parameter and computation efficient
language models. We use an efficient transformer-based architecture with
adaptive embedding and softmax, differentiable non-parametric cache, Hebbian
softmax, knowledge distillation, network pruning, and low-bit quantization. In
this paper, we provide the winning solution to the NeurIPS 2019 MicroNet
Challenge in the language modeling track. Compared to the baseline language
model provided by the MicroNet Challenge, our model is 90 times more
parameter-efficient and 36 times more computation-efficient while achieving the
required test perplexity of 35 on the Wikitext-103 dataset. We hope that this
work will aid future research into efficient language models, and we have
released our full source code at
https://github.com/mit-han-lab/neurips-micronet.
| 2,020 | Computation and Language |
Integrating Semantic and Structural Information with Graph Convolutional
Network for Controversy Detection | Identifying controversial posts on social media is a fundamental task for
mining public sentiment, assessing the influence of events, and alleviating the
polarized views. However, existing methods fail to 1) effectively incorporate
the semantic information from content-related posts; 2) preserve the structural
information for reply relationship modeling; 3) properly handle posts from
topics dissimilar to those in the training set. To overcome the first two
limitations, we propose Topic-Post-Comment Graph Convolutional Network
(TPC-GCN), which integrates the information from the graph structure and
content of topics, posts, and comments for post-level controversy detection. As
to the third limitation, we extend our model to Disentangled TPC-GCN
(DTPC-GCN), to disentangle topic-related and topic-unrelated features and then
fuse dynamically. Extensive experiments on two real-world datasets demonstrate
that our models outperform existing methods. Analysis of the results and cases
proves that our models can integrate both semantic and structural information
with significant generalizability.
| 2,020 | Computation and Language |
Sequential Sentence Matching Network for Multi-turn Response Selection
in Retrieval-based Chatbots | Recently, open domain multi-turn chatbots have attracted much interest from
lots of researchers in both academia and industry. The dominant retrieval-based
methods use context-response matching mechanisms for multi-turn response
selection. Specifically, the state-of-the-art methods perform the
context-response matching by word or segment similarity. However, these models
lack a full exploitation of the sentence-level semantic information, and make
simple mistakes that humans can easily avoid. In this work, we propose a
matching network, called sequential sentence matching network (S2M), to use the
sentence-level semantic information to address the problem. Firstly and most
importantly, we find that by using the sentence-level semantic information, the
network successfully addresses the problem and gets a significant improvement
on matching, resulting in a state-of-the-art performance. Furthermore, we
integrate the sentence matching we introduced here and the usual word
similarity matching reported in the current literature, to match at different
semantic levels. Experiments on three public data sets show that such
integration further improves the model performance.
| 2,020 | Computation and Language |
ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether
Self-Training Helps Them | This paper presents the winning system for the propaganda Technique
Classification (TC) task and the second-placed system for the propaganda Span
Identification (SI) task. The purpose of TC task was to identify an applied
propaganda technique given propaganda text fragment. The goal of SI task was to
find specific text fragments which contain at least one propaganda technique.
Both of the developed solutions used semi-supervised learning technique of
self-training. Interestingly, although CRF is barely used with
transformer-based language models, the SI task was approached with RoBERTa-CRF
architecture. An ensemble of RoBERTa-based models was proposed for the TC task,
with one of them making use of Span CLS layers we introduce in the present
paper. In addition to describing the submitted systems, an impact of
architectural decisions and training schemes is investigated along with remarks
regarding training models of the same or better quality with lower
computational budget. Finally, the results of error analysis are presented.
| 2,020 | Computation and Language |
Logical Inferences with Comparatives and Generalized Quantifiers | Comparative constructions pose a challenge in Natural Language Inference
(NLI), which is the task of determining whether a text entails a hypothesis.
Comparatives are structurally complex in that they interact with other
linguistic phenomena such as quantifiers, numerals, and lexical antonyms. In
formal semantics, there is a rich body of work on comparatives and gradable
expressions using the notion of degree. However, a logical inference system for
comparatives has not been sufficiently developed for use in the NLI task. In
this paper, we present a compositional semantics that maps various comparative
constructions in English to semantic representations via Combinatory Categorial
Grammar (CCG) parsers and combine it with an inference system based on
automated theorem proving. We evaluate our system on three NLI datasets that
contain complex logical inferences with comparatives, generalized quantifiers,
and numerals. We show that the system outperforms previous logic-based systems
as well as recent deep learning-based models.
| 2,020 | Computation and Language |
Unsupervised Embedding-based Detection of Lexical Semantic Changes | This paper describes EmbLexChange, a system introduced by the "Life-Language"
team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic
changes. EmbLexChange is defined as the divergence between the embedding based
profiles of word w (calculated with respect to a set of reference words) in the
source and the target domains (source and target domains can be simply two time
frames t1 and t2). The underlying assumption is that the lexical-semantic
change of word w would affect its co-occurring words and subsequently alters
the neighborhoods in the embedding spaces. We show that using a resampling
framework for the selection of reference words, we can reliably detect
lexical-semantic changes in English, German, Swedish, and Latin. EmbLexChange
achieved second place in the binary detection of semantic changes in the
SemEval-2020.
| 2,020 | Computation and Language |
A Text Reassembling Approach to Natural Language Generation | Recent years have seen a number of proposals for performing Natural Language
Generation (NLG) based in large part on statistical techniques. Despite having
many attractive features, we argue that these existing approaches nonetheless
have some important drawbacks, sometimes because the approach in question is
not fully statistical (i.e., relies on a certain amount of handcrafting),
sometimes because the approach in question lacks transparency. Focussing on
some of the key NLG tasks (namely Content Selection, Lexical Choice, and
Linguistic Realisation), we propose a novel approach, called the Text
Reassembling approach to NLG (TRG), which approaches the ideal of a purely
statistical approach very closely, and which is at the same time highly
transparent. We evaluate the TRG approach and discuss how TRG may be extended
to deal with other NLG tasks, such as Document Structuring, and Aggregation. We
discuss the strengths and limitations of TRG, concluding that the method may
hold particular promise for domain experts who want to build an NLG system
despite having little expertise in linguistics and NLG.
| 2,020 | Computation and Language |
IntelliCode Compose: Code Generation Using Transformer | In software development through integrated development environments (IDEs),
code completion is one of the most widely used features. Nevertheless, majority
of integrated development environments only support completion of methods and
APIs, or arguments.
In this paper, we introduce IntelliCode Compose $-$ a general-purpose
multilingual code completion tool which is capable of predicting sequences of
code tokens of arbitrary types, generating up to entire lines of syntactically
correct code. It leverages state-of-the-art generative transformer model
trained on 1.2 billion lines of source code in Python, $C\#$, JavaScript and
TypeScript programming languages. IntelliCode Compose is deployed as a
cloud-based web service. It makes use of client-side tree-based caching,
efficient parallel implementation of the beam search decoder, and compute graph
optimizations to meet edit-time completion suggestion requirements in the
Visual Studio Code IDE and Azure Notebook.
Our best model yields an average edit similarity of $86.7\%$ and a perplexity
of 1.82 for Python programming language.
| 2,020 | Computation and Language |
Recurrent Chunking Mechanisms for Long-Text Machine Reading
Comprehension | In this paper, we study machine reading comprehension (MRC) on long texts,
where a model takes as inputs a lengthy document and a question and then
extracts a text span from the document as an answer. State-of-the-art models
tend to use a pretrained transformer model (e.g., BERT) to encode the joint
contextual information of document and question. However, these
transformer-based models can only take a fixed-length (e.g., 512) text as its
input. To deal with even longer text inputs, previous approaches usually chunk
them into equally-spaced segments and predict answers based on each segment
independently without considering the information from other segments. As a
result, they may form segments that fail to cover the correct answer span or
retain insufficient contexts around it, which significantly degrades the
performance. Moreover, they are less capable of answering questions that need
cross-segment information.
We propose to let a model learn to chunk in a more flexible way via
reinforcement learning: a model can decide the next segment that it wants to
process in either direction. We also employ recurrent mechanisms to enable
information to flow across segments. Experiments on three MRC datasets -- CoQA,
QuAC, and TriviaQA -- demonstrate the effectiveness of our proposed recurrent
chunking mechanisms: we can obtain segments that are more likely to contain
complete answers and at the same time provide sufficient contexts around the
ground truth answers for better predictions.
| 2,020 | Computation and Language |
Rethinking and Improving Natural Language Generation with Layer-Wise
Multi-View Decoding | In sequence-to-sequence learning, e.g., natural language generation, the
decoder relies on the attention mechanism to efficiently extract information
from the encoder. While it is common practice to draw information from only the
last encoder layer, recent work has proposed to use representations from
different encoder layers for diversified levels of information. Nonetheless,
the decoder still obtains only a single view of the source sequences, which
might lead to insufficient training of the encoder layer stack due to the
hierarchy bypassing problem. In this work, we propose layer-wise multi-view
decoding, where for each decoder layer, together with the representations from
the last encoder layer, which serve as a global view, those from other encoder
layers are supplemented for a stereoscopic view of the source sequences.
Systematic experiments and analyses show that we successfully address the
hierarchy bypassing problem, require almost negligible parameter increase, and
substantially improve the performance of sequence-to-sequence learning with
deep representations on five diverse tasks, i.e., machine translation,
abstractive summarization, image captioning, video captioning, medical report
generation, and paraphrase generation. In particular, our approach achieves new
state-of-the-art results on ten benchmark datasets, including a low-resource
machine translation dataset and two low-resource medical report generation
datasets.
| 2,022 | Computation and Language |
Learning Probabilistic Sentence Representations from Paraphrases | Probabilistic word embeddings have shown effectiveness in capturing notions
of generality and entailment, but there is very little work on doing the
analogous type of investigation for sentences. In this paper we define
probabilistic models that produce distributions for sentences. Our
best-performing model treats each word as a linear transformation operator
applied to a multivariate Gaussian distribution. We train our models on
paraphrases and demonstrate that they naturally capture sentence specificity.
While our proposed model achieves the best performance overall, we also show
that specificity is represented by simpler architectures via the norm of the
sentence vectors. Qualitative analysis shows that our probabilistic model
captures sentential entailment and provides ways to analyze the specificity and
preciseness of individual words.
| 2,020 | Computation and Language |
RPD: A Distance Function Between Word Embeddings | It is well-understood that different algorithms, training processes, and
corpora produce different word embeddings. However, less is known about the
relation between different embedding spaces, i.e. how far different sets of
embeddings deviate from each other. In this paper, we propose a novel metric
called Relative pairwise inner Product Distance (RPD) to quantify the distance
between different sets of word embeddings. This metric has a unified scale for
comparing different sets of word embeddings. Based on the properties of RPD, we
study the relations of word embeddings of different algorithms systematically
and investigate the influence of different training processes and corpora. The
results shed light on the poorly understood word embeddings and justify RPD as
a measure of the distance of embedding spaces.
| 2,020 | Computation and Language |
Semi-Automating Knowledge Base Construction for Cancer Genetics | In this work, we consider the exponentially growing subarea of genetics in
cancer. The need to synthesize and centralize this evidence for dissemination
has motivated a team of physicians to manually construct and maintain a
knowledge base that distills key results reported in the literature. This is a
laborious process that entails reading through full-text articles to understand
the study design, assess study quality, and extract the reported cancer risk
estimates associated with particular hereditary cancer genes (i.e.,
penetrance). In this work, we propose models to automatically surface key
elements from full-text cancer genetics articles, with the ultimate aim of
expediting the manual workflow currently in place.
We propose two challenging tasks that are critical for characterizing the
findings reported cancer genetics studies: (i) Extracting snippets of text that
describe \emph{ascertainment mechanisms}, which in turn inform whether the
population studied may introduce bias owing to deviations from the target
population; (ii) Extracting reported risk estimates (e.g., odds or hazard
ratios) associated with specific germline mutations. The latter task may be
viewed as a joint entity tagging and relation extraction problem. To train
models for these tasks, we induce distant supervision over tokens and snippets
in full-text articles using the manually constructed knowledge base. We propose
and evaluate several model variants, including a transformer-based joint entity
and relation extraction model to extract <germline mutation, risk-estimate>}
pairs. We observe strong empirical performance, highlighting the practical
potential for such models to aid KB construction in this space. We ablate
components of our model, observing, e.g., that a joint model for <germline
mutation, risk-estimate> fares substantially better than a pipelined approach.
| 2,020 | Computation and Language |
Adversarial Training for Commonsense Inference | We propose an AdversariaL training algorithm for commonsense InferenCE
(ALICE). We apply small perturbations to word embeddings and minimize the
resultant adversarial risk to regularize the model. We exploit a novel
combination of two different approaches to estimate these perturbations: 1)
using the true label and 2) using the model prediction. Without relying on any
human-crafted features, knowledge bases, or additional datasets other than the
target datasets, our model boosts the fine-tuning performance of RoBERTa,
achieving competitive results on multiple reading comprehension datasets that
require commonsense inference.
| 2,020 | Computation and Language |
Encodings of Source Syntax: Similarities in NMT Representations Across
Target Languages | We train neural machine translation (NMT) models from English to six target
languages, using NMT encoder representations to predict ancestor constituent
labels of source language words. We find that NMT encoders learn similar source
syntax regardless of NMT target language, relying on explicit morphosyntactic
cues to extract syntactic features from source sentences. Furthermore, the NMT
encoders outperform RNNs trained directly on several of the constituent label
prediction tasks, suggesting that NMT encoder representations can be used
effectively for natural language tasks involving syntax. However, both the NMT
encoders and the directly-trained RNNs learn substantially different syntactic
information from a probabilistic context-free grammar (PCFG) parser. Despite
lower overall accuracy scores, the PCFG often performs well on sentences for
which the RNN-based models perform poorly, suggesting that RNN architectures
are constrained in the types of syntax they can learn.
| 2,020 | Computation and Language |
IMoJIE: Iterative Memory-Based Joint Open Information Extraction | While traditional systems for Open Information Extraction were statistical
and rule-based, recently neural models have been introduced for the task. Our
work builds upon CopyAttention, a sequence generation OpenIE model (Cui et.
al., 2018). Our analysis reveals that CopyAttention produces a constant number
of extractions per sentence, and its extracted tuples often express redundant
information.
We present IMoJIE, an extension to CopyAttention, which produces the next
extraction conditioned on all previously extracted tuples. This approach
overcomes both shortcomings of CopyAttention, resulting in a variable number of
diverse extractions per sentence. We train IMoJIE on training data bootstrapped
from extractions of several non-neural systems, which have been automatically
filtered to reduce redundancy and noise. IMoJIE outperforms CopyAttention by
about 18 F1 pts, and a BERT-based strong baseline by 2 F1 pts, establishing a
new state of the art for the task.
| 2,020 | Computation and Language |
Multi-modal Automated Speech Scoring using Attention Fusion | In this study, we propose a novel multi-modal end-to-end neural approach for
automated assessment of non-native English speakers' spontaneous speech using
attention fusion. The pipeline employs Bi-directional Recurrent Convolutional
Neural Networks and Bi-directional Long Short-Term Memory Neural Networks to
encode acoustic and lexical cues from spectrograms and transcriptions,
respectively. Attention fusion is performed on these learned predictive
features to learn complex interactions between different modalities before
final scoring. We compare our model with strong baselines and find combined
attention to both lexical and acoustic cues significantly improves the overall
performance of the system. Further, we present a qualitative and quantitative
analysis of our model.
| 2,021 | Computation and Language |
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global
E-Commerce | With the prosperous of cross-border e-commerce, there is an urgent demand for
designing intelligent approaches for assisting e-commerce sellers to offer
local products for consumers from all over the world. In this paper, we explore
a new task of cross-lingual information retrieval, i.e., cross-lingual
set-to-description retrieval in cross-border e-commerce, which involves
matching product attribute sets in the source language with persuasive product
descriptions in the target language. We manually collect a new and high-quality
paired dataset, where each pair contains an unordered product attribute set in
the source language and an informative product description in the target
language. As the dataset construction process is both time-consuming and
costly, the new dataset only comprises of 13.5k pairs, which is a low-resource
setting and can be viewed as a challenging testbed for model development and
evaluation in cross-border e-commerce. To tackle this cross-lingual
set-to-description retrieval task, we propose a novel cross-lingual matching
network (CLMN) with the enhancement of context-dependent cross-lingual mapping
upon the pre-trained monolingual BERT representations. Experimental results
indicate that our proposed CLMN yields impressive results on the challenging
task and the context-dependent cross-lingual mapping on BERT yields noticeable
improvement over the pre-trained multi-lingual BERT model.
| 2,020 | Computation and Language |
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