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Asking Crowdworkers to Write Entailment Examples: The Best of Bad
Options | Large-scale natural language inference (NLI) datasets such as SNLI or MNLI
have been created by asking crowdworkers to read a premise and write three new
hypotheses, one for each possible semantic relationships (entailment,
contradiction, and neutral). While this protocol has been used to create useful
benchmark data, it remains unclear whether the writing-based annotation
protocol is optimal for any purpose, since it has not been evaluated directly.
Furthermore, there is ample evidence that crowdworker writing can introduce
artifacts in the data. We investigate two alternative protocols which
automatically create candidate (premise, hypothesis) pairs for annotators to
label. Using these protocols and a writing-based baseline, we collect several
new English NLI datasets of over 3k examples each, each using a fixed amount of
annotator time, but a varying number of examples to fit that time budget. Our
experiments on NLI and transfer learning show negative results: None of the
alternative protocols outperforms the baseline in evaluations of generalization
within NLI or on transfer to outside target tasks. We conclude that crowdworker
writing still the best known option for entailment data, highlighting the need
for further data collection work to focus on improving writing-based annotation
processes.
| 2,020 | Computation and Language |
Model Selection for Cross-Lingual Transfer | Transformers that are pre-trained on multilingual corpora, such as, mBERT and
XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In
the zero-shot transfer setting, only English training data is used, and the
fine-tuned model is evaluated on another target language. While this works
surprisingly well, substantial variance has been observed in target language
performance between different fine-tuning runs, and in the zero-shot setup, no
target-language development data is available to select among multiple
fine-tuned models. Prior work has relied on English dev data to select among
models that are fine-tuned with different learning rates, number of steps and
other hyperparameters, often resulting in suboptimal choices. In this paper, we
show that it is possible to select consistently better models when small
amounts of annotated data are available in auxiliary pivot languages. We
propose a machine learning approach to model selection that uses the fine-tuned
model's own internal representations to predict its cross-lingual capabilities.
In extensive experiments we find that this method consistently selects better
models than English validation data across twenty five languages (including
eight low-resource languages), and often achieves results that are comparable
to model selection using target language development data.
| 2,021 | Computation and Language |
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth
Mover's Distance | Pre-trained language models (e.g., BERT) have achieved significant success in
various natural language processing (NLP) tasks. However, high storage and
computational costs obstruct pre-trained language models to be effectively
deployed on resource-constrained devices. In this paper, we propose a novel
BERT distillation method based on many-to-many layer mapping, which allows each
intermediate student layer to learn from any intermediate teacher layers. In
this way, our model can learn from different teacher layers adaptively for
various NLP tasks. %motivated by the intuition that different NLP tasks require
different levels of linguistic knowledge contained in the intermediate layers
of BERT. In addition, we leverage Earth Mover's Distance (EMD) to compute the
minimum cumulative cost that must be paid to transform knowledge from teacher
network to student network. EMD enables the effective matching for many-to-many
layer mapping. %EMD can be applied to network layers with different sizes and
effectively measures semantic distance between the teacher network and student
network. Furthermore, we propose a cost attention mechanism to learn the layer
weights used in EMD automatically, which is supposed to further improve the
model's performance and accelerate convergence time. Extensive experiments on
GLUE benchmark demonstrate that our model achieves competitive performance
compared to strong competitors in terms of both accuracy and model compression.
| 2,020 | Computation and Language |
Corruption Is Not All Bad: Incorporating Discourse Structure into
Pre-training via Corruption for Essay Scoring | Existing approaches for automated essay scoring and document representation
learning typically rely on discourse parsers to incorporate discourse structure
into text representation. However, the performance of parsers is not always
adequate, especially when they are used on noisy texts, such as student essays.
In this paper, we propose an unsupervised pre-training approach to capture
discourse structure of essays in terms of coherence and cohesion that does not
require any discourse parser or annotation. We introduce several types of
token, sentence and paragraph-level corruption techniques for our proposed
pre-training approach and augment masked language modeling pre-training with
our pre-training method to leverage both contextualized and discourse
information. Our proposed unsupervised approach achieves new state-of-the-art
result on essay Organization scoring task.
| 2,020 | Computation and Language |
Incorporating BERT into Parallel Sequence Decoding with Adapters | While large scale pre-trained language models such as BERT have achieved
great success on various natural language understanding tasks, how to
efficiently and effectively incorporate them into sequence-to-sequence models
and the corresponding text generation tasks remains a non-trivial problem. In
this paper, we propose to address this problem by taking two different BERT
models as the encoder and decoder respectively, and fine-tuning them by
introducing simple and lightweight adapter modules, which are inserted between
BERT layers and tuned on the task-specific dataset. In this way, we obtain a
flexible and efficient model which is able to jointly leverage the information
contained in the source-side and target-side BERT models, while bypassing the
catastrophic forgetting problem. Each component in the framework can be
considered as a plug-in unit, making the framework flexible and task agnostic.
Our framework is based on a parallel sequence decoding algorithm named
Mask-Predict considering the bi-directional and conditional independent nature
of BERT, and can be adapted to traditional autoregressive decoding easily. We
conduct extensive experiments on neural machine translation tasks where the
proposed method consistently outperforms autoregressive baselines while
reducing the inference latency by half, and achieves $36.49$/$33.57$ BLEU
scores on IWSLT14 German-English/WMT14 German-English translation. When adapted
to autoregressive decoding, the proposed method achieves $30.60$/$43.56$ BLEU
scores on WMT14 English-German/English-French translation, on par with the
state-of-the-art baseline models.
| 2,020 | Computation and Language |
Improving Text Generation Evaluation with Batch Centering and Tempered
Word Mover Distance | Recent advances in automatic evaluation metrics for text have shown that deep
contextualized word representations, such as those generated by BERT encoders,
are helpful for designing metrics that correlate well with human judgements. At
the same time, it has been argued that contextualized word representations
exhibit sub-optimal statistical properties for encoding the true similarity
between words or sentences. In this paper, we present two techniques for
improving encoding representations for similarity metrics: a batch-mean
centering strategy that improves statistical properties; and a computationally
efficient tempered Word Mover Distance, for better fusion of the information in
the contextualized word representations. We conduct numerical experiments that
demonstrate the robustness of our techniques, reporting results over various
BERT-backbone learned metrics and achieving state of the art correlation with
human ratings on several benchmarks.
| 2,020 | Computation and Language |
The workweek is the best time to start a family -- A Study of GPT-2
Based Claim Generation | Argument generation is a challenging task whose research is timely
considering its potential impact on social media and the dissemination of
information. Here we suggest a pipeline based on GPT-2 for generating coherent
claims, and explore the types of claims that it produces, and their veracity,
using an array of manual and automatic assessments. In addition, we explore the
interplay between this task and the task of Claim Retrieval, showing how they
can complement one another.
| 2,020 | Computation and Language |
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained
Language Models | Language models (LMs) have proven surprisingly successful at capturing
factual knowledge by completing cloze-style fill-in-the-blank questions such as
"Punta Cana is located in _." However, while knowledge is both written and
queried in many languages, studies on LMs' factual representation ability have
almost invariably been performed on English. To assess factual knowledge
retrieval in LMs in different languages, we create a multilingual benchmark of
cloze-style probes for 23 typologically diverse languages. To properly handle
language variations, we expand probing methods from single- to multi-word
entities, and develop several decoding algorithms to generate multi-token
predictions. Extensive experimental results provide insights about how well (or
poorly) current state-of-the-art LMs perform at this task in languages with
more or fewer available resources. We further propose a code-switching-based
method to improve the ability of multilingual LMs to access knowledge, and
verify its effectiveness on several benchmark languages. Benchmark data and
code have been released at https://x-factr.github.io.
| 2,020 | Computation and Language |
Mathematical Word Problem Generation from Commonsense Knowledge Graph
and Equations | There is an increasing interest in the use of mathematical word problem (MWP)
generation in educational assessment. Different from standard natural question
generation, MWP generation needs to maintain the underlying mathematical
operations between quantities and variables, while at the same time ensuring
the relevance between the output and the given topic. To address above problem,
we develop an end-to-end neural model to generate diverse MWPs in real-world
scenarios from commonsense knowledge graph and equations. The proposed model
(1) learns both representations from edge-enhanced Levi graphs of symbolic
equations and commonsense knowledge; (2) automatically fuses equation and
commonsense knowledge information via a self-planning module when generating
the MWPs. Experiments on an educational gold-standard set and a large-scale
generated MWP set show that our approach is superior on the MWP generation
task, and it outperforms the SOTA models in terms of both automatic evaluation
metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e.,
equation relevance, topic relevance, and language coherence. To encourage
reproducible results, we make our code and MWP dataset public available at
\url{https://github.com/tal-ai/MaKE_EMNLP2021}.
| 2,021 | Computation and Language |
Mitigating Gender Bias in Machine Translation with Target Gender
Annotations | When translating "The secretary asked for details." to a language with
grammatical gender, it might be necessary to determine the gender of the
subject "secretary". If the sentence does not contain the necessary
information, it is not always possible to disambiguate. In such cases, machine
translation systems select the most common translation option, which often
corresponds to the stereotypical translations, thus potentially exacerbating
prejudice and marginalisation of certain groups and people. We argue that the
information necessary for an adequate translation can not always be deduced
from the sentence being translated or even might depend on external knowledge.
Therefore, in this work, we propose to decouple the task of acquiring the
necessary information from the task of learning to translate correctly when
such information is available. To that end, we present a method for training
machine translation systems to use word-level annotations containing
information about subject's gender. To prepare training data, we annotate
regular source language words with grammatical gender information of the
corresponding target language words. Using such data to train machine
translation systems reduces their reliance on gender stereotypes when
information about the subject's gender is available. Our experiments on five
language pairs show that this allows improving accuracy on the WinoMT test set
by up to 25.8 percentage points.
| 2,020 | Computation and Language |
KLearn: Background Knowledge Inference from Summarization Data | The goal of text summarization is to compress documents to the relevant
information while excluding background information already known to the
receiver. So far, summarization researchers have given considerably more
attention to relevance than to background knowledge. In contrast, this work
puts background knowledge in the foreground. Building on the realization that
the choices made by human summarizers and annotators contain implicit
information about their background knowledge, we develop and compare techniques
for inferring background knowledge from summarization data. Based on this
framework, we define summary scoring functions that explicitly model background
knowledge, and show that these scoring functions fit human judgments
significantly better than baselines. We illustrate some of the many potential
applications of our framework. First, we provide insights into human
information importance priors. Second, we demonstrate that averaging the
background knowledge of multiple, potentially biased annotators or corpora
greatly improves summary-scoring performance. Finally, we discuss potential
applications of our framework beyond summarization.
| 2,020 | Computation and Language |
Annotationsaurus: A Searchable Directory of Annotation Tools | Manual annotation of textual documents is a necessary task when constructing
benchmark corpora for training and evaluating machine learning algorithms. We
created a comprehensive directory of annotation tools that currently includes
93 tools. We analyzed the tools over a set of 31 features and implemented
simple scripts and a Web application that filters the tools based on chosen
criteria. We present two use cases using the directory and propose ideas for
its maintenance. The directory, source codes for scripts, and link to the Web
application are available at: https://github.com/mariananeves/annotation-tools
| 2,020 | Computation and Language |
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural
Networks | Text summarization aims to compress a textual document to a short summary
while keeping salient information. Extractive approaches are widely used in
text summarization because of their fluency and efficiency. However, most of
existing extractive models hardly capture inter-sentence relationships,
particularly in long documents. They also often ignore the effect of topical
information on capturing important contents. To address these issues, this
paper proposes a graph neural network (GNN)-based extractive summarization
model, enabling to capture inter-sentence relationships efficiently via
graph-structured document representation. Moreover, our model integrates a
joint neural topic model (NTM) to discover latent topics, which can provide
document-level features for sentence selection. The experimental results
demonstrate that our model not only substantially achieves state-of-the-art
results on CNN/DM and NYT datasets but also considerably outperforms existing
approaches on scientific paper datasets consisting of much longer documents,
indicating its better robustness in document genres and lengths. Further
discussions show that topical information can help the model preselect salient
contents from an entire document, which interprets its effectiveness in long
document summarization.
| 2,020 | Computation and Language |
BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting
the (Graded) Effect of Context in Word Similarity | This paper presents the team BRUMS submission to SemEval-2020 Task 3: Graded
Word Similarity in Context. The system utilises state-of-the-art contextualised
word embeddings, which have some task-specific adaptations, including stacked
embeddings and average embeddings. Overall, the approach achieves good
evaluation scores across all the languages, while maintaining simplicity.
Following the final rankings, our approach is ranked within the top 5 solutions
of each language while preserving the 1st position of Finnish subtask 2.
| 2,021 | Computation and Language |
BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive
Language Identification in Social Media | In this paper, we describe the team \textit{BRUMS} entry to OffensEval 2:
Multilingual Offensive Language Identification in Social Media in SemEval-2020.
The OffensEval organizers provided participants with annotated datasets
containing posts from social media in Arabic, Danish, English, Greek and
Turkish. We present a multilingual deep learning model to identify offensive
language in social media. Overall, the approach achieves acceptable evaluation
scores, while maintaining flexibility between languages.
| 2,020 | Computation and Language |
RGCL at SemEval-2020 Task 6: Neural Approaches to Definition Extraction | This paper presents the RGCL team submission to SemEval 2020 Task 6:
DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence
and token levels. It utilises state-of-the-art neural network architectures,
which have some task-specific adaptations, including an automatically extended
training set. Overall, the approach achieves acceptable evaluation scores,
while maintaining flexibility in architecture selection.
| 2,020 | Computation and Language |
F1 is Not Enough! Models and Evaluation Towards User-Centered
Explainable Question Answering | Explainable question answering systems predict an answer together with an
explanation showing why the answer has been selected. The goal is to enable
users to assess the correctness of the system and understand its reasoning
process. However, we show that current models and evaluation settings have
shortcomings regarding the coupling of answer and explanation which might cause
serious issues in user experience. As a remedy, we propose a hierarchical model
and a new regularization term to strengthen the answer-explanation coupling as
well as two evaluation scores to quantify the coupling. We conduct experiments
on the HOTPOTQA benchmark data set and perform a user study. The user study
shows that our models increase the ability of the users to judge the
correctness of the system and that scores like F1 are not enough to estimate
the usefulness of a model in a practical setting with human users. Our scores
are better aligned with user experience, making them promising candidates for
model selection.
| 2,020 | Computation and Language |
Extending Implicit Discourse Relation Recognition to the PDTB-3 | The PDTB-3 contains many more Implicit discourse relations than the previous
PDTB-2. This is in part because implicit relations have now been annotated
within sentences as well as between them. In addition, some now co-occur with
explicit discourse relations, instead of standing on their own. Here we show
that while this can complicate the problem of identifying the location of
implicit discourse relations, it can in turn simplify the problem of
identifying their senses. We present data to support this claim, as well as
methods that can serve as a non-trivial baseline for future state-of-the-art
recognizers for implicit discourse relations.
| 2,020 | Computation and Language |
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information
Networks | Joint-event-extraction, which extracts structural information (i.e., entities
or triggers of events) from unstructured real-world corpora, has attracted more
and more research attention in natural language processing. Most existing works
do not fully address the sparse co-occurrence relationships between entities
and triggers, which loses this important information and thus deteriorates the
extraction performance. To mitigate this issue, we first define the
joint-event-extraction as a sequence-to-sequence labeling task with a tag set
composed of tags of triggers and entities. Then, to incorporate the missing
information in the aforementioned co-occurrence relationships, we propose a
Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of
either triggers or entities based on the type distribution of each other.
Moreover, since the connected entities and triggers naturally form a
heterogeneous information network (HIN), we leverage the latent pattern along
meta-paths for a given corpus to further improve the performance of our
proposed method. To verify the effectiveness of our proposed method, we conduct
extensive experiments on four real-world datasets as well as compare our method
with state-of-the-art methods. Empirical results and analysis show that our
approach outperforms the state-of-the-art methods in both entity and trigger
extraction.
| 2,020 | Computation and Language |
Modeling the Music Genre Perception across Language-Bound Cultures | The music genre perception expressed through human annotations of artists or
albums varies significantly across language-bound cultures. These variations
cannot be modeled as mere translations since we also need to account for
cultural differences in the music genre perception. In this work, we study the
feasibility of obtaining relevant cross-lingual, culture-specific music genre
annotations based only on language-specific semantic representations, namely
distributed concept embeddings and ontologies. Our study, focused on six
languages, shows that unsupervised cross-lingual music genre annotation is
feasible with high accuracy, especially when combining both types of
representations. This approach of studying music genres is the most extensive
to date and has many implications in musicology and music information
retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus
to benchmark state of the art multilingual pre-trained embedding models.
| 2,020 | Computation and Language |
CAPT: Contrastive Pre-Training for Learning Denoised Sequence
Representations | Pre-trained self-supervised models such as BERT have achieved striking
success in learning sequence representations, especially for natural language
processing. These models typically corrupt the given sequences with certain
types of noise, such as masking, shuffling, or substitution, and then try to
recover the original input. However, such pre-training approaches are prone to
learning representations that are covariant with the noise, leading to the
discrepancy between the pre-training and fine-tuning stage. To remedy this, we
present ContrAstive Pre-Training (CAPT) to learn noise invariant sequence
representations. The proposed CAPT encourages the consistency between
representations of the original sequence and its corrupted version via
unsupervised instance-wise training signals. In this way, it not only
alleviates the pretrain-finetune discrepancy induced by the noise of
pre-training, but also aids the pre-trained model in better capturing global
semantics of the input via more effective sentence-level supervision. Different
from most prior work that focuses on a particular modality, comprehensive
empirical evidence on 11 natural language understanding and cross-modal tasks
illustrates that CAPT is applicable for both language and vision-language
tasks, and obtains surprisingly consistent improvement, including 0.6\%
absolute gain on GLUE benchmarks and 0.8\% absolute increment on
$\text{NLVR}^2$.
| 2,020 | Computation and Language |
The Tatoeba Translation Challenge -- Realistic Data Sets for Low
Resource and Multilingual MT | This paper describes the development of a new benchmark for machine
translation that provides training and test data for thousands of language
pairs covering over 500 languages and tools for creating state-of-the-art
translation models from that collection. The main goal is to trigger the
development of open translation tools and models with a much broader coverage
of the World's languages. Using the package it is possible to work on realistic
low-resource scenarios avoiding artificially reduced setups that are common
when demonstrating zero-shot or few-shot learning. For the first time, this
package provides a comprehensive collection of diverse data sets in hundreds of
languages with systematic language and script annotation and data splits to
extend the narrow coverage of existing benchmarks. Together with the data
release, we also provide a growing number of pre-trained baseline models for
individual language pairs and selected language groups.
| 2,020 | Computation and Language |
Fine-grained linguistic evaluation for state-of-the-art Machine
Translation | This paper describes a test suite submission providing detailed statistics of
linguistic performance for the state-of-the-art German-English systems of the
Fifth Conference of Machine Translation (WMT20). The analysis covers 107
phenomena organized in 14 categories based on about 5,500 test items, including
a manual annotation effort of 45 person hours. Two systems (Tohoku and Huoshan)
appear to have significantly better test suite accuracy than the others,
although the best system of WMT20 is not significantly better than the one from
WMT19 in a macro-average. Additionally, we identify some linguistic phenomena
where all systems suffer (such as idioms, resultative predicates and
pluperfect), but we are also able to identify particular weaknesses for
individual systems (such as quotation marks, lexical ambiguity and sluicing).
Most of the systems of WMT19 which submitted new versions this year show
improvements.
| 2,020 | Computation and Language |
Aspect-based Document Similarity for Research Papers | Traditional document similarity measures provide a coarse-grained distinction
between similar and dissimilar documents. Typically, they do not consider in
what aspects two documents are similar. This limits the granularity of
applications like recommender systems that rely on document similarity. In this
paper, we extend similarity with aspect information by performing a pairwise
document classification task. We evaluate our aspect-based document similarity
for research papers. Paper citations indicate the aspect-based similarity,
i.e., the section title in which a citation occurs acts as a label for the pair
of citing and cited paper. We apply a series of Transformer models such as
RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM
baseline. We perform our experiments on two newly constructed datasets of
172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. Our
results show SciBERT as the best performing system. A qualitative examination
validates our quantitative results. Our findings motivate future research of
aspect-based document similarity and the development of a recommender system
based on the evaluated techniques. We make our datasets, code, and trained
models publicly available.
| 2,020 | Computation and Language |
Interpreting Attention Models with Human Visual Attention in Machine
Reading Comprehension | While neural networks with attention mechanisms have achieved superior
performance on many natural language processing tasks, it remains unclear to
which extent learned attention resembles human visual attention. In this paper,
we propose a new method that leverages eye-tracking data to investigate the
relationship between human visual attention and neural attention in machine
reading comprehension. To this end, we introduce a novel 23 participant eye
tracking dataset - MQA-RC, in which participants read movie plots and answered
pre-defined questions. We compare state of the art networks based on long
short-term memory (LSTM), convolutional neural models (CNN) and XLNet
Transformer architectures. We find that higher similarity to human attention
and performance significantly correlates to the LSTM and CNN models. However,
we show this relationship does not hold true for the XLNet models -- despite
the fact that the XLNet performs best on this challenging task. Our results
suggest that different architectures seem to learn rather different neural
attention strategies and similarity of neural to human attention does not
guarantee best performance.
| 2,020 | Computation and Language |
Multilingual Argument Mining: Datasets and Analysis | The growing interest in argument mining and computational argumentation
brings with it a plethora of Natural Language Understanding (NLU) tasks and
corresponding datasets. However, as with many other NLU tasks, the dominant
language is English, with resources in other languages being few and far
between. In this work, we explore the potential of transfer learning using the
multilingual BERT model to address argument mining tasks in non-English
languages, based on English datasets and the use of machine translation. We
show that such methods are well suited for classifying the stance of arguments
and detecting evidence, but less so for assessing the quality of arguments,
presumably because quality is harder to preserve under translation. In
addition, focusing on the translate-train approach, we show how the choice of
languages for translation, and the relations among them, affect the accuracy of
the resultant model. Finally, to facilitate evaluation of transfer learning on
argument mining tasks, we provide a human-generated dataset with more than 10k
arguments in multiple languages, as well as machine translation of the English
datasets.
| 2,020 | Computation and Language |
RuSemShift: a dataset of historical lexical semantic change in Russian | We present RuSemShift, a large-scale manually annotated test set for the task
of semantic change modeling in Russian for two long-term time period pairs:
from the pre-Soviet through the Soviet times and from the Soviet through the
post-Soviet times. Target words were annotated by multiple crowd-source
workers. The annotation process was organized following the DURel framework and
was based on sentence contexts extracted from the Russian National Corpus.
Additionally, we report the performance of several distributional approaches on
RuSemShift, achieving promising results, which at the same time leave room for
other researchers to improve.
| 2,020 | Computation and Language |
Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource
Language | Low-resource languages such as Filipino suffer from data scarcity which makes
it challenging to develop NLP applications for Filipino language. The use of
Transfer Learning (TL) techniques alleviates this problem in low-resource
setting. In recent years, transformer-based models are proven to be effective
in low-resource tasks but faces challenges in accessibility due to its high
compute and memory requirements. For this reason, there's a need for a cheaper
but effective alternative. This paper has three contributions. First, release a
pre-trained AWD-LSTM language model for Filipino language. Second, benchmark
AWD-LSTM in the Hate Speech classification task and show that it performs on
par with transformer-based models. Third, analyze the the performance of
AWD-LSTM in low-resource setting using degradation test and compare it with
transformer-based models.
-----
Ang mga low-resource languages tulad ng Filipino ay gipit sa accessible na
datos kaya't mahirap gumawa ng mga applications sa wikang ito. Ang mga Transfer
Learning (TL) techniques ay malaking tulong para sa low-resource setting o mga
pagkakataong gipit sa datos. Sa mga nagdaang taon, nanaig ang mga
transformer-based TL techniques pagdating sa low-resource tasks ngunit ito ay
mataas na compute and memory requirements kaya nangangailangan ng mas mura pero
epektibong alternatibo. Ang papel na ito ay may tatlong kontribusyon. Una,
maglabas ng pre-trained AWD-LSTM language model sa wikang Filipino upang maging
tuntungan sa pagbuo ng mga NLP applications sa wikang Filipino. Pangalawa, mag
benchmark ng AWD-LSTM sa Hate Speech classification task at ipakita na kayang
nitong makipagsabayan sa mga transformer-based models. Pangatlo, suriin ang
performance ng AWD-LSTM sa low-resource setting gamit ang degradation test at
ikumpara ito sa mga transformer-based models.
| 2,020 | Computation and Language |
Demographic Representation and Collective Storytelling in the Me Too
Twitter Hashtag Activism Movement | The #MeToo movement on Twitter has drawn attention to the pervasive nature of
sexual harassment and violence. While #MeToo has been praised for providing
support for self-disclosures of harassment or violence and shifting societal
response, it has also been criticized for exemplifying how women of color have
been discounted for their historical contributions to and excluded from
feminist movements. Through an analysis of over 600,000 tweets from over
256,000 unique users, we examine online #MeToo conversations across gender and
racial/ethnic identities and the topics that each demographic emphasized. We
found that tweets authored by white women were overrepresented in the movement
compared to other demographics, aligning with criticism of unequal
representation. We found that intersected identities contributed differing
narratives to frame the movement, co-opted the movement to raise visibility in
parallel ongoing movements, employed the same hashtags both critically and
supportively, and revived and created new hashtags in response to pivotal
moments. Notably, tweets authored by black women often expressed emotional
support and were critical about differential treatment in the justice system
and by police. In comparison, tweets authored by white women and men often
highlighted sexual harassment and violence by public figures and weaved in more
general political discussions. We discuss the implications of work for digital
activism research and design including suggestions to raise visibility by those
who were under-represented in this hashtag activism movement. Content warning:
this article discusses issues of sexual harassment and violence.
| 2,020 | Computation and Language |
XL-WiC: A Multilingual Benchmark for Evaluating Semantic
Contextualization | The ability to correctly model distinct meanings of a word is crucial for the
effectiveness of semantic representation techniques. However, most existing
evaluation benchmarks for assessing this criterion are tied to sense
inventories (usually WordNet), restricting their usage to a small subset of
knowledge-based representation techniques. The Word-in-Context dataset (WiC)
addresses the dependence on sense inventories by reformulating the standard
disambiguation task as a binary classification problem; but, it is limited to
the English language. We put forward a large multilingual benchmark, XL-WiC,
featuring gold standards in 12 new languages from varied language families and
with different degrees of resource availability, opening room for evaluation
scenarios such as zero-shot cross-lingual transfer. We perform a series of
experiments to determine the reliability of the datasets and to set performance
baselines for several recent contextualized multilingual models. Experimental
results show that even when no tagged instances are available for a target
language, models trained solely on the English data can attain competitive
performance in the task of distinguishing different meanings of a word, even
for distant languages. XL-WiC is available at
https://pilehvar.github.io/xlwic/.
| 2,020 | Computation and Language |
Does my multimodal model learn cross-modal interactions? It's harder to
tell than you might think! | Modeling expressive cross-modal interactions seems crucial in multimodal
tasks, such as visual question answering. However, sometimes high-performing
black-box algorithms turn out to be mostly exploiting unimodal signals in the
data. We propose a new diagnostic tool, empirical multimodally-additive
function projection (EMAP), for isolating whether or not cross-modal
interactions improve performance for a given model on a given task. This
function projection modifies model predictions so that cross-modal interactions
are eliminated, isolating the additive, unimodal structure. For seven
image+text classification tasks (on each of which we set new state-of-the-art
benchmarks), we find that, in many cases, removing cross-modal interactions
results in little to no performance degradation. Surprisingly, this holds even
when expressive models, with capacity to consider interactions, otherwise
outperform less expressive models; thus, performance improvements, even when
present, often cannot be attributed to consideration of cross-modal feature
interactions. We hence recommend that researchers in multimodal machine
learning report the performance not only of unimodal baselines, but also the
EMAP of their best-performing model.
| 2,020 | Computation and Language |
With Little Power Comes Great Responsibility | Despite its importance to experimental design, statistical power (the
probability that, given a real effect, an experiment will reject the null
hypothesis) has largely been ignored by the NLP community. Underpowered
experiments make it more difficult to discern the difference between
statistical noise and meaningful model improvements, and increase the chances
of exaggerated findings. By meta-analyzing a set of existing NLP papers and
datasets, we characterize typical power for a variety of settings and conclude
that underpowered experiments are common in the NLP literature. In particular,
for several tasks in the popular GLUE benchmark, small test sets mean that most
attempted comparisons to state of the art models will not be adequately
powered. Similarly, based on reasonable assumptions, we find that the most
typical experimental design for human rating studies will be underpowered to
detect small model differences, of the sort that are frequently studied. For
machine translation, we find that typical test sets of 2000 sentences have
approximately 75% power to detect differences of 1 BLEU point. To improve the
situation going forward, we give an overview of best practices for power
analysis in NLP and release a series of notebooks to assist with future power
analyses.
| 2,020 | Computation and Language |
Enhancing the Identification of Cyberbullying through Participant Roles | Cyberbullying is a prevalent social problem that inflicts detrimental
consequences to the health and safety of victims such as psychological
distress, anti-social behaviour, and suicide. The automation of cyberbullying
detection is a recent but widely researched problem, with current research
having a strong focus on a binary classification of bullying versus
non-bullying. This paper proposes a novel approach to enhancing cyberbullying
detection through role modeling. We utilise a dataset from ASKfm to perform
multi-class classification to detect participant roles (e.g. victim, harasser).
Our preliminary results demonstrate promising performance including 0.83 and
0.76 of F1-score for cyberbullying and role classification respectively,
outperforming baselines.
| 2,020 | Computation and Language |
Probing for Multilingual Numerical Understanding in Transformer-Based
Language Models | Natural language numbers are an example of compositional structures, where
larger numbers are composed of operations on smaller numbers. Given that
compositional reasoning is a key to natural language understanding, we propose
novel multilingual probing tasks tested on DistilBERT, XLM, and BERT to
investigate for evidence of compositional reasoning over numerical data in
various natural language number systems. By using both grammaticality judgment
and value comparison classification tasks in English, Japanese, Danish, and
French, we find evidence that the information encoded in these pretrained
models' embeddings is sufficient for grammaticality judgments but generally not
for value comparisons. We analyze possible reasons for this and discuss how our
tasks could be extended in further studies.
| 2,020 | Computation and Language |
A Multi-Modal Method for Satire Detection using Textual and Visual Cues | Satire is a form of humorous critique, but it is sometimes misinterpreted by
readers as legitimate news, which can lead to harmful consequences. We observe
that the images used in satirical news articles often contain absurd or
ridiculous content and that image manipulation is used to create fictional
scenarios. While previous work have studied text-based methods, in this work we
propose a multi-modal approach based on state-of-the-art visiolinguistic model
ViLBERT. To this end, we create a new dataset consisting of images and
headlines of regular and satirical news for the task of satire detection. We
fine-tune ViLBERT on the dataset and train a convolutional neural network that
uses an image forensics technique. Evaluation on the dataset shows that our
proposed multi-modal approach outperforms image-only, text-only, and simple
fusion baselines.
| 2,020 | Computation and Language |
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint
Aspect-Sentiment Topic Embedding | Aspect-based sentiment analysis of review texts is of great value for
understanding user feedback in a fine-grained manner. It has in general two
sub-tasks: (i) extracting aspects from each review, and (ii) classifying
aspect-based reviews by sentiment polarity. In this paper, we propose a
weakly-supervised approach for aspect-based sentiment analysis, which uses only
a few keywords describing each aspect/sentiment without using any labeled
examples. Existing methods are either designed only for one of the sub-tasks,
neglecting the benefit of coupling both, or are based on topic models that may
contain overlapping concepts. We propose to first learn <sentiment, aspect>
joint topic embeddings in the word embedding space by imposing regularizations
to encourage topic distinctiveness, and then use neural models to generalize
the word-level discriminative information by pre-training the classifiers with
embedding-based predictions and self-training them on unlabeled data. Our
comprehensive performance analysis shows that our method generates quality
joint topics and outperforms the baselines significantly (7.4% and 5.1%
F1-score gain on average for aspect and sentiment classification respectively)
on benchmark datasets. Our code and data are available at
https://github.com/teapot123/JASen.
| 2,020 | Computation and Language |
Language Networks: a Practical Approach | This manuscript provides a short and practical introduction to the topic of
language networks. This text aims at assisting researchers with no practical
experience in text and/or network analysis. We provide a practical tutorial on
how to model and characterize texts using network-based features. In this
tutorial, we also include examples of pre-processing and network
representations. A brief description of the main tasks allying network science
and text analysis is also provided. A further development of this text shall
include a practical description of network classification via machine learning
methods.
| 2,020 | Computation and Language |
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and
Relation Transferring | Taxonomy is not only a fundamental form of knowledge representation, but also
crucial to vast knowledge-rich applications, such as question answering and web
search. Most existing taxonomy construction methods extract hypernym-hyponym
entity pairs to organize a "universal" taxonomy. However, these generic
taxonomies cannot satisfy user's specific interest in certain areas and
relations. Moreover, the nature of instance taxonomy treats each node as a
single word, which has low semantic coverage. In this paper, we propose a
method for seed-guided topical taxonomy construction, which takes a corpus and
a seed taxonomy described by concept names as input, and constructs a more
complete taxonomy based on user's interest, wherein each node is represented by
a cluster of coherent terms. Our framework, CoRel, has two modules to fulfill
this goal. A relation transferring module learns and transfers the user's
interested relation along multiple paths to expand the seed taxonomy structure
in width and depth. A concept learning module enriches the semantics of each
concept node by jointly embedding the taxonomy and text. Comprehensive
experiments conducted on real-world datasets show that Corel generates
high-quality topical taxonomies and outperforms all the baselines
significantly.
| 2,020 | Computation and Language |
Sensitivity of BLANC to human-scored qualities of text summaries | We explore the sensitivity of a document summary quality estimator, BLANC, to
human assessment of qualities for the same summaries. In our human evaluations,
we distinguish five summary qualities, defined by how fluent, understandable,
informative, compact, and factually correct the summary is. We make the case
for optimal BLANC parameters, at which the BLANC sensitivity to almost all of
summary qualities is about as good as the sensitivity of a human annotator.
| 2,020 | Computation and Language |
"What Are You Trying to Do?" Semantic Typing of Event Processes | This paper studies a new cognitively motivated semantic typing task,
multi-axis event process typing, that, given an event process, attempts to
infer free-form type labels describing (i) the type of action made by the
process and (ii) the type of object the process seeks to affect. This task is
inspired by computational and cognitive studies of event understanding, which
suggest that understanding processes of events is often directed by recognizing
the goals, plans or intentions of the protagonist(s). We develop a large
dataset containing over 60k event processes, featuring ultra fine-grained
typing on both the action and object type axes with very large ($10^3\sim
10^4$) label vocabularies. We then propose a hybrid learning framework, P2GT,
which addresses the challenging typing problem with indirect supervision from
glosses1and a joint learning-to-rank framework. As our experiments indicate,
P2GT supports identifying the intent of processes, as well as the fine semantic
type of the affected object. It also demonstrates the capability of handling
few-shot cases, and strong generalizability on out-of-domain event processes.
| 2,020 | Computation and Language |
Joint Constrained Learning for Event-Event Relation Extraction | Understanding natural language involves recognizing how multiple event
mentions structurally and temporally interact with each other. In this process,
one can induce event complexes that organize multi-granular events with
temporal order and membership relations interweaving among them. Due to the
lack of jointly labeled data for these relational phenomena and the restriction
on the structures they articulate, we propose a joint constrained learning
framework for modeling event-event relations. Specifically, the framework
enforces logical constraints within and across multiple temporal and subevent
relations by converting these constraints into differentiable learning
objectives. We show that our joint constrained learning approach effectively
compensates for the lack of jointly labeled data, and outperforms SOTA methods
on benchmarks for both temporal relation extraction and event hierarchy
construction, replacing a commonly used but more expensive global inference
process. We also present a promising case study showing the effectiveness of
our approach in inducing event complexes on an external corpus.
| 2,021 | Computation and Language |
Vokenization: Improving Language Understanding with Contextualized,
Visual-Grounded Supervision | Humans learn language by listening, speaking, writing, reading, and also, via
interaction with the multimodal real world. Existing language pre-training
frameworks show the effectiveness of text-only self-supervision while we
explore the idea of a visually-supervised language model in this paper. We find
that the main reason hindering this exploration is the large divergence in
magnitude and distributions between the visually-grounded language datasets and
pure-language corpora. Therefore, we develop a technique named "vokenization"
that extrapolates multimodal alignments to language-only data by contextually
mapping language tokens to their related images (which we call "vokens"). The
"vokenizer" is trained on relatively small image captioning datasets and we
then apply it to generate vokens for large language corpora. Trained with these
contextually generated vokens, our visually-supervised language models show
consistent improvements over self-supervised alternatives on multiple
pure-language tasks such as GLUE, SQuAD, and SWAG. Code and pre-trained models
publicly available at https://github.com/airsplay/vokenization
| 2,020 | Computation and Language |
Google Crowdsourced Speech Corpora and Related Open-Source Resources for
Low-Resource Languages and Dialects: An Overview | This paper presents an overview of a program designed to address the growing
need for developing freely available speech resources for under-represented
languages. At present we have released 38 datasets for building text-to-speech
and automatic speech recognition applications for languages and dialects of
South and Southeast Asia, Africa, Europe and South America. The paper describes
the methodology used for developing such corpora and presents some of our
findings that could benefit under-represented language communities.
| 2,020 | Computation and Language |
A Self-supervised Representation Learning of Sentence Structure for
Authorship Attribution | Syntactic structure of sentences in a document substantially informs about
its authorial writing style. Sentence representation learning has been widely
explored in recent years and it has been shown that it improves the
generalization of different downstream tasks across many domains. Even though
utilizing probing methods in several studies suggests that these learned
contextual representations implicitly encode some amount of syntax, explicit
syntactic information further improves the performance of deep neural models in
the domain of authorship attribution. These observations have motivated us to
investigate the explicit representation learning of syntactic structure of
sentences. In this paper, we propose a self-supervised framework for learning
structural representations of sentences. The self-supervised network contains
two components; a lexical sub-network and a syntactic sub-network which take
the sequence of words and their corresponding structural labels as the input,
respectively. Due to the n-to-1 mapping of words to their structural labels,
each word will be embedded into a vector representation which mainly carries
structural information. We evaluate the learned structural representations of
sentences using different probing tasks, and subsequently utilize them in the
authorship attribution task. Our experimental results indicate that the
structural embeddings significantly improve the classification tasks when
concatenated with the existing pre-trained word embeddings.
| 2,022 | Computation and Language |
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised
Approach | Given a document and a target aspect (e.g., a topic of interest),
aspect-based abstractive summarization attempts to generate a summary with
respect to the aspect. Previous studies usually assume a small pre-defined set
of aspects and fall short of summarizing on other diverse topics. In this work,
we study summarizing on arbitrary aspects relevant to the document, which
significantly expands the application of the task in practice. Due to the lack
of supervision data, we develop a new weak supervision construction method and
an aspect modeling scheme, both of which integrate rich external knowledge
sources such as ConceptNet and Wikipedia. Experiments show our approach
achieves performance boosts on summarizing both real and synthetic documents
given pre-defined or arbitrary aspects.
| 2,020 | Computation and Language |
A Graph Representation of Semi-structured Data for Web Question
Answering | The abundant semi-structured data on the Web, such as HTML-based tables and
lists, provide commercial search engines a rich information source for question
answering (QA). Different from plain text passages in Web documents, Web tables
and lists have inherent structures, which carry semantic correlations among
various elements in tables and lists. Many existing studies treat tables and
lists as flat documents with pieces of text and do not make good use of
semantic information hidden in structures. In this paper, we propose a novel
graph representation of Web tables and lists based on a systematic
categorization of the components in semi-structured data as well as their
relations. We also develop pre-training and reasoning techniques on the graph
model for the QA task. Extensive experiments on several real datasets collected
from a commercial engine verify the effectiveness of our approach. Our method
improves F1 score by 3.90 points over the state-of-the-art baselines.
| 2,020 | Computation and Language |
Unsupervised Relation Extraction from Language Models using Constrained
Cloze Completion | We show that state-of-the-art self-supervised language models can be readily
used to extract relations from a corpus without the need to train a fine-tuned
extractive head. We introduce RE-Flex, a simple framework that performs
constrained cloze completion over pretrained language models to perform
unsupervised relation extraction. RE-Flex uses contextual matching to ensure
that language model predictions matches supporting evidence from the input
corpus that is relevant to a target relation. We perform an extensive
experimental study over multiple relation extraction benchmarks and demonstrate
that RE-Flex outperforms competing unsupervised relation extraction methods
based on pretrained language models by up to 27.8 $F_1$ points compared to the
next-best method. Our results show that constrained inference queries against a
language model can enable accurate unsupervised relation extraction.
| 2,020 | Computation and Language |
Modeling Protagonist Emotions for Emotion-Aware Storytelling | Emotions and their evolution play a central role in creating a captivating
story. In this paper, we present the first study on modeling the emotional
trajectory of the protagonist in neural storytelling. We design methods that
generate stories that adhere to given story titles and desired emotion arcs for
the protagonist. Our models include Emotion Supervision (EmoSup) and two
Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards
designed to regularize the story generation process through reinforcement
learning. Our automatic and manual evaluations demonstrate that these models
are significantly better at generating stories that follow the desired emotion
arcs compared to baseline methods, without sacrificing story quality.
| 2,020 | Computation and Language |
Semantically-Aligned Universal Tree-Structured Solver for Math Word
Problems | A practical automatic textual math word problems (MWPs) solver should be able
to solve various textual MWPs while most existing works only focused on
one-unknown linear MWPs. Herein, we propose a simple but efficient method
called Universal Expression Tree (UET) to make the first attempt to represent
the equations of various MWPs uniformly. Then a semantically-aligned universal
tree-structured solver (SAU-Solver) based on an encoder-decoder framework is
proposed to resolve multiple types of MWPs in a unified model, benefiting from
our UET representation. Our SAU-Solver generates a universal expression tree
explicitly by deciding which symbol to generate according to the generated
symbols' semantic meanings like human solving MWPs. Besides, our SAU-Solver
also includes a novel subtree-level semanticallyaligned regularization to
further enforce the semantic constraints and rationality of the generated
expression tree by aligning with the contextual information. Finally, to
validate the universality of our solver and extend the research boundary of
MWPs, we introduce a new challenging Hybrid Math Word Problems dataset (HMWP),
consisting of three types of MWPs. Experimental results on several MWPs
datasets show that our model can solve universal types of MWPs and outperforms
several state-of-the-art models.
| 2,020 | Computation and Language |
A Wrong Answer or a Wrong Question? An Intricate Relationship between
Question Reformulation and Answer Selection in Conversational Question
Answering | The dependency between an adequate question formulation and correct answer
selection is a very intriguing but still underexplored area. In this paper, we
show that question rewriting (QR) of the conversational context allows to shed
more light on this phenomenon and also use it to evaluate robustness of
different answer selection approaches. We introduce a simple framework that
enables an automated analysis of the conversational question answering (QA)
performance using question rewrites, and present the results of this analysis
on the TREC CAsT and QuAC (CANARD) datasets. Our experiments uncover
sensitivity to question formulation of the popular state-of-the-art models for
reading comprehension and passage ranking. Our results demonstrate that the
reading comprehension model is insensitive to question formulation, while the
passage ranking changes dramatically with a little variation in the input
question. The benefit of QR is that it allows us to pinpoint and group such
cases automatically. We show how to use this methodology to verify whether QA
models are really learning the task or just finding shortcuts in the dataset,
and better understand the frequent types of error they make.
| 2,022 | Computation and Language |
Learning Word Representations for Tunisian Sentiment Analysis | Tunisians on social media tend to express themselves in their local dialect
using Latin script (TUNIZI). This raises an additional challenge to the process
of exploring and recognizing online opinions. To date, very little work has
addressed TUNIZI sentiment analysis due to scarce resources for training an
automated system. In this paper, we focus on the Tunisian dialect sentiment
analysis used on social media. Most of the previous work used machine learning
techniques combined with handcrafted features. More recently, Deep Neural
Networks were widely used for this task, especially for the English language.
In this paper, we explore the importance of various unsupervised word
representations (word2vec, BERT) and we investigate the use of Convolutional
Neural Networks and Bidirectional Long Short-Term Memory. Without using any
kind of handcrafted features, our experimental results on two publicly
available datasets showed comparable performances to other languages.
| 2,020 | Computation and Language |
fugashi, a Tool for Tokenizing Japanese in Python | Recent years have seen an increase in the number of large-scale multilingual
NLP projects. However, even in such projects, languages with special processing
requirements are often excluded. One such language is Japanese. Japanese is
written without spaces, tokenization is non-trivial, and while high quality
open source tokenizers exist they can be hard to use and lack English
documentation. This paper introduces fugashi, a MeCab wrapper for Python, and
gives an introduction to tokenizing Japanese.
| 2,020 | Computation and Language |
Memformer: A Memory-Augmented Transformer for Sequence Modeling | Transformers have reached remarkable success in sequence modeling. However,
these models have efficiency issues as they need to store all the history
token-level representations as memory. We present Memformer, an efficient
neural network for sequence modeling, that utilizes an external dynamic memory
to encode and retrieve past information. Our model achieves linear time
complexity and constant memory space complexity when processing long sequences.
We also propose a new optimization scheme, memory replay back-propagation
(MRBP), which promotes long-range back-propagation through time with a
significantly reduced memory requirement. Experimental results show that
Memformer has achieved comparable performance compared to the baselines by
using 8.1x less memory space and 3.2x faster on inference. Analysis of the
attention pattern shows that our external memory slots can encode and retain
important information through timesteps.
| 2,022 | Computation and Language |
No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet
Detection | The sudden widespread menace created by the present global pandemic COVID-19
has had an unprecedented effect on our lives. Man-kind is going through
humongous fear and dependence on social media like never before. Fear
inevitably leads to panic, speculations, and the spread of misinformation. Many
governments have taken measures to curb the spread of such misinformation for
public well being. Besides global measures, to have effective outreach, systems
for demographically local languages have an important role to play in this
effort. Towards this, we propose an approach to detect fake news about COVID-19
early on from social media, such as tweets, for multiple Indic-Languages
besides English. In addition, we also create an annotated dataset of Hindi and
Bengali tweet for fake news detection. We propose a BERT based model augmented
with additional relevant features extracted from Twitter to identify fake
tweets. To expand our approach to multiple Indic languages, we resort to mBERT
based model which is fine-tuned over created dataset in Hindi and Bengali. We
also propose a zero-shot learning approach to alleviate the data scarcity issue
for such low resource languages. Through rigorous experiments, we show that our
approach reaches around 89% F-Score in fake tweet detection which supercedes
the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark
for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model
achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our
zero-shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali
Tweets without any annotated data, which clearly indicates the efficacy of our
approach.
| 2,020 | Computation and Language |
DA-Transformer: Distance-aware Transformer | Transformer has achieved great success in the NLP field by composing various
advanced models like BERT and GPT. However, Transformer and its existing
variants may not be optimal in capturing token distances because the position
or distance embeddings used by these methods usually cannot keep the precise
information of real distances, which may not be beneficial for modeling the
orders and relations of contexts. In this paper, we propose DA-Transformer,
which is a distance-aware Transformer that can exploit the real distance. We
propose to incorporate the real distances between tokens to re-scale the raw
self-attention weights, which are computed by the relevance between attention
query and key. Concretely, in different self-attention heads the relative
distance between each pair of tokens is weighted by different learnable
parameters, which control the different preferences on long- or short-term
information of these heads. Since the raw weighted real distances may not be
optimal for adjusting self-attention weights, we propose a learnable sigmoid
function to map them into re-scaled coefficients that have proper ranges. We
first clip the raw self-attention weights via the ReLU function to keep
non-negativity and introduce sparsity, and then multiply them with the
re-scaled coefficients to encode real distance information into self-attention.
Extensive experiments on five benchmark datasets show that DA-Transformer can
effectively improve the performance of many tasks and outperform the vanilla
Transformer and its several variants.
| 2,021 | Computation and Language |
Pair the Dots: Jointly Examining Training History and Test Stimuli for
Model Interpretability | Any prediction from a model is made by a combination of learning history and
test stimuli. This provides significant insights for improving model
interpretability: {\it because of which part(s) of which training example(s),
the model attends to which part(s) of a test example}. Unfortunately, existing
methods to interpret a model's predictions are only able to capture a single
aspect of either test stimuli or learning history, and evidences from both are
never combined or integrated. In this paper, we propose an efficient and
differentiable approach to make it feasible to interpret a model's prediction
by jointly examining training history and test stimuli. Test stimuli is first
identified by gradient-based methods, signifying {\it the part of a test
example that the model attends to}. The gradient-based saliency scores are then
propagated to training examples using influence functions to identify {\it
which part(s) of which training example(s)} make the model attends to the test
stimuli. The system is differentiable and time efficient: the adoption of
saliency scores from gradient-based methods allows us to efficiently trace a
model's prediction through test stimuli, and then back to training examples
through influence functions. We demonstrate that the proposed methodology
offers clear explanations about neural model decisions, along with being useful
for performing error analysis, crafting adversarial examples and fixing
erroneously classified examples.
| 2,020 | Computation and Language |
Neural Databases | In recent years, neural networks have shown impressive performance gains on
long-standing AI problems, and in particular, answering queries from natural
language text. These advances raise the question of whether they can be
extended to a point where we can relax the fundamental assumption of database
management, namely, that our data is represented as fields of a pre-defined
schema.
This paper presents a first step in answering that question. We describe
NeuralDB, a database system with no pre-defined schema, in which updates and
queries are given in natural language. We develop query processing techniques
that build on the primitives offered by the state of the art Natural Language
Processing methods.
We begin by demonstrating that at the core, recent NLP transformers, powered
by pre-trained language models, can answer select-project-join queries if they
are given the exact set of relevant facts. However, they cannot scale to
non-trivial databases and cannot perform aggregation queries. Based on these
findings, we describe a NeuralDB architecture that runs multiple Neural SPJ
operators in parallel, each with a set of database sentences that can produce
one of the answers to the query. The result of these operators is fed to an
aggregation operator if needed. We describe an algorithm that learns how to
create the appropriate sets of facts to be fed into each of the Neural SPJ
operators. Importantly, this algorithm can be trained by the Neural SPJ
operator itself. We experimentally validate the accuracy of NeuralDB and its
components, showing that we can answer queries over thousands of sentences with
very high accuracy.
| 2,020 | Computation and Language |
Medical Code Assignment with Gated Convolution and Note-Code Interaction | Medical code assignment from clinical text is a fundamental task in clinical
information system management. As medical notes are typically lengthy and the
medical coding system's code space is large, this task is a long-standing
challenge. Recent work applies deep neural network models to encode the medical
notes and assign medical codes to clinical documents. However, these methods
are still ineffective as they do not fully encode and capture the lengthy and
rich semantic information of medical notes nor explicitly exploit the
interactions between the notes and codes. We propose a novel method, gated
convolutional neural networks, and a note-code interaction (GatedCNN-NCI), for
automatic medical code assignment to overcome these challenges. Our methods
capture the rich semantic information of the lengthy clinical text for better
representation by utilizing embedding injection and gated information
propagation in the medical note encoding module. With a novel note-code
interaction design and a graph message passing mechanism, we explicitly capture
the underlying dependency between notes and codes, enabling effective code
prediction. A weight sharing scheme is further designed to decrease the number
of trainable parameters. Empirical experiments on real-world clinical datasets
show that our proposed model outperforms state-of-the-art models in most cases,
and our model size is on par with light-weighted baselines.
| 2,022 | Computation and Language |
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime
with Search | Despite transformers' impressive accuracy, their computational cost is often
prohibitive to use with limited computational resources. Most previous
approaches to improve inference efficiency require a separate model for each
possible computational budget. In this paper, we extend PoWER-BERT (Goyal et
al., 2020) and propose Length-Adaptive Transformer that can be used for various
inference scenarios after one-shot training. We train a transformer with
LengthDrop, a structural variant of dropout, which stochastically determines a
sequence length at each layer. We then conduct a multi-objective evolutionary
search to find a length configuration that maximizes the accuracy and minimizes
the efficiency metric under any given computational budget. Additionally, we
significantly extend the applicability of PoWER-BERT beyond sequence-level
classification into token-level classification with Drop-and-Restore process
that drops word-vectors temporarily in intermediate layers and restores at the
last layer if necessary. We empirically verify the utility of the proposed
approach by demonstrating the superior accuracy-efficiency trade-off under
various setups, including span-based question answering and text
classification. Code is available at
https://github.com/clovaai/length-adaptive-transformer.
| 2,021 | Computation and Language |
Chinese Lexical Simplification | Lexical simplification has attracted much attention in many languages, which
is the process of replacing complex words in a given sentence with simpler
alternatives of equivalent meaning. Although the richness of vocabulary in
Chinese makes the text very difficult to read for children and non-native
speakers, there is no research work for Chinese lexical simplification (CLS)
task. To circumvent difficulties in acquiring annotations, we manually create
the first benchmark dataset for CLS, which can be used for evaluating the
lexical simplification systems automatically. In order to acquire more thorough
comparison, we present five different types of methods as baselines to generate
substitute candidates for the complex word that include synonym-based approach,
word embedding-based approach, pretrained language model-based approach,
sememe-based approach, and a hybrid approach. Finally, we design the
experimental evaluation of these baselines and discuss their advantages and
disadvantages. To our best knowledge, this is the first study for CLS task.
| 2,020 | Computation and Language |
Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical
Supervision from Extractive Summaries | The difficulty of generating coherent long texts lies in the fact that
existing models overwhelmingly focus on predicting local words, and cannot make
high level plans on what to generate or capture the high-level discourse
dependencies between chunks of texts. Inspired by human writing processes,
where a list of bullet points or a catalog is first outlined, and then each
bullet point is expanded to form the whole article, we propose {\it SOE}, a
pipelined system that involves of summarizing, outlining and elaborating for
long text generation: the model first outlines the summaries for different
segments of long texts, and then elaborates on each bullet point to generate
the corresponding segment. To avoid the labor-intensive process of summary
soliciting, we propose the {\it reconstruction} strategy, which extracts
segment summaries in an unsupervised manner by selecting its most informative
part to reconstruct the segment. The proposed generation system comes with the
following merits: (1) the summary provides high-level guidance for text
generation and avoids the local minimum of individual word predictions; (2) the
high-level discourse dependencies are captured in the conditional dependencies
between summaries and are preserved during the summary expansion process and
(3) additionally, we are able to consider significantly more contexts by
representing contexts as concise summaries. Extensive experiments demonstrate
that SOE produces long texts with significantly better quality, along with
faster convergence speed.
| 2,022 | Computation and Language |
AutoADR: Automatic Model Design for Ad Relevance | Large-scale pre-trained models have attracted extensive attention in the
research community and shown promising results on various tasks of natural
language processing. However, these pre-trained models are memory and
computation intensive, hindering their deployment into industrial online
systems like Ad Relevance. Meanwhile, how to design an effective yet efficient
model architecture is another challenging problem in online Ad Relevance.
Recently, AutoML shed new lights on architecture design, but how to integrate
it with pre-trained language models remains unsettled. In this paper, we
propose AutoADR (Automatic model design for AD Relevance) -- a novel end-to-end
framework to address this challenge, and share our experience to ship these
cutting-edge techniques into online Ad Relevance system at Microsoft Bing.
Specifically, AutoADR leverages a one-shot neural architecture search algorithm
to find a tailored network architecture for Ad Relevance. The search process is
simultaneously guided by knowledge distillation from a large pre-trained
teacher model (e.g. BERT), while taking the online serving constraints (e.g.
memory and latency) into consideration. We add the model designed by AutoADR as
a sub-model into the production Ad Relevance model. This additional sub-model
improves the Precision-Recall AUC (PR AUC) on top of the original Ad Relevance
model by 2.65X of the normalized shipping bar. More importantly, adding this
automatically designed sub-model leads to a statistically significant 4.6%
Bad-Ad ratio reduction in online A/B testing. This model has been shipped into
Microsoft Bing Ad Relevance Production model.
| 2,020 | Computation and Language |
Recipes for Safety in Open-domain Chatbots | Models trained on large unlabeled corpora of human interactions will learn
patterns and mimic behaviors therein, which include offensive or otherwise
toxic behavior and unwanted biases. We investigate a variety of methods to
mitigate these issues in the context of open-domain generative dialogue models.
We introduce a new human-and-model-in-the-loop framework for both training
safer models and for evaluating them, as well as a novel method to distill
safety considerations inside generative models without the use of an external
classifier at deployment time. We conduct experiments comparing these methods
and find our new techniques are (i) safer than existing models as measured by
automatic and human evaluations while (ii) maintaining usability metrics such
as engagingness relative to the state of the art. We then discuss the
limitations of this work by analyzing failure cases of our models.
| 2,021 | Computation and Language |
A Relaxed Matching Procedure for Unsupervised BLI | Recently unsupervised Bilingual Lexicon Induction (BLI) without any parallel
corpus has attracted much research interest. One of the crucial parts in
methods for the BLI task is the matching procedure. Previous works impose a too
strong constraint on the matching and lead to many counterintuitive translation
pairings. Thus, We propose a relaxed matching procedure to find a more precise
matching between two languages. We also find that aligning source and target
language embedding space bidirectionally will bring significant improvement. We
follow the previous iterative framework to conduct experiments. Results on
standard benchmark demonstrate the effectiveness of our proposed method, which
substantially outperforms previous unsupervised methods.
| 2,020 | Computation and Language |
Re-evaluating Evaluation in Text Summarization | Automated evaluation metrics as a stand-in for manual evaluation are an
essential part of the development of text-generation tasks such as text
summarization. However, while the field has progressed, our standard metrics
have not -- for nearly 20 years ROUGE has been the standard evaluation in most
summarization papers. In this paper, we make an attempt to re-evaluate the
evaluation method for text summarization: assessing the reliability of
automatic metrics using top-scoring system outputs, both abstractive and
extractive, on recently popular datasets for both system-level and
summary-level evaluation settings. We find that conclusions about evaluation
metrics on older datasets do not necessarily hold on modern datasets and
systems.
| 2,020 | Computation and Language |
Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction | Semi-supervision is a promising paradigm for Bilingual Lexicon Induction
(BLI) with limited annotations. However, previous semisupervised methods do not
fully utilize the knowledge hidden in annotated and nonannotated data, which
hinders further improvement of their performance. In this paper, we propose a
new semi-supervised BLI framework to encourage the interaction between the
supervised signal and unsupervised alignment. We design two message-passing
mechanisms to transfer knowledge between annotated and non-annotated data,
named prior optimal transport and bi-directional lexicon update respectively.
Then, we perform semi-supervised learning based on a cyclic or a parallel
parameter feeding routine to update our models. Our framework is a general
framework that can incorporate any supervised and unsupervised BLI methods
based on optimal transport. Experimental results on MUSE and VecMap datasets
show significant improvement of our models. Ablation study also proves that the
two-way interaction between the supervised signal and unsupervised alignment
accounts for the gain of the overall performance. Results on distant language
pairs further illustrate the advantage and robustness of our proposed method.
| 2,020 | Computation and Language |
An Investigation on Different Underlying Quantization Schemes for
Pre-trained Language Models | Recently, pre-trained language models like BERT have shown promising
performance on multiple natural language processing tasks. However, the
application of these models has been limited due to their huge size. To reduce
its size, a popular and efficient way is quantization. Nevertheless, most of
the works focusing on BERT quantization adapted primary linear clustering as
the quantization scheme, and few works try to upgrade it. That limits the
performance of quantization significantly. In this paper, we implement k-means
quantization and compare its performance on the fix-precision quantization of
BERT with linear quantization. Through the comparison, we verify that the
effect of the underlying quantization scheme upgrading is underestimated and
there is a huge development potential of k-means quantization. Besides, we also
compare the two quantization schemes on ALBERT models to explore the robustness
differences between different pre-trained models.
| 2,020 | Computation and Language |
Exploiting Spectral Augmentation for Code-Switched Spoken Language
Identification | Spoken language Identification (LID) systems are needed to identify the
language(s) present in a given audio sample, and typically could be the first
step in many speech processing related tasks such as automatic speech
recognition (ASR). Automatic identification of the languages present in a
speech signal is not only scientifically interesting, but also of practical
importance in a multilingual country such as India. In many of the Indian
cities, when people interact with each other, as many as three languages may
get mixed. These may include the official language of that province, Hindi and
English (at times the languages of the neighboring provinces may also get mixed
during these interactions). This makes the spoken LID task extremely
challenging in Indian context. While quite a few LID systems in the context of
Indian languages have been implemented, most such systems have used small scale
speech data collected internally within an organization. In the current work,
we perform spoken LID on three Indian languages (Gujarati, Telugu, and Tamil)
code-mixed with English. This task was organized by the Microsoft research team
as a spoken LID challenge. In our work, we modify the usual spectral
augmentation approach and propose a language mask that discriminates the
language ID pairs, which leads to a noise robust spoken LID system. The
proposed method gives a relative improvement of approximately 3-5% in the LID
accuracy over a baseline system proposed by Microsoft on the three language
pairs for two shared tasks suggested in the challenge.
| 2,020 | Computation and Language |
The EOS Decision and Length Extrapolation | Extrapolation to unseen sequence lengths is a challenge for neural generative
models of language. In this work, we characterize the effect on length
extrapolation of a modeling decision often overlooked: predicting the end of
the generative process through the use of a special end-of-sequence (EOS)
vocabulary item. We study an oracle setting - forcing models to generate to the
correct sequence length at test time - to compare the length-extrapolative
behavior of networks trained to predict EOS (+EOS) with networks not trained to
(-EOS). We find that -EOS substantially outperforms +EOS, for example
extrapolating well to lengths 10 times longer than those seen at training time
in a bracket closing task, as well as achieving a 40% improvement over +EOS in
the difficult SCAN dataset length generalization task. By comparing the hidden
states and dynamics of -EOS and +EOS models, we observe that +EOS models fail
to generalize because they (1) unnecessarily stratify their hidden states by
their linear position is a sequence (structures we call length manifolds) or
(2) get stuck in clusters (which we refer to as length attractors) once the EOS
token is the highest-probability prediction.
| 2,020 | Computation and Language |
Geometry matters: Exploring language examples at the decision boundary | A growing body of recent evidence has highlighted the limitations of natural
language processing (NLP) datasets and classifiers. These include the presence
of annotation artifacts in datasets, classifiers relying on shallow features
like a single word (e.g., if a movie review has the word "romantic", the review
tends to be positive), or unnecessary words (e.g., learning a proper noun to
classify a movie as positive or negative). The presence of such artifacts has
subsequently led to the development of challenging datasets to force the model
to generalize better. While a variety of heuristic strategies, such as
counterfactual examples and contrast sets, have been proposed, the theoretical
justification about what makes these examples difficult for the classifier is
often lacking or unclear. In this paper, using tools from information geometry,
we propose a theoretical way to quantify the difficulty of an example in NLP.
Using our approach, we explore difficult examples for several deep learning
architectures. We discover that both BERT, CNN and fasttext are susceptible to
word substitutions in high difficulty examples. These classifiers tend to
perform poorly on the FIM test set. (generated by sampling and perturbing
difficult examples, with accuracy dropping below 50%). We replicate our
experiments on 5 NLP datasets (YelpReviewPolarity, AGNEWS, SogouNews,
YelpReviewFull and Yahoo Answers). On YelpReviewPolarity we observe a
correlation coefficient of -0.4 between resilience to perturbations and the
difficulty score. Similarly we observe a correlation of 0.35 between the
difficulty score and the empirical success probability of random substitutions.
Our approach is simple, architecture agnostic and can be used to study the
fragilities of text classification models. All the code used will be made
publicly available, including a tool to explore the difficult examples for
other datasets.
| 2,021 | Computation and Language |
Text Classification Using Label Names Only: A Language Model
Self-Training Approach | Current text classification methods typically require a good number of
human-labeled documents as training data, which can be costly and difficult to
obtain in real applications. Humans can perform classification without seeing
any labeled examples but only based on a small set of words describing the
categories to be classified. In this paper, we explore the potential of only
using the label name of each class to train classification models on unlabeled
data, without using any labeled documents. We use pre-trained neural language
models both as general linguistic knowledge sources for category understanding
and as representation learning models for document classification. Our method
(1) associates semantically related words with the label names, (2) finds
category-indicative words and trains the model to predict their implied
categories, and (3) generalizes the model via self-training. We show that our
model achieves around 90% accuracy on four benchmark datasets including topic
and sentiment classification without using any labeled documents but learning
from unlabeled data supervised by at most 3 words (1 in most cases) per class
as the label name.
| 2,020 | Computation and Language |
Learning Improvised Chatbots from Adversarial Modifications of Natural
Language Feedback | The ubiquitous nature of chatbots and their interaction with users generate
an enormous amount of data. Can we improve chatbots using this data? A
self-feeding chatbot improves itself by asking natural language feedback when a
user is dissatisfied with its response and uses this feedback as an additional
training sample. However, user feedback in most cases contains extraneous
sequences hindering their usefulness as a training sample. In this work, we
propose a generative adversarial model that converts noisy feedback into a
plausible natural response in a conversation. The generator's goal is to
convert the feedback into a response that answers the user's previous utterance
and to fool the discriminator which distinguishes feedback from natural
responses. We show that augmenting original training data with these modified
feedback responses improves the original chatbot performance from 69.94% to
75.96% in ranking correct responses on the Personachat dataset, a large
improvement given that the original model is already trained on 131k samples.
| 2,020 | Computation and Language |
Positioning yourself in the maze of Neural Text Generation: A
Task-Agnostic Survey | Neural text generation metamorphosed into several critical natural language
applications ranging from text completion to free form narrative generation. In
order to progress research in text generation, it is critical to absorb the
existing research works and position ourselves in this massively growing field.
Specifically, this paper surveys the fundamental components of modeling
approaches relaying task agnostic impacts across various generation tasks such
as storytelling, summarization, translation etc., In this context, we present
an abstraction of the imperative techniques with respect to learning paradigms,
pretraining, modeling approaches, decoding and the key challenges outstanding
in the field in each of them. Thereby, we deliver a one-stop destination for
researchers in the field to facilitate a perspective on where to situate their
work and how it impacts other closely related generation tasks.
| 2,021 | Computation and Language |
Decoding Methods for Neural Narrative Generation | Narrative generation is an open-ended NLP task in which a model generates a
story given a prompt. The task is similar to neural response generation for
chatbots; however, innovations in response generation are often not applied to
narrative generation, despite the similarity between these tasks. We aim to
bridge this gap by applying and evaluating advances in decoding methods for
neural response generation to neural narrative generation. In particular, we
employ GPT-2 and perform ablations across nucleus sampling thresholds and
diverse decoding hyperparameters -- specifically, maximum mutual information --
analyzing results over multiple criteria with automatic and human evaluation.
We find that (1) nucleus sampling is generally best with thresholds between 0.7
and 0.9; (2) a maximum mutual information objective can improve the quality of
generated stories; and (3) established automatic metrics do not correlate well
with human judgments of narrative quality on any qualitative metric.
| 2,021 | Computation and Language |
Six Attributes of Unhealthy Conversation | We present a new dataset of approximately 44000 comments labeled by
crowdworkers. Each comment is labelled as either 'healthy' or 'unhealthy', in
addition to binary labels for the presence of six potentially 'unhealthy'
sub-attributes: (1) hostile; (2) antagonistic, insulting, provocative or
trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic;
and/or (6) an unfair generalisation. Each label also has an associated
confidence score. We argue that there is a need for datasets which enable
research based on a broad notion of 'unhealthy online conversation'. We build
this typology to encompass a substantial proportion of the individual comments
which contribute to unhealthy online conversation. For some of these
attributes, this is the first publicly available dataset of this scale. We
explore the quality of the dataset, present some summary statistics and initial
models to illustrate the utility of this data, and highlight limitations and
directions for further research.
| 2,021 | Computation and Language |
On Cross-Dataset Generalization in Automatic Detection of Online Abuse | NLP research has attained high performances in abusive language detection as
a supervised classification task. While in research settings, training and test
datasets are usually obtained from similar data samples, in practice systems
are often applied on data that are different from the training set in topic and
class distributions. Also, the ambiguity in class definitions inherited in this
task aggravates the discrepancies between source and target datasets. We
explore the topic bias and the task formulation bias in cross-dataset
generalization. We show that the benign examples in the Wikipedia Detox dataset
are biased towards platform-specific topics. We identify these examples using
unsupervised topic modeling and manual inspection of topics' keywords. Removing
these topics increases cross-dataset generalization, without reducing in-domain
classification performance. For a robust dataset design, we suggest applying
inexpensive unsupervised methods to inspect the collected data and downsize the
non-generalizable content before manually annotating for class labels.
| 2,021 | Computation and Language |
From Language to Language-ish: How Brain-Like is an LSTM's
Representation of Nonsensical Language Stimuli? | The representations generated by many models of language (word embeddings,
recurrent neural networks and transformers) correlate to brain activity
recorded while people read. However, these decoding results are usually based
on the brain's reaction to syntactically and semantically sound language
stimuli. In this study, we asked: how does an LSTM (long short term memory)
language model, trained (by and large) on semantically and syntactically intact
language, represent a language sample with degraded semantic or syntactic
information? Does the LSTM representation still resemble the brain's reaction?
We found that, even for some kinds of nonsensical language, there is a
statistically significant relationship between the brain's activity and the
representations of an LSTM. This indicates that, at least in some instances,
LSTMs and the human brain handle nonsensical data similarly.
| 2,020 | Computation and Language |
A new approach for extracting the conceptual schema of texts based on
the linguistic Thematic Progression theory | The purpose of this article is to present a new approach for the discovery
and labelling of the implicit conceptual schema of texts through the
application of the Thematic Progression theory. The underlying conceptual
schema is the core component for the generation of summaries that are genuinely
consistent with the semantics of the text.
| 2,020 | Computation and Language |
Semantic Label Smoothing for Sequence to Sequence Problems | Label smoothing has been shown to be an effective regularization strategy in
classification, that prevents overfitting and helps in label de-noising.
However, extending such methods directly to seq2seq settings, such as Machine
Translation, is challenging: the large target output space of such problems
makes it intractable to apply label smoothing over all possible outputs. Most
existing approaches for seq2seq settings either do token level smoothing, or
smooth over sequences generated by randomly substituting tokens in the target
sequence. Unlike these works, in this paper, we propose a technique that
smooths over \emph{well formed} relevant sequences that not only have
sufficient n-gram overlap with the target sequence, but are also
\emph{semantically similar}. Our method shows a consistent and significant
improvement over the state-of-the-art techniques on different datasets.
| 2,020 | Computation and Language |
The Language of Food during the Pandemic: Hints about the Dietary
Effects of Covid-19 | We study the language of food on Twitter during the pandemic lockdown in the
United States, focusing on the two month period of March 15 to May 15, 2020.
Specifically, we analyze over770,000 tweets published during the lockdown and
the equivalent period in the five previous years and highlight several worrying
trends. First, we observe that during the lockdown there was a notable shift
from mentions of healthy foods to unhealthy foods. Second, we show an increased
pointwise mutual information of depression hashtags with food-related tweets
posted during the lockdown and an increased association between depression
hashtags and unhealthy foods, tobacco, and alcohol during the lockdown.
| 2,020 | Computation and Language |
Neural Deepfake Detection with Factual Structure of Text | Deepfake detection, the task of automatically discriminating
machine-generated text, is increasingly critical with recent advances in
natural language generative models. Existing approaches to deepfake detection
typically represent documents with coarse-grained representations. However,
they struggle to capture factual structures of documents, which is a
discriminative factor between machine-generated and human-written text
according to our statistical analysis. To address this, we propose a
graph-based model that utilizes the factual structure of a document for
deepfake detection of text. Our approach represents the factual structure of a
given document as an entity graph, which is further utilized to learn sentence
representations with a graph neural network. Sentence representations are then
composed to a document representation for making predictions, where consistent
relations between neighboring sentences are sequentially modeled. Results of
experiments on two public deepfake datasets show that our approach
significantly improves strong base models built with RoBERTa. Model analysis
further indicates that our model can distinguish the difference in the factual
structure between machine-generated text and human-written text.
| 2,020 | Computation and Language |
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware
Medical Dialogue Generation | Developing conversational agents to interact with patients and provide
primary clinical advice has attracted increasing attention due to its huge
application potential, especially in the time of COVID-19 Pandemic. However,
the training of end-to-end neural-based medical dialogue system is restricted
by an insufficient quantity of medical dialogue corpus. In this work, we make
the first attempt to build and release a large-scale high-quality Medical
Dialogue dataset related to 12 types of common Gastrointestinal diseases named
MedDG, with more than 17K conversations collected from the online health
consultation community. Five different categories of entities, including
diseases, symptoms, attributes, tests, and medicines, are annotated in each
conversation of MedDG as additional labels. To push forward the future research
on building expert-sensitive medical dialogue system, we proposes two kinds of
medical dialogue tasks based on MedDG dataset. One is the next entity
prediction and the other is the doctor response generation. To acquire a clear
comprehension on these two medical dialogue tasks, we implement several
state-of-the-art benchmarks, as well as design two dialogue models with a
further consideration on the predicted entities. Experimental results show that
the pre-train language models and other baselines struggle on both tasks with
poor performance in our dataset, and the response quality can be enhanced with
the help of auxiliary entity information. From human evaluation, the simple
retrieval model outperforms several state-of-the-art generative models,
indicating that there still remains a large room for improvement on generating
medically meaningful responses.
| 2,022 | Computation and Language |
Multi-Task Learning for Cross-Lingual Abstractive Summarization | We present a multi-task learning framework for cross-lingual abstractive
summarization to augment training data. Recent studies constructed pseudo
cross-lingual abstractive summarization data to train their neural
encoder-decoders. Meanwhile, we introduce existing genuine data such as
translation pairs and monolingual abstractive summarization data into training.
Our proposed method, Transum, attaches a special token to the beginning of the
input sentence to indicate the target task. The special token enables us to
incorporate the genuine data into the training data easily. The experimental
results show that Transum achieves better performance than the model trained
with only pseudo cross-lingual summarization data. In addition, we achieve the
top ROUGE score on Chinese-English and Arabic-English abstractive
summarization. Moreover, Transum also has a positive effect on machine
translation. Experimental results indicate that Transum improves the
performance from the strong baseline, Transformer, in Chinese-English,
Arabic-English, and English-Japanese translation datasets.
| 2,020 | Computation and Language |
RNNs can generate bounded hierarchical languages with optimal memory | Recurrent neural networks empirically generate natural language with high
syntactic fidelity. However, their success is not well-understood
theoretically. We provide theoretical insight into this success, proving in a
finite-precision setting that RNNs can efficiently generate bounded
hierarchical languages that reflect the scaffolding of natural language syntax.
We introduce Dyck-($k$,$m$), the language of well-nested brackets (of $k$
types) and $m$-bounded nesting depth, reflecting the bounded memory needs and
long-distance dependencies of natural language syntax. The best known results
use $O(k^{\frac{m}{2}})$ memory (hidden units) to generate these languages. We
prove that an RNN with $O(m \log k)$ hidden units suffices, an exponential
reduction in memory, by an explicit construction. Finally, we show that no
algorithm, even with unbounded computation, can suffice with $o(m \log k)$
hidden units.
| 2,020 | Computation and Language |
Named Entity Recognition and Relation Extraction using Enhanced Table
Filling by Contextualized Representations | In this study, a novel method for extracting named entities and relations
from unstructured text based on the table representation is presented. By using
contextualized word embeddings, the proposed method computes representations
for entity mentions and long-range dependencies without complicated
hand-crafted features or neural-network architectures. We also adapt a tensor
dot-product to predict relation labels all at once without resorting to
history-based predictions or search strategies. These advances significantly
simplify the model and algorithm for the extraction of named entities and
relations. Despite its simplicity, the experimental results demonstrate that
the proposed method outperforms the state-of-the-art methods on the CoNLL04 and
ACE05 English datasets. We also confirm that the proposed method achieves a
comparable performance with the state-of-the-art NER models on the ACE05
datasets when multiple sentences are provided for context aggregation.
| 2,022 | Computation and Language |
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow
finer-grained inferences about sentiment to be drawn from the same text,
depending on context. For example, a given text can have different targets
(e.g., neighborhoods) and different aspects (e.g., price or safety), with
different sentiment associated with each target-aspect pair. In this paper, we
investigate whether adding context to self-attention models improves
performance on (T)ABSA. We propose two variants of Context-Guided BERT
(CG-BERT) that learn to distribute attention under different contexts. We first
adapt a context-aware Transformer to produce a CG-BERT that uses context-guided
softmax-attention. Next, we propose an improved Quasi-Attention CG-BERT model
that learns a compositional attention that supports subtractive attention. We
train both models with pretrained BERT on two (T)ABSA datasets: SentiHood and
SemEval-2014 (Task 4). Both models achieve new state-of-the-art results with
our QACG-BERT model having the best performance. Furthermore, we provide
analyses of the impact of context in the our proposed models. Our work provides
more evidence for the utility of adding context-dependencies to pretrained
self-attention-based language models for context-based natural language tasks.
| 2,021 | Computation and Language |
Natural Language Rationales with Full-Stack Visual Reasoning: From
Pixels to Semantic Frames to Commonsense Graphs | Natural language rationales could provide intuitive, higher-level
explanations that are easily understandable by humans, complementing the more
broadly studied lower-level explanations based on gradients or attention
weights. We present the first study focused on generating natural language
rationales across several complex visual reasoning tasks: visual commonsense
reasoning, visual-textual entailment, and visual question answering. The key
challenge of accurate rationalization is comprehensive image understanding at
all levels: not just their explicit content at the pixel level, but their
contextual contents at the semantic and pragmatic levels. We present
Rationale^VT Transformer, an integrated model that learns to generate free-text
rationales by combining pretrained language models with object recognition,
grounded visual semantic frames, and visual commonsense graphs. Our experiments
show that the base pretrained language model benefits from visual adaptation
and that free-text rationalization is a promising research direction to
complement model interpretability for complex visual-textual reasoning tasks.
| 2,020 | Computation and Language |
Improving Constituency Parsing with Span Attention | Constituency parsing is a fundamental and important task for natural language
understanding, where a good representation of contextual information can help
this task. N-grams, which is a conventional type of feature for contextual
information, have been demonstrated to be useful in many tasks, and thus could
also be beneficial for constituency parsing if they are appropriately modeled.
In this paper, we propose span attention for neural chart-based constituency
parsing to leverage n-gram information. Considering that current chart-based
parsers with Transformer-based encoder represent spans by subtraction of the
hidden states at the span boundaries, which may cause information loss
especially for long spans, we incorporate n-grams into span representations by
weighting them according to their contributions to the parsing process.
Moreover, we propose categorical span attention to further enhance the model by
weighting n-grams within different length categories, and thus benefit
long-sentence parsing. Experimental results on three widely used benchmark
datasets demonstrate the effectiveness of our approach in parsing Arabic,
Chinese, and English, where state-of-the-art performance is obtained by our
approach on all of them.
| 2,020 | Computation and Language |
Token Sequence Labeling vs. Clause Classification for English Emotion
Stimulus Detection | Emotion stimulus detection is the task of finding the cause of an emotion in
a textual description, similar to target or aspect detection for sentiment
analysis. Previous work approached this in three ways, namely (1) as text
classification into an inventory of predefined possible stimuli ("Is the
stimulus category A or B?"), (2) as sequence labeling of tokens ("Which tokens
describe the stimulus?"), and (3) as clause classification ("Does this clause
contain the emotion stimulus?"). So far, setting (3) has been evaluated broadly
on Mandarin and (2) on English, but no comparison has been performed.
Therefore, we aim to answer whether clause classification or sequence labeling
is better suited for emotion stimulus detection in English. To accomplish that,
we propose an integrated framework which enables us to evaluate the two
different approaches comparably, implement models inspired by state-of-the-art
approaches in Mandarin, and test them on four English data sets from different
domains. Our results show that sequence labeling is superior on three out of
four datasets, in both clause-based and sequence-based evaluation. The only
case in which clause classification performs better is one data set with a high
density of clause annotations. Our error analysis further confirms
quantitatively and qualitatively that clauses are not the appropriate stimulus
unit in English.
| 2,020 | Computation and Language |
Grammatical Error Correction in Low Error Density Domains: A New
Benchmark and Analyses | Evaluation of grammatical error correction (GEC) systems has primarily
focused on essays written by non-native learners of English, which however is
only part of the full spectrum of GEC applications. We aim to broaden the
target domain of GEC and release CWEB, a new benchmark for GEC consisting of
website text generated by English speakers of varying levels of proficiency.
Website data is a common and important domain that contains far fewer
grammatical errors than learner essays, which we show presents a challenge to
state-of-the-art GEC systems. We demonstrate that a factor behind this is the
inability of systems to rely on a strong internal language model in low error
density domains. We hope this work shall facilitate the development of
open-domain GEC models that generalize to different topics and genres.
| 2,020 | Computation and Language |
Pretrained Language Models for Dialogue Generation with Multiple Input
Sources | Large-scale pretrained language models have achieved outstanding performance
on natural language understanding tasks. However, it is still under
investigating how to apply them to dialogue generation tasks, especially those
with responses conditioned on multiple sources. Previous work simply
concatenates all input sources or averages information from different input
sources. In this work, we study dialogue models with multiple input sources
adapted from the pretrained language model GPT2. We explore various methods to
fuse multiple separate attention information corresponding to different
sources. Our experimental results show that proper fusion methods deliver
higher relevance with dialogue history than simple fusion baselines.
| 2,020 | Computation and Language |
Learning Better Representation for Tables by Self-Supervised Tasks | Table-to-text generation aims at automatically generating natural text to
help people to conveniently obtain the important information in tables.
Although neural models for table-to-text have achieved remarkable progress,
some problems still overlooked. The first is that the values recorded in many
tables are mostly numbers in practice. The existing approaches do not do
special treatment for these, and still regard these as words in natural
language text. Secondly, the target texts in training dataset may contain
redundant information or facts do not exist in the input tables. These may give
wrong supervision signals to some methods based on content selection and
planning and auxiliary supervision. To solve these problems, we propose two
self-supervised tasks, Number Ordering and Significance Ordering, to help to
learn better table representation. The former works on the column dimension to
help to incorporate the size property of numbers into table representation. The
latter acts on row dimension and help to learn a significance-aware table
representation. We test our methods on the widely used dataset ROTOWIRE which
consists of NBA game statistic and related news. The experimental results
demonstrate that the model trained together with these two self-supervised
tasks can generate text that contains more salient and well-organized facts,
even without modeling context selection and planning. And we achieve the
state-of-the-art performance on automatic metrics.
| 2,021 | Computation and Language |
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in
the Conversation | Emotion Recognition in Conversations (ERC) is essential for building
empathetic human-machine systems. Existing studies on ERC primarily focus on
summarizing the context information in a conversation, however, ignoring the
differentiated emotional behaviors within and across different modalities.
Designing appropriate strategies that fit the differentiated multi-modal
emotional behaviors can produce more accurate emotional predictions. Thus, we
propose the DialogueTransformer to explore the differentiated emotional
behaviors from the intra- and inter-modal perspectives. For intra-modal, we
construct a novel Hierarchical Transformer that can easily switch between
sequential and feed-forward structures according to the differentiated context
preference within each modality. For inter-modal, we constitute a novel
Multi-Grained Interactive Fusion that applies both neuron- and vector-grained
feature interactions to learn the differentiated contributions across all
modalities. Experimental results show that DialogueTRM outperforms the
state-of-the-art by a significant margin on three benchmark datasets.
| 2,020 | Computation and Language |
Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses | Popular Neural Machine Translation model training uses strategies like
backtranslation to improve BLEU scores, requiring large amounts of additional
data and training. We introduce a class of conditional
generative-discriminative hybrid losses that we use to fine-tune a trained
machine translation model. Through a combination of targeted fine-tuning
objectives and intuitive re-use of the training data the model has failed to
adequately learn from, we improve the model performance of both a
sentence-level and a contextual model without using any additional data. We
target the improvement of pronoun translations through our fine-tuning and
evaluate our models on a pronoun benchmark testset. Our sentence-level model
shows a 0.5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets,
while our contextual model achieves the best results, improving from 31.81 to
32 BLEU on WMT14 De-En testset, and from 32.10 to 33.13 on the IWSLT13 De-En
testset, with corresponding improvements in pronoun translation. We further
show the generalizability of our method by reproducing the improvements on two
additional language pairs, Fr-En and Cs-En. Code available at
<https://github.com/ntunlp/pronoun-finetuning>.
| 2,020 | Computation and Language |
Diverse Keyphrase Generation with Neural Unlikelihood Training | In this paper, we study sequence-to-sequence (S2S) keyphrase generation
models from the perspective of diversity. Recent advances in neural natural
language generation have made possible remarkable progress on the task of
keyphrase generation, demonstrated through improvements on quality metrics such
as F1-score. However, the importance of diversity in keyphrase generation has
been largely ignored. We first analyze the extent of information redundancy
present in the outputs generated by a baseline model trained using maximum
likelihood estimation (MLE). Our findings show that repetition of keyphrases is
a major issue with MLE training. To alleviate this issue, we adopt neural
unlikelihood (UL) objective for training the S2S model. Our version of UL
training operates at (1) the target token level to discourage the generation of
repeating tokens; (2) the copy token level to avoid copying repetitive tokens
from the source text. Further, to encourage better model planning during the
decoding process, we incorporate K-step ahead token prediction objective that
computes both MLE and UL losses on future tokens as well. Through extensive
experiments on datasets from three different domains we demonstrate that the
proposed approach attains considerably large diversity gains, while maintaining
competitive output quality.
| 2,020 | Computation and Language |
Inducing Alignment Structure with Gated Graph Attention Networks for
Sentence Matching | Sentence matching is a fundamental task of natural language processing with
various applications. Most recent approaches adopt attention-based neural
models to build word- or phrase-level alignment between two sentences. However,
these models usually ignore the inherent structure within the sentences and
fail to consider various dependency relationships among text units. To address
these issues, this paper proposes a graph-based approach for sentence matching.
First, we represent a sentence pair as a graph with several carefully design
strategies. We then employ a novel gated graph attention network to encode the
constructed graph for sentence matching. Experimental results demonstrate that
our method substantially achieves state-of-the-art performance on two datasets
across tasks of natural language and paraphrase identification. Further
discussions show that our model can learn meaningful graph structure,
indicating its superiority on improved interpretability.
| 2,021 | Computation and Language |
Reliable Evaluations for Natural Language Inference based on a Unified
Cross-dataset Benchmark | Recent studies show that crowd-sourced Natural Language Inference (NLI)
datasets may suffer from significant biases like annotation artifacts. Models
utilizing these superficial clues gain mirage advantages on the in-domain
testing set, which makes the evaluation results over-estimated. The lack of
trustworthy evaluation settings and benchmarks stalls the progress of NLI
research. In this paper, we propose to assess a model's trustworthy
generalization performance with cross-datasets evaluation. We present a new
unified cross-datasets benchmark with 14 NLI datasets, and re-evaluate 9
widely-used neural network-based NLI models as well as 5 recently proposed
debiasing methods for annotation artifacts. Our proposed evaluation scheme and
experimental baselines could provide a basis to inspire future reliable NLI
research.
| 2,020 | Computation and Language |
Does Chinese BERT Encode Word Structure? | Contextualized representations give significantly improved results for a wide
range of NLP tasks. Much work has been dedicated to analyzing the features
captured by representative models such as BERT. Existing work finds that
syntactic, semantic and word sense knowledge are encoded in BERT. However,
little work has investigated word features for character-based languages such
as Chinese. We investigate Chinese BERT using both attention weight
distribution statistics and probing tasks, finding that (1) word information is
captured by BERT; (2) word-level features are mostly in the middle
representation layers; (3) downstream tasks make different use of word features
in BERT, with POS tagging and chunking relying the most on word features, and
natural language inference relying the least on such features.
| 2,020 | Computation and Language |
Wasserstein Distance Regularized Sequence Representation for Text
Matching in Asymmetrical Domains | One approach to matching texts from asymmetrical domains is projecting the
input sequences into a common semantic space as feature vectors upon which the
matching function can be readily defined and learned. In real-world matching
practices, it is often observed that with the training goes on, the feature
vectors projected from different domains tend to be indistinguishable. The
phenomenon, however, is often overlooked in existing matching models. As a
result, the feature vectors are constructed without any regularization, which
inevitably increases the difficulty of learning the downstream matching
functions. In this paper, we propose a novel match method tailored for text
matching in asymmetrical domains, called WD-Match. In WD-Match, a Wasserstein
distance-based regularizer is defined to regularize the features vectors
projected from different domains. As a result, the method enforces the feature
projection function to generate vectors such that those correspond to different
domains cannot be easily discriminated. The training process of WD-Match
amounts to a game that minimizes the matching loss regularized by the
Wasserstein distance. WD-Match can be used to improve different text matching
methods, by using the method as its underlying matching model. Four popular
text matching methods have been exploited in the paper. Experimental results
based on four publicly available benchmarks showed that WD-Match consistently
outperformed the underlying methods and the baselines.
| 2,020 | Computation and Language |
Unsupervised Bitext Mining and Translation via Self-trained Contextual
Embeddings | We describe an unsupervised method to create pseudo-parallel corpora for
machine translation (MT) from unaligned text. We use multilingual BERT to
create source and target sentence embeddings for nearest-neighbor search and
adapt the model via self-training. We validate our technique by extracting
parallel sentence pairs on the BUCC 2017 bitext mining task and observe up to a
24.5 point increase (absolute) in F1 scores over previous unsupervised methods.
We then improve an XLM-based unsupervised neural MT system pre-trained on
Wikipedia by supplementing it with pseudo-parallel text mined from the same
corpus, boosting unsupervised translation performance by up to 3.5 BLEU on the
WMT'14 French-English and WMT'16 German-English tasks and outperforming the
previous state-of-the-art. Finally, we enrich the IWSLT'15 English-Vietnamese
corpus with pseudo-parallel Wikipedia sentence pairs, yielding a 1.2 BLEU
improvement on the low-resource MT task. We demonstrate that unsupervised
bitext mining is an effective way of augmenting MT datasets and complements
existing techniques like initializing with pre-trained contextual embeddings.
| 2,020 | Computation and Language |
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