Titles
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Positional Artefacts Propagate Through Masked Language Model Embeddings
|
In this work, we demonstrate that the contextualized word vectors derived
from pretrained masked language model-based encoders share a common, perhaps
undesirable pattern across layers. Namely, we find cases of persistent outlier
neurons within BERT and RoBERTa's hidden state vectors that consistently bear
the smallest or largest values in said vectors. In an attempt to investigate
the source of this information, we introduce a neuron-level analysis method,
which reveals that the outliers are closely related to information captured by
positional embeddings. We also pre-train the RoBERTa-base models from scratch
and find that the outliers disappear without using positional embeddings. These
outliers, we find, are the major cause of anisotropy of encoders' raw vector
spaces, and clipping them leads to increased similarity across vectors. We
demonstrate this in practice by showing that clipped vectors can more
accurately distinguish word senses, as well as lead to better sentence
embeddings when mean pooling. In three supervised tasks, we find that clipping
does not affect the performance.
| 2,021 |
Computation and Language
|
Bangla Text Classification using Transformers
|
Text classification has been one of the earliest problems in NLP. Over time
the scope of application areas has broadened and the difficulty of dealing with
new areas (e.g., noisy social media content) has increased. The problem-solving
strategy switched from classical machine learning to deep learning algorithms.
One of the recent deep neural network architecture is the Transformer. Models
designed with this type of network and its variants recently showed their
success in many downstream natural language processing tasks, especially for
resource-rich languages, e.g., English. However, these models have not been
explored fully for Bangla text classification tasks. In this work, we fine-tune
multilingual transformer models for Bangla text classification tasks in
different domains, including sentiment analysis, emotion detection, news
categorization, and authorship attribution. We obtain the state of the art
results on six benchmark datasets, improving upon the previous results by 5-29%
accuracy across different tasks.
| 2,020 |
Computation and Language
|
Hierarchical Multitask Learning Approach for BERT
|
Recent works show that learning contextualized embeddings for words is
beneficial for downstream tasks. BERT is one successful example of this
approach. It learns embeddings by solving two tasks, which are masked language
model (masked LM) and the next sentence prediction (NSP). The pre-training of
BERT can also be framed as a multitask learning problem. In this work, we adopt
hierarchical multitask learning approaches for BERT pre-training. Pre-training
tasks are solved at different layers instead of the last layer, and information
from the NSP task is transferred to the masked LM task. Also, we propose a new
pre-training task bigram shift to encode word order information. We choose two
downstream tasks, one of which requires sentence-level embeddings (textual
entailment), and the other requires contextualized embeddings of words
(question answering). Due to computational restrictions, we use the downstream
task data instead of a large dataset for the pre-training to see the
performance of proposed models when given a restricted dataset. We test their
performance on several probing tasks to analyze learned embeddings. Our results
show that imposing a task hierarchy in pre-training improves the performance of
embeddings.
| 2,020 |
Computation and Language
|
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading
Comprehension
|
Machine reading comprehension (MRC) is the task that asks a machine to answer
questions based on a given context. For Chinese MRC, due to the non-literal and
non-compositional semantic characteristics, Chinese idioms pose unique
challenges for machines to understand. Previous studies tend to treat idioms
separately without fully exploiting the relationship among them. In this paper,
we first define the concept of literal meaning coverage to measure the
consistency between semantics and literal meanings for Chinese idioms. With the
definition, we prove that the literal meanings of many idioms are far from
their semantics, and we also verify that the synonymic relationship can
mitigate this inconsistency, which would be beneficial for idiom comprehension.
Furthermore, to fully utilize the synonymic relationship, we propose the
synonym knowledge enhanced reader. Specifically, for each idiom, we first
construct a synonym graph according to the annotations from a high-quality
synonym dictionary or the cosine similarity between the pre-trained idiom
embeddings and then incorporate the graph attention network and gate mechanism
to encode the graph. Experimental results on ChID, a large-scale Chinese idiom
reading comprehension dataset, show that our model achieves state-of-the-art
performance.
| 2,020 |
Computation and Language
|
VisBERT: Hidden-State Visualizations for Transformers
|
Explainability and interpretability are two important concepts, the absence
of which can and should impede the application of well-performing neural
networks to real-world problems. At the same time, they are difficult to
incorporate into the large, black-box models that achieve state-of-the-art
results in a multitude of NLP tasks. Bidirectional Encoder Representations from
Transformers (BERT) is one such black-box model. It has become a staple
architecture to solve many different NLP tasks and has inspired a number of
related Transformer models. Understanding how these models draw conclusions is
crucial for both their improvement and application. We contribute to this
challenge by presenting VisBERT, a tool for visualizing the contextual token
representations within BERT for the task of (multi-hop) Question Answering.
Instead of analyzing attention weights, we focus on the hidden states resulting
from each encoder block within the BERT model. This way we can observe how the
semantic representations are transformed throughout the layers of the model.
VisBERT enables users to get insights about the model's internal state and to
explore its inference steps or potential shortcomings. The tool allows us to
identify distinct phases in BERT's transformations that are similar to a
traditional NLP pipeline and offer insights during failed predictions.
| 2,020 |
Computation and Language
|
Auxiliary Sequence Labeling Tasks for Disfluency Detection
|
Detecting disfluencies in spontaneous speech is an important preprocessing
step in natural language processing and speech recognition applications.
Existing works for disfluency detection have focused on designing a single
objective only for disfluency detection, while auxiliary objectives utilizing
linguistic information of a word such as named entity or part-of-speech
information can be effective. In this paper, we focus on detecting disfluencies
on spoken transcripts and propose a method utilizing named entity recognition
(NER) and part-of-speech (POS) as auxiliary sequence labeling (SL) tasks for
disfluency detection. First, we investigate cases that utilizing linguistic
information of a word can prevent mispredicting important words and can be
helpful for the correct detection of disfluencies. Second, we show that
training a disfluency detection model with auxiliary SL tasks can improve its
F-score in disfluency detection. Then, we analyze which auxiliary SL tasks are
influential depending on baseline models. Experimental results on the widely
used English Switchboard dataset show that our method outperforms the previous
state-of-the-art in disfluency detection.
| 2,021 |
Computation and Language
|
Automated Discovery of Mathematical Definitions in Text with Deep Neural
Networks
|
Automatic definition extraction from texts is an important task that has
numerous applications in several natural language processing fields such as
summarization, analysis of scientific texts, automatic taxonomy generation,
ontology generation, concept identification, and question answering. For
definitions that are contained within a single sentence, this problem can be
viewed as a binary classification of sentences into definitions and
non-definitions. In this paper, we focus on automatic detection of one-sentence
definitions in mathematical texts, which are difficult to separate from
surrounding text. We experiment with several data representations, which
include sentence syntactic structure and word embeddings, and apply deep
learning methods such as the Convolutional Neural Network (CNN) and the Long
Short-Term Memory network (LSTM), in order to identify mathematical
definitions. Our experiments demonstrate the superiority of CNN and its
combination with LSTM, when applied on the syntactically-enriched input
representation. We also present a new dataset for definition extraction from
mathematical texts. We demonstrate that this dataset is beneficial for training
supervised models aimed at extraction of mathematical definitions. Our
experiments with different domains demonstrate that mathematical definitions
require special treatment, and that using cross-domain learning is inefficient
for that task.
| 2,020 |
Computation and Language
|
Refer, Reuse, Reduce: Generating Subsequent References in Visual and
Conversational Contexts
|
Dialogue participants often refer to entities or situations repeatedly within
a conversation, which contributes to its cohesiveness. Subsequent references
exploit the common ground accumulated by the interlocutors and hence have
several interesting properties, namely, they tend to be shorter and reuse
expressions that were effective in previous mentions. In this paper, we tackle
the generation of first and subsequent references in visually grounded
dialogue. We propose a generation model that produces referring utterances
grounded in both the visual and the conversational context. To assess the
referring effectiveness of its output, we also implement a reference resolution
system. Our experiments and analyses show that the model produces better, more
effective referring utterances than a model not grounded in the dialogue
context, and generates subsequent references that exhibit linguistic patterns
akin to humans.
| 2,020 |
Computation and Language
|
Generating Image Descriptions via Sequential Cross-Modal Alignment
Guided by Human Gaze
|
When speakers describe an image, they tend to look at objects before
mentioning them. In this paper, we investigate such sequential cross-modal
alignment by modelling the image description generation process
computationally. We take as our starting point a state-of-the-art image
captioning system and develop several model variants that exploit information
from human gaze patterns recorded during language production. In particular, we
propose the first approach to image description generation where visual
processing is modelled $\textit{sequentially}$. Our experiments and analyses
confirm that better descriptions can be obtained by exploiting gaze-driven
attention and shed light on human cognitive processes by comparing different
ways of aligning the gaze modality with language production. We find that
processing gaze data sequentially leads to descriptions that are better aligned
to those produced by speakers, more diverse, and more natural${-}$particularly
when gaze is encoded with a dedicated recurrent component.
| 2,020 |
Computation and Language
|
Action State Update Approach to Dialogue Management
|
Utterance interpretation is one of the main functions of a dialogue manager,
which is the key component of a dialogue system. We propose the action state
update approach (ASU) for utterance interpretation, featuring a statistically
trained binary classifier used to detect dialogue state update actions in the
text of a user utterance. Our goal is to interpret referring expressions in
user input without a domain-specific natural language understanding component.
For training the model, we use active learning to automatically select
simulated training examples. With both user-simulated and interactive human
evaluations, we show that the ASU approach successfully interprets user
utterances in a dialogue system, including those with referring expressions.
| 2,020 |
Computation and Language
|
Scaling Hidden Markov Language Models
|
The hidden Markov model (HMM) is a fundamental tool for sequence modeling
that cleanly separates the hidden state from the emission structure. However,
this separation makes it difficult to fit HMMs to large datasets in modern NLP,
and they have fallen out of use due to very poor performance compared to fully
observed models. This work revisits the challenge of scaling HMMs to language
modeling datasets, taking ideas from recent approaches to neural modeling. We
propose methods for scaling HMMs to massive state spaces while maintaining
efficient exact inference, a compact parameterization, and effective
regularization. Experiments show that this approach leads to models that are
more accurate than previous HMM and n-gram-based methods, making progress
towards the performance of state-of-the-art neural models.
| 2,020 |
Computation and Language
|
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling
|
Semantic role labeling (SRL) identifies predicate-argument structure(s) in a
given sentence. Although different languages have different argument
annotations, polyglot training, the idea of training one model on multiple
languages, has previously been shown to outperform monolingual baselines,
especially for low resource languages. In fact, even a simple combination of
data has been shown to be effective with polyglot training by representing the
distant vocabularies in a shared representation space. Meanwhile, despite the
dissimilarity in argument annotations between languages, certain argument
labels do share common semantic meaning across languages (e.g. adjuncts have
more or less similar semantic meaning across languages). To leverage such
similarity in annotation space across languages, we propose a method called
Cross-Lingual Argument Regularizer (CLAR). CLAR identifies such linguistic
annotation similarity across languages and exploits this information to map the
target language arguments using a transformation of the space on which source
language arguments lie. By doing so, our experimental results show that CLAR
consistently improves SRL performance on multiple languages over monolingual
and polyglot baselines for low resource languages.
| 2,020 |
Computation and Language
|
Adversarial Semantic Collisions
|
We study semantic collisions: texts that are semantically unrelated but
judged as similar by NLP models. We develop gradient-based approaches for
generating semantic collisions and demonstrate that state-of-the-art models for
many tasks which rely on analyzing the meaning and similarity of texts--
including paraphrase identification, document retrieval, response suggestion,
and extractive summarization-- are vulnerable to semantic collisions. For
example, given a target query, inserting a crafted collision into an irrelevant
document can shift its retrieval rank from 1000 to top 3. We show how to
generate semantic collisions that evade perplexity-based filtering and discuss
other potential mitigations. Our code is available at
https://github.com/csong27/collision-bert.
| 2,020 |
Computation and Language
|
An Analysis of Dataset Overlap on Winograd-Style Tasks
|
The Winograd Schema Challenge (WSC) and variants inspired by it have become
important benchmarks for common-sense reasoning (CSR). Model performance on the
WSC has quickly progressed from chance-level to near-human using neural
language models trained on massive corpora. In this paper, we analyze the
effects of varying degrees of overlap between these training corpora and the
test instances in WSC-style tasks. We find that a large number of test
instances overlap considerably with the corpora on which state-of-the-art
models are (pre)trained, and that a significant drop in classification accuracy
occurs when we evaluate models on instances with minimal overlap. Based on
these results, we develop the KnowRef-60K dataset, which consists of over 60k
pronoun disambiguation problems scraped from web data. KnowRef-60K is the
largest corpus to date for WSC-style common-sense reasoning and exhibits a
significantly lower proportion of overlaps with current pretraining corpora.
| 2,020 |
Computation and Language
|
EstBERT: A Pretrained Language-Specific BERT for Estonian
|
This paper presents EstBERT, a large pretrained transformer-based
language-specific BERT model for Estonian. Recent work has evaluated
multilingual BERT models on Estonian tasks and found them to outperform the
baselines. Still, based on existing studies on other languages, a
language-specific BERT model is expected to improve over the multilingual ones.
We first describe the EstBERT pretraining process and then present the results
of the models based on finetuned EstBERT for multiple NLP tasks, including POS
and morphological tagging, named entity recognition and text classification.
The evaluation results show that the models based on EstBERT outperform
multilingual BERT models on five tasks out of six, providing further evidence
towards a view that training language-specific BERT models are still useful,
even when multilingual models are available.
| 2,021 |
Computation and Language
|
Language Through a Prism: A Spectral Approach for Multiscale Language
Representations
|
Language exhibits structure at different scales, ranging from subwords to
words, sentences, paragraphs, and documents. To what extent do deep models
capture information at these scales, and can we force them to better capture
structure across this hierarchy? We approach this question by focusing on
individual neurons, analyzing the behavior of their activations at different
timescales. We show that signal processing provides a natural framework for
separating structure across scales, enabling us to 1) disentangle
scale-specific information in existing embeddings and 2) train models to learn
more about particular scales. Concretely, we apply spectral filters to the
activations of a neuron across an input, producing filtered embeddings that
perform well on part of speech tagging (word-level), dialog speech acts
classification (utterance-level), or topic classification (document-level),
while performing poorly on the other tasks. We also present a prism layer for
training models, which uses spectral filters to constrain different neurons to
model structure at different scales. Our proposed BERT + Prism model can better
predict masked tokens using long-range context and produces multiscale
representations that perform better at utterance- and document-level tasks. Our
methods are general and readily applicable to other domains besides language,
such as images, audio, and video.
| 2,020 |
Computation and Language
|
Multi-document Summarization via Deep Learning Techniques: A Survey
|
Multi-document summarization (MDS) is an effective tool for information
aggregation that generates an informative and concise summary from a cluster of
topic-related documents. Our survey, the first of its kind, systematically
overviews the recent deep learning based MDS models. We propose a novel
taxonomy to summarize the design strategies of neural networks and conduct a
comprehensive summary of the state-of-the-art. We highlight the differences
between various objective functions that are rarely discussed in the existing
literature. Finally, we propose several future directions pertaining to this
new and exciting field.
| 2,021 |
Computation and Language
|
Simultaneous Speech-to-Speech Translation System with Neural Incremental
ASR, MT, and TTS
|
This paper presents a newly developed, simultaneous neural speech-to-speech
translation system and its evaluation. The system consists of three
fully-incremental neural processing modules for automatic speech recognition
(ASR), machine translation (MT), and text-to-speech synthesis (TTS). We
investigated its overall latency in the system's Ear-Voice Span and speaking
latency along with module-level performance.
| 2,020 |
Computation and Language
|
Natural Language Inference in Context -- Investigating Contextual
Reasoning over Long Texts
|
Natural language inference (NLI) is a fundamental NLP task, investigating the
entailment relationship between two texts. Popular NLI datasets present the
task at sentence-level. While adequate for testing semantic representations,
they fall short for testing contextual reasoning over long texts, which is a
natural part of the human inference process. We introduce ConTRoL, a new
dataset for ConTextual Reasoning over Long texts. Consisting of 8,325
expert-designed "context-hypothesis" pairs with gold labels, ConTRoL is a
passage-level NLI dataset with a focus on complex contextual reasoning types
such as logical reasoning. It is derived from competitive selection and
recruitment test (verbal reasoning test) for police recruitment, with expert
level quality. Compared with previous NLI benchmarks, the materials in ConTRoL
are much more challenging, involving a range of reasoning types. Empirical
results show that state-of-the-art language models perform by far worse than
educated humans. Our dataset can also serve as a testing-set for downstream
tasks like Checking Factual Correctness of Summaries.
| 2,020 |
Computation and Language
|
A Transfer Learning Approach for Dialogue Act Classification of GitHub
Issue Comments
|
Social coding platforms, such as GitHub, serve as laboratories for studying
collaborative problem solving in open source software development; a key
feature is their ability to support issue reporting which is used by teams to
discuss tasks and ideas. Analyzing the dialogue between team members, as
expressed in issue comments, can yield important insights about the performance
of virtual teams. This paper presents a transfer learning approach for
performing dialogue act classification on issue comments. Since no large
labeled corpus of GitHub issue comments exists, employing transfer learning
enables us to leverage standard dialogue act datasets in combination with our
own GitHub comment dataset. We compare the performance of several word and
sentence level encoding models including Global Vectors for Word
Representations (GloVe), Universal Sentence Encoder (USE), and Bidirectional
Encoder Representations from Transformers (BERT). Being able to map the issue
comments to dialogue acts is a useful stepping stone towards understanding
cognitive team processes.
| 2,020 |
Computation and Language
|
Determining Question-Answer Plausibility in Crowdsourced Datasets Using
Multi-Task Learning
|
Datasets extracted from social networks and online forums are often prone to
the pitfalls of natural language, namely the presence of unstructured and noisy
data. In this work, we seek to enable the collection of high-quality
question-answer datasets from social media by proposing a novel task for
automated quality analysis and data cleaning: question-answer (QA)
plausibility. Given a machine or user-generated question and a crowd-sourced
response from a social media user, we determine if the question and response
are valid; if so, we identify the answer within the free-form response. We
design BERT-based models to perform the QA plausibility task, and we evaluate
the ability of our models to generate a clean, usable question-answer dataset.
Our highest-performing approach consists of a single-task model which
determines the plausibility of the question, followed by a multi-task model
which evaluates the plausibility of the response as well as extracts answers
(Question Plausibility AUROC=0.75, Response Plausibility AUROC=0.78, Answer
Extraction F1=0.665).
| 2,020 |
Computation and Language
|
On the Usefulness of Self-Attention for Automatic Speech Recognition
with Transformers
|
Self-attention models such as Transformers, which can capture temporal
relationships without being limited by the distance between events, have given
competitive speech recognition results. However, we note the range of the
learned context increases from the lower to upper self-attention layers, whilst
acoustic events often happen within short time spans in a left-to-right order.
This leads to a question: for speech recognition, is a global view of the
entire sequence useful for the upper self-attention encoder layers in
Transformers? To investigate this, we train models with lower
self-attention/upper feed-forward layers encoders on Wall Street Journal and
Switchboard. Compared to baseline Transformers, no performance drop but minor
gains are observed. We further developed a novel metric of the diagonality of
attention matrices and found the learned diagonality indeed increases from the
lower to upper encoder self-attention layers. We conclude the global view is
unnecessary in training upper encoder layers.
| 2,020 |
Computation and Language
|
When Do You Need Billions of Words of Pretraining Data?
|
NLP is currently dominated by general-purpose pretrained language models like
RoBERTa, which achieve strong performance on NLU tasks through pretraining on
billions of words. But what exact knowledge or skills do Transformer LMs learn
from large-scale pretraining that they cannot learn from less data? We adopt
four probing methods---classifier probing, information-theoretic probing,
unsupervised relative acceptability judgment, and fine-tuning on NLU
tasks---and draw learning curves that track the growth of these different
measures of linguistic ability with respect to pretraining data volume using
the MiniBERTas, a group of RoBERTa models pretrained on 1M, 10M, 100M and 1B
words. We find that LMs require only about 10M or 100M words to learn
representations that reliably encode most syntactic and semantic features we
test. A much larger quantity of data is needed in order to acquire enough
commonsense knowledge and other skills required to master typical downstream
NLU tasks. The results suggest that, while the ability to encode linguistic
features is almost certainly necessary for language understanding, it is likely
that other forms of knowledge are the major drivers of recent improvements in
language understanding among large pretrained models.
| 2,020 |
Computation and Language
|
To What Degree Can Language Borders Be Blurred In BERT-based
Multilingual Spoken Language Understanding?
|
This paper addresses the question as to what degree a BERT-based multilingual
Spoken Language Understanding (SLU) model can transfer knowledge across
languages. Through experiments we will show that, although it works
substantially well even on distant language groups, there is still a gap to the
ideal multilingual performance. In addition, we propose a novel BERT-based
adversarial model architecture to learn language-shared and language-specific
representations for multilingual SLU. Our experimental results prove that the
proposed model is capable of narrowing the gap to the ideal multilingual
performance.
| 2,020 |
Computation and Language
|
Translating Similar Languages: Role of Mutual Intelligibility in
Multilingual Transformers
|
We investigate different approaches to translate between similar languages
under low resource conditions, as part of our contribution to the WMT 2020
Similar Languages Translation Shared Task. We submitted Transformer-based
bilingual and multilingual systems for all language pairs, in the two
directions. We also leverage back-translation for one of the language pairs,
acquiring an improvement of more than 3 BLEU points. We interpret our results
in light of the degree of mutual intelligibility (based on Jaccard similarity)
between each pair, finding a positive correlation between mutual
intelligibility and model performance. Our Spanish-Catalan model has the best
performance of all the five language pairs. Except for the case of
Hindi-Marathi, our bilingual models achieve better performance than the
multilingual models on all pairs.
| 2,020 |
Computation and Language
|
Does Social Support Expressed in Post Titles Elicit Comments in Online
Substance Use Recovery Forums?
|
Individuals recovering from substance use often seek social support
(emotional and informational) on online recovery forums, where they can both
write and comment on posts, expressing their struggles and successes. A common
challenge in these forums is that certain posts (some of which may be support
seeking) receive no comments. In this work, we use data from two Reddit
substance recovery forums:/r/Leaves and/r/OpiatesRecovery, to determine the
relationship between the social supports expressed in the titles of posts and
the number of comments they receive. We show that the types of social support
expressed in post titles that elicit comments vary from one substance use
recovery forum to the other.
| 2,020 |
Computation and Language
|
Multi-Task Sequence Prediction For Tunisian Arabizi Multi-Level
Annotation
|
In this paper we propose a multi-task sequence prediction system, based on
recurrent neural networks and used to annotate on multiple levels an Arabizi
Tunisian corpus. The annotation performed are text classification,
tokenization, PoS tagging and encoding of Tunisian Arabizi into CODA* Arabic
orthography. The system is learned to predict all the annotation levels in
cascade, starting from Arabizi input. We evaluate the system on the TIGER
German corpus, suitably converting data to have a multi-task problem, in order
to show the effectiveness of our neural architecture. We show also how we used
the system in order to annotate a Tunisian Arabizi corpus, which has been
afterwards manually corrected and used to further evaluate sequence models on
Tunisian data. Our system is developed for the Fairseq framework, which allows
for a fast and easy use for any other sequence prediction problem.
| 2,021 |
Computation and Language
|
UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability
Prediction with Multi-task Learning on Self-Supervised Annotations
|
This work describes a self-supervised data augmentation approach used to
improve learning models' performances when only a moderate amount of labeled
data is available. Multiple copies of the original model are initially trained
on the downstream task. Their predictions are then used to annotate a large set
of unlabeled examples. Finally, multi-task training is performed on the
parallel annotations of the resulting training set, and final scores are
obtained by averaging annotator-specific head predictions. Neural language
models are fine-tuned using this procedure in the context of the AcCompl-it
shared task at EVALITA 2020, obtaining considerable improvements in prediction
quality.
| 2,020 |
Computation and Language
|
On the State of Social Media Data for Mental Health Research
|
Data-driven methods for mental health treatment and surveillance have become
a major focus in computational science research in the last decade. However,
progress in the domain, in terms of both medical understanding and system
performance, remains bounded by the availability of adequate data. Prior
systematic reviews have not necessarily made it possible to measure the degree
to which data-related challenges have affected research progress. In this
paper, we offer an analysis specifically on the state of social media data that
exists for conducting mental health research. We do so by introducing an
open-source directory of mental health datasets, annotated using a standardized
schema to facilitate meta-analysis.
| 2,021 |
Computation and Language
|
Towards Preemptive Detection of Depression and Anxiety in Twitter
|
Depression and anxiety are psychiatric disorders that are observed in many
areas of everyday life. For example, these disorders manifest themselves
somewhat frequently in texts written by nondiagnosed users in social media.
However, detecting users with these conditions is not a straightforward task as
they may not explicitly talk about their mental state, and if they do,
contextual cues such as immediacy must be taken into account. When available,
linguistic flags pointing to probable anxiety or depression could be used by
medical experts to write better guidelines and treatments. In this paper, we
develop a dataset designed to foster research in depression and anxiety
detection in Twitter, framing the detection task as a binary tweet
classification problem. We then apply state-of-the-art classification models to
this dataset, providing a competitive set of baselines alongside qualitative
error analysis. Our results show that language models perform reasonably well,
and better than more traditional baselines. Nonetheless, there is clear room
for improvement, particularly with unbalanced training sets and in cases where
seemingly obvious linguistic cues (keywords) are used counter-intuitively.
| 2,020 |
Computation and Language
|
Medical Knowledge-enriched Textual Entailment Framework
|
One of the cardinal tasks in achieving robust medical question answering
systems is textual entailment. The existing approaches make use of an ensemble
of pre-trained language models or data augmentation, often to clock higher
numbers on the validation metrics. However, two major shortcomings impede
higher success in identifying entailment: (1) understanding the focus/intent of
the question and (2) ability to utilize the real-world background knowledge to
capture the context beyond the sentence. In this paper, we present a novel
Medical Knowledge-Enriched Textual Entailment framework that allows the model
to acquire a semantic and global representation of the input medical text with
the help of a relevant domain-specific knowledge graph. We evaluate our
framework on the benchmark MEDIQA-RQE dataset and manifest that the use of
knowledge enriched dual-encoding mechanism help in achieving an absolute
improvement of 8.27% over SOTA language models. We have made the source code
available here.
| 2,020 |
Computation and Language
|
Towards Interpretable Natural Language Understanding with Explanations
as Latent Variables
|
Recently generating natural language explanations has shown very promising
results in not only offering interpretable explanations but also providing
additional information and supervision for prediction. However, existing
approaches usually require a large set of human annotated explanations for
training while collecting a large set of explanations is not only time
consuming but also expensive. In this paper, we develop a general framework for
interpretable natural language understanding that requires only a small set of
human annotated explanations for training. Our framework treats natural
language explanations as latent variables that model the underlying reasoning
process of a neural model. We develop a variational EM framework for
optimization where an explanation generation module and an
explanation-augmented prediction module are alternatively optimized and
mutually enhance each other. Moreover, we further propose an explanation-based
self-training method under this framework for semi-supervised learning. It
alternates between assigning pseudo-labels to unlabeled data and generating new
explanations to iteratively improve each other. Experiments on two natural
language understanding tasks demonstrate that our framework can not only make
effective predictions in both supervised and semi-supervised settings, but also
generate good natural language explanation.
| 2,022 |
Computation and Language
|
Neural Machine Translation for Extremely Low-Resource African Languages:
A Case Study on Bambara
|
Low-resource languages present unique challenges to (neural) machine
translation. We discuss the case of Bambara, a Mande language for which
training data is scarce and requires significant amounts of pre-processing.
More than the linguistic situation of Bambara itself, the socio-cultural
context within which Bambara speakers live poses challenges for automated
processing of this language. In this paper, we present the first parallel data
set for machine translation of Bambara into and from English and French and the
first benchmark results on machine translation to and from Bambara. We discuss
challenges in working with low-resource languages and propose strategies to
cope with data scarcity in low-resource machine translation (MT).
| 2,020 |
Computation and Language
|
DoLFIn: Distributions over Latent Features for Interpretability
|
Interpreting the inner workings of neural models is a key step in ensuring
the robustness and trustworthiness of the models, but work on neural network
interpretability typically faces a trade-off: either the models are too
constrained to be very useful, or the solutions found by the models are too
complex to interpret. We propose a novel strategy for achieving
interpretability that -- in our experiments -- avoids this trade-off. Our
approach builds on the success of using probability as the central quantity,
such as for instance within the attention mechanism. In our architecture,
DoLFIn (Distributions over Latent Features for Interpretability), we do no
determine beforehand what each feature represents, and features go altogether
into an unordered set. Each feature has an associated probability ranging from
0 to 1, weighing its importance for further processing. We show that, unlike
attention and saliency map approaches, this set-up makes it straight-forward to
compute the probability with which an input component supports the decision the
neural model makes. To demonstrate the usefulness of the approach, we apply
DoLFIn to text classification, and show that DoLFIn not only provides
interpretable solutions, but even slightly outperforms the classical CNN and
BiLSTM text classifiers on the SST2 and AG-news datasets.
| 2,020 |
Computation and Language
|
OCR Post Correction for Endangered Language Texts
|
There is little to no data available to build natural language processing
models for most endangered languages. However, textual data in these languages
often exists in formats that are not machine-readable, such as paper books and
scanned images. In this work, we address the task of extracting text from these
resources. We create a benchmark dataset of transcriptions for scanned books in
three critically endangered languages and present a systematic analysis of how
general-purpose OCR tools are not robust to the data-scarce setting of
endangered languages. We develop an OCR post-correction method tailored to ease
training in this data-scarce setting, reducing the recognition error rate by
34% on average across the three languages.
| 2,020 |
Computation and Language
|
E.T.: Entity-Transformers. Coreference augmented Neural Language Model
for richer mention representations via Entity-Transformer blocks
|
In the last decade, the field of Neural Language Modelling has witnessed
enormous changes, with the development of novel models through the use of
Transformer architectures. However, even these models struggle to model long
sequences due to memory constraints and increasing computational complexity.
Coreference annotations over the training data can provide context far beyond
the modelling limitations of such language models. In this paper we present an
extension over the Transformer-block architecture used in neural language
models, specifically in GPT2, in order to incorporate entity annotations during
training. Our model, GPT2E, extends the Transformer layers architecture of GPT2
to Entity-Transformers, an architecture designed to handle coreference
information when present. To that end, we achieve richer representations for
entity mentions, with insignificant training cost. We show the comparative
model performance between GPT2 and GPT2E in terms of Perplexity on the CoNLL
2012 and LAMBADA datasets as well as the key differences in the entity
representations and their effects in downstream tasks such as Named Entity
Recognition. Furthermore, our approach can be adopted by the majority of
Transformer-based language models.
| 2,020 |
Computation and Language
|
Don't Read Too Much into It: Adaptive Computation for Open-Domain
Question Answering
|
Most approaches to Open-Domain Question Answering consist of a light-weight
retriever that selects a set of candidate passages, and a computationally
expensive reader that examines the passages to identify the correct answer.
Previous works have shown that as the number of retrieved passages increases,
so does the performance of the reader. However, they assume all retrieved
passages are of equal importance and allocate the same amount of computation to
them, leading to a substantial increase in computational cost. To reduce this
cost, we propose the use of adaptive computation to control the computational
budget allocated for the passages to be read. We first introduce a technique
operating on individual passages in isolation which relies on anytime
prediction and a per-layer estimation of an early exit probability. We then
introduce SkylineBuilder, an approach for dynamically deciding on which passage
to allocate computation at each step, based on a resource allocation policy
trained via reinforcement learning. Our results on SQuAD-Open show that
adaptive computation with global prioritisation improves over several strong
static and adaptive methods, leading to a 4.3x reduction in computation while
retaining 95% performance of the full model.
| 2,020 |
Computation and Language
|
Multilingual AMR-to-Text Generation
|
Generating text from structured data is challenging because it requires
bridging the gap between (i) structure and natural language (NL) and (ii)
semantically underspecified input and fully specified NL output. Multilingual
generation brings in an additional challenge: that of generating into languages
with varied word order and morphological properties. In this work, we focus on
Abstract Meaning Representations (AMRs) as structured input, where previous
research has overwhelmingly focused on generating only into English. We
leverage advances in cross-lingual embeddings, pretraining, and multilingual
models to create multilingual AMR-to-text models that generate in twenty one
different languages. For eighteen languages, based on automatic metrics, our
multilingual models surpass baselines that generate into a single language. We
analyse the ability of our multilingual models to accurately capture morphology
and word order using human evaluation, and find that native speakers judge our
generations to be fluent.
| 2,020 |
Computation and Language
|
Generating Fact Checking Briefs
|
Fact checking at scale is difficult -- while the number of active fact
checking websites is growing, it remains too small for the needs of the
contemporary media ecosystem. However, despite good intentions, contributions
from volunteers are often error-prone, and thus in practice restricted to claim
detection. We investigate how to increase the accuracy and efficiency of fact
checking by providing information about the claim before performing the check,
in the form of natural language briefs. We investigate passage-based briefs,
containing a relevant passage from Wikipedia, entity-centric ones consisting of
Wikipedia pages of mentioned entities, and Question-Answering Briefs, with
questions decomposing the claim, and their answers. To produce QABriefs, we
develop QABriefer, a model that generates a set of questions conditioned on the
claim, searches the web for evidence, and generates answers. To train its
components, we introduce QABriefDataset which we collected via crowdsourcing.
We show that fact checking with briefs -- in particular QABriefs -- increases
the accuracy of crowdworkers by 10% while slightly decreasing the time taken.
For volunteer (unpaid) fact checkers, QABriefs slightly increase accuracy and
reduce the time required by around 20%.
| 2,020 |
Computation and Language
|
From Unsupervised Machine Translation To Adversarial Text Generation
|
We present a self-attention based bilingual adversarial text generator
(B-GAN) which can learn to generate text from the encoder representation of an
unsupervised neural machine translation system. B-GAN is able to generate a
distributed latent space representation which can be paired with an attention
based decoder to generate fluent sentences. When trained on an encoder shared
between two languages and paired with the appropriate decoder, it can generate
sentences in either language. B-GAN is trained using a combination of
reconstruction loss for auto-encoder, a cross domain loss for translation and a
GAN based adversarial loss for text generation. We demonstrate that B-GAN,
trained on monolingual corpora only using multiple losses, generates more
fluent sentences compared to monolingual baselines while effectively using half
the number of parameters.
| 2,020 |
Computation and Language
|
Resource Constrained Dialog Policy Learning via Differentiable Inductive
Logic Programming
|
Motivated by the needs of resource constrained dialog policy learning, we
introduce dialog policy via differentiable inductive logic (DILOG). We explore
the tasks of one-shot learning and zero-shot domain transfer with DILOG on
SimDial and MultiWoZ. Using a single representative dialog from the restaurant
domain, we train DILOG on the SimDial dataset and obtain 99+% in-domain test
accuracy. We also show that the trained DILOG zero-shot transfers to all other
domains with 99+% accuracy, proving the suitability of DILOG to slot-filling
dialogs. We further extend our study to the MultiWoZ dataset achieving 90+%
inform and success metrics. We also observe that these metrics are not
capturing some of the shortcomings of DILOG in terms of false positives,
prompting us to measure an auxiliary Action F1 score. We show that DILOG is
100x more data efficient than state-of-the-art neural approaches on MultiWoZ
while achieving similar performance metrics. We conclude with a discussion on
the strengths and weaknesses of DILOG.
| 2,020 |
Computation and Language
|
Deep Sound Change: Deep and Iterative Learning, Convolutional Neural
Networks, and Language Change
|
This paper proposes a framework for modeling sound change that combines deep
learning and iterative learning. Acquisition and transmission of speech is
modeled by training generations of Generative Adversarial Networks (GANs) on
unannotated raw speech data. The paper argues that several properties of sound
change emerge from the proposed architecture. GANs (Goodfellow et al. 2014
arXiv:1406.2661, Donahue et al. 2019 arXiv:1705.07904) are uniquely appropriate
for modeling language change because the networks are trained on raw
unsupervised acoustic data, contain no language-specific features and, as
argued in Begu\v{s} (2020 arXiv:2006.03965), encode phonetic and phonological
representations in their latent space and generate linguistically informative
innovative data. The first generation of networks is trained on the relevant
sequences in human speech from TIMIT. The subsequent generations are not
trained on TIMIT, but on generated outputs from the previous generation and
thus start learning from each other in an iterative learning task. The initial
allophonic distribution is progressively being lost with each generation,
likely due to pressures from the global distribution of aspiration in the
training data. The networks show signs of a gradual shift in phonetic targets
characteristic of a gradual phonetic sound change. At endpoints, the outputs
superficially resemble a phonological change -- rule loss.
| 2,021 |
Computation and Language
|
Morphological Disambiguation from Stemming Data
|
Morphological analysis and disambiguation is an important task and a crucial
preprocessing step in natural language processing of morphologically rich
languages. Kinyarwanda, a morphologically rich language, currently lacks tools
for automated morphological analysis. While linguistically curated finite state
tools can be easily developed for morphological analysis, the morphological
richness of the language allows many ambiguous analyses to be produced,
requiring effective disambiguation. In this paper, we propose learning to
morphologically disambiguate Kinyarwanda verbal forms from a new stemming
dataset collected through crowd-sourcing. Using feature engineering and a
feed-forward neural network based classifier, we achieve about 89%
non-contextualized disambiguation accuracy. Our experiments reveal that
inflectional properties of stems and morpheme association rules are the most
discriminative features for disambiguation.
| 2,022 |
Computation and Language
|
E-commerce Query-based Generation based on User Review
|
With the increasing number of merchandise on e-commerce platforms, users tend
to refer to reviews of other shoppers to decide which product they should buy.
However, with so many reviews of a product, users often have to spend lots of
time browsing through reviews talking about product attributes they do not care
about. We want to establish a system that can automatically summarize and
answer user's product specific questions.
In this study, we propose a novel seq2seq based text generation model to
generate answers to user's question based on reviews posted by previous users.
Given a user question and/or target sentiment polarity, we extract aspects of
interest and generate an answer that summarizes previous relevant user reviews.
Specifically, our model performs attention between input reviews and target
aspects during encoding and is conditioned on both review rating and input
context during decoding. We also incorporate a pre-trained auxiliary rating
classifier to improve model performance and accelerate convergence during
training. Experiments using real-world e-commerce dataset show that our model
achieves improvement in performance compared to previously introduced models.
| 2,020 |
Computation and Language
|
NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for
Identify Informative COVID-19 English Tweets
|
This paper presents the model submitted by the NIT_COVID-19 team for
identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared
task addresses the problem of automatically identifying whether an English
tweet related to informative (novel coronavirus) or not. These informative
tweets provide information about recovered, confirmed, suspected, and death
cases as well as the location or travel history of the cases. The proposed
approach includes pre-processing techniques and pre-trained RoBERTa with
suitable hyperparameters for English coronavirus tweet classification. The
performance achieved by the proposed model for shared task WNUT 2020 Task2 is
89.14% in the F1-score metric.
| 2,021 |
Computation and Language
|
An Investigation of Potential Function Designs for Neural CRF
|
The neural linear-chain CRF model is one of the most widely-used approach to
sequence labeling. In this paper, we investigate a series of increasingly
expressive potential functions for neural CRF models, which not only integrate
the emission and transition functions, but also explicitly take the
representations of the contextual words as input. Our extensive experiments
show that the decomposed quadrilinear potential function based on the vector
representations of two neighboring labels and two neighboring words
consistently achieves the best performance.
| 2,021 |
Computation and Language
|
Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion
Texts
|
A document which elaborates opinions and arguments related to the previous
court cases is known as a legal opinion text. Lawyers and legal officials have
to spend considerable effort and time to obtain the required information
manually from those documents when dealing with new legal cases. Hence, it
provides much convenience to those individuals if there is a way to automate
the process of extracting information from legal opinion texts. Party-based
sentiment analysis will play a key role in the automation system by identifying
opinion values with respect to each legal parties in legal texts.
| 2,021 |
Computation and Language
|
Personal-ITY: A Novel YouTube-based Corpus for Personality Prediction in
Italian
|
We present a novel corpus for personality prediction in Italian, containing a
larger number of authors and a different genre compared to previously available
resources. The corpus is built exploiting Distant Supervision, assigning
Myers-Briggs Type Indicator (MBTI) labels to YouTube comments, and can lend
itself to a variety of experiments. We report on preliminary experiments on
Personal-ITY, which can serve as a baseline for future work, showing that some
types are easier to predict than others, and discussing the perks of
cross-dataset prediction.
| 2,020 |
Computation and Language
|
Multilingual Irony Detection with Dependency Syntax and Neural Models
|
This paper presents an in-depth investigation of the effectiveness of
dependency-based syntactic features on the irony detection task in a
multilingual perspective (English, Spanish, French and Italian). It focuses on
the contribution from syntactic knowledge, exploiting linguistic resources
where syntax is annotated according to the Universal Dependencies scheme. Three
distinct experimental settings are provided. In the first, a variety of
syntactic dependency-based features combined with classical machine learning
classifiers are explored. In the second scenario, two well-known types of word
embeddings are trained on parsed data and tested against gold standard
datasets. In the third setting, dependency-based syntactic features are
combined into the Multilingual BERT architecture. The results suggest that
fine-grained dependency-based syntactic information is informative for the
detection of irony.
| 2,020 |
Computation and Language
|
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
|
Lack of training data in low-resource languages presents huge challenges to
sequence labeling tasks such as named entity recognition (NER) and machine
reading comprehension (MRC). One major obstacle is the errors on the boundary
of predicted answers. To tackle this problem, we propose CalibreNet, which
predicts answers in two steps. In the first step, any existing sequence
labeling method can be adopted as a base model to generate an initial answer.
In the second step, CalibreNet refines the boundary of the initial answer. To
tackle the challenge of lack of training data in low-resource languages, we
dedicatedly develop a novel unsupervised phrase boundary recovery pre-training
task to enhance the multilingual boundary detection capability of CalibreNet.
Experiments on two cross-lingual benchmark datasets show that the proposed
approach achieves SOTA results on zero-shot cross-lingual NER and MRC tasks.
| 2,020 |
Computation and Language
|
IGSQL: Database Schema Interaction Graph Based Neural Model for
Context-Dependent Text-to-SQL Generation
|
Context-dependent text-to-SQL task has drawn much attention in recent years.
Previous models on context-dependent text-to-SQL task only concentrate on
utilizing historical user inputs. In this work, in addition to using encoders
to capture historical information of user inputs, we propose a database schema
interaction graph encoder to utilize historicalal information of database
schema items. In decoding phase, we introduce a gate mechanism to weigh the
importance of different vocabularies and then make the prediction of SQL
tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which
are two large complex context-dependent cross-domain text-to-SQL datasets. Our
model outperforms previous state-of-the-art model by a large margin and
achieves new state-of-the-art results on the two datasets. The comparison and
ablation results demonstrate the efficacy of our model and the usefulness of
the database schema interaction graph encoder.
| 2,020 |
Computation and Language
|
Assessment of text coherence based on the cohesion estimation
|
In this paper, a graph-based coherence estimation method based on the
cohesion estimation is suggested. Our method uses a graph-based approach to
provide a user with an understanding of the evaluation process. Moreover, it
can be applied to different languages, therefore, the effectiveness of this
method is examined on the set of English, Chinese, and Arabic texts.
| 2,020 |
Computation and Language
|
On the Sentence Embeddings from Pre-trained Language Models
|
Pre-trained contextual representations like BERT have achieved great success
in natural language processing. However, the sentence embeddings from the
pre-trained language models without fine-tuning have been found to poorly
capture semantic meaning of sentences. In this paper, we argue that the
semantic information in the BERT embeddings is not fully exploited. We first
reveal the theoretical connection between the masked language model
pre-training objective and the semantic similarity task theoretically, and then
analyze the BERT sentence embeddings empirically. We find that BERT always
induces a non-smooth anisotropic semantic space of sentences, which harms its
performance of semantic similarity. To address this issue, we propose to
transform the anisotropic sentence embedding distribution to a smooth and
isotropic Gaussian distribution through normalizing flows that are learned with
an unsupervised objective. Experimental results show that our proposed
BERT-flow method obtains significant performance gains over the
state-of-the-art sentence embeddings on a variety of semantic textual
similarity tasks. The code is available at
https://github.com/bohanli/BERT-flow.
| 2,020 |
Computation and Language
|
Audrey: A Personalized Open-Domain Conversational Bot
|
Conversational Intelligence requires that a person engage on informational,
personal and relational levels. Advances in Natural Language Understanding have
helped recent chatbots succeed at dialog on the informational level. However,
current techniques still lag for conversing with humans on a personal level and
fully relating to them. The University of Michigan's submission to the Alexa
Prize Grand Challenge 3, Audrey, is an open-domain conversational chat-bot that
aims to engage customers on these levels through interest driven conversations
guided by customers' personalities and emotions. Audrey is built from
socially-aware models such as Emotion Detection and a Personal Understanding
Module to grasp a deeper understanding of users' interests and desires. Our
architecture interacts with customers using a hybrid approach balanced between
knowledge-driven response generators and context-driven neural response
generators to cater to all three levels of conversations. During the
semi-finals period, we achieved an average cumulative rating of 3.25 on a 1-5
Likert scale.
| 2,020 |
Computation and Language
|
Situated Data, Situated Systems: A Methodology to Engage with Power
Relations in Natural Language Processing Research
|
We propose a bias-aware methodology to engage with power relations in natural
language processing (NLP) research. NLP research rarely engages with bias in
social contexts, limiting its ability to mitigate bias. While researchers have
recommended actions, technical methods, and documentation practices, no
methodology exists to integrate critical reflections on bias with technical NLP
methods. In this paper, after an extensive and interdisciplinary literature
review, we contribute a bias-aware methodology for NLP research. We also
contribute a definition of biased text, a discussion of the implications of
biased NLP systems, and a case study demonstrating how we are executing the
bias-aware methodology in research on archival metadata descriptions.
| 2,020 |
Computation and Language
|
Overview of CAPITEL Shared Tasks at IberLEF 2020: Named Entity
Recognition and Universal Dependencies Parsing
|
We present the results of the CAPITEL-EVAL shared task, held in the context
of the IberLEF 2020 competition series. CAPITEL-EVAL consisted on two subtasks:
(1) Named Entity Recognition and Classification and (2) Universal Dependency
parsing. For both, the source data was a newly annotated corpus, CAPITEL, a
collection of Spanish articles in the newswire domain. A total of seven teams
participated in CAPITEL-EVAL, with a total of 13 runs submitted across all
subtasks. Data, results and further information about this task can be found at
sites.google.com/view/capitel2020.
| 2,020 |
Computation and Language
|
The Impact of Text Presentation on Translator Performance
|
Widely used computer-aided translation (CAT) tools divide documents into
segments such as sentences and arrange them in a side-by-side, spreadsheet-like
view. We present the first controlled evaluation of these design choices on
translator performance, measuring speed and accuracy in three experimental text
processing tasks. We find significant evidence that sentence-by-sentence
presentation enables faster text reproduction and within-sentence error
identification compared to unsegmented text, and that a top-and-bottom
arrangement of source and target sentences enables faster text reproduction
compared to a side-by-side arrangement. For revision, on the other hand, our
results suggest that presenting unsegmented text results in the highest
accuracy and time efficiency. Our findings have direct implications for best
practices in designing CAT tools.
| 2,020 |
Computation and Language
|
Text Augmentation for Language Models in High Error Recognition Scenario
|
We examine the effect of data augmentation for training of language models
for speech recognition. We compare augmentation based on global error
statistics with one based on per-word unigram statistics of ASR errors and
observe that it is better to only pay attention the global substitution,
deletion and insertion rates. This simple scheme also performs consistently
better than label smoothing and its sampled variants. Additionally, we
investigate into the behavior of perplexity estimated on augmented data, but
conclude that it gives no better prediction of the final error rate. Our best
augmentation scheme increases the absolute WER improvement from second-pass
rescoring from 1.1 % to 1.9 % absolute on the CHiMe-6 challenge.
| 2,020 |
Computation and Language
|
Exploring the Value of Personalized Word Embeddings
|
In this paper, we introduce personalized word embeddings, and examine their
value for language modeling. We compare the performance of our proposed
prediction model when using personalized versus generic word representations,
and study how these representations can be leveraged for improved performance.
We provide insight into what types of words can be more accurately predicted
when building personalized models. Our results show that a subset of words
belonging to specific psycholinguistic categories tend to vary more in their
representations across users and that combining generic and personalized word
embeddings yields the best performance, with a 4.7% relative reduction in
perplexity. Additionally, we show that a language model using personalized word
embeddings can be effectively used for authorship attribution.
| 2,020 |
Computation and Language
|
Author's Sentiment Prediction
|
We introduce PerSenT, a dataset of crowd-sourced annotations of the sentiment
expressed by the authors towards the main entities in news articles. The
dataset also includes paragraph-level sentiment annotations to provide more
fine-grained supervision for the task. Our benchmarks of multiple strong
baselines show that this is a difficult classification task. The results also
suggest that simply fine-tuning document-level representations from BERT isn't
adequate for this task. Making paragraph-level decisions and aggregating them
over the entire document is also ineffective. We present empirical and
qualitative analyses that illustrate the specific challenges posed by this
dataset. We release this dataset with 5.3k documents and 38k paragraphs
covering 3.2k unique entities as a challenge in entity sentiment analysis.
| 2,023 |
Computation and Language
|
Incorporating a Local Translation Mechanism into Non-autoregressive
Translation
|
In this work, we introduce a novel local autoregressive translation (LAT)
mechanism into non-autoregressive translation (NAT) models so as to capture
local dependencies among tar-get outputs. Specifically, for each target
decoding position, instead of only one token, we predict a short sequence of
tokens in an autoregressive way. We further design an efficient merging
algorithm to align and merge the out-put pieces into one final output sequence.
We integrate LAT into the conditional masked language model (CMLM;
Ghazvininejad et al.,2019) and similarly adopt iterative decoding. Empirical
results on five translation tasks show that compared with CMLM, our method
achieves comparable or better performance with fewer decoding iterations,
bringing a 2.5xspeedup. Further analysis indicates that our method reduces
repeated translations and performs better at longer sentences.
| 2,020 |
Computation and Language
|
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled
Multitask Learning Framework
|
Existing studies on using social media for deriving mental health status of
users focus on the depression detection task. However, for case management and
referral to psychiatrists, healthcare workers require practical and scalable
depressive disorder screening and triage system. This study aims to design and
evaluate a decision support system (DSS) to reliably determine the depressive
triage level by capturing fine-grained depressive symptoms expressed in user
tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is
routinely used in clinical practice. The reliable detection of depressive
symptoms from tweets is challenging because the 280-character limit on tweets
incentivizes the use of creative artifacts in the utterances and figurative
usage contributes to effective expression. We propose a novel BERT based robust
multi-task learning framework to accurately identify the depressive symptoms
using the auxiliary task of figurative usage detection. Specifically, our
proposed novel task sharing mechanism, co-task aware attention, enables
automatic selection of optimal information across the BERT layers and tasks by
soft-sharing of parameters. Our results show that modeling figurative usage can
demonstrably improve the model's robustness and reliability for distinguishing
the depression symptoms.
| 2,020 |
Computation and Language
|
Augmenting BERT Carefully with Underrepresented Linguistic Features
|
Fine-tuned Bidirectional Encoder Representations from Transformers
(BERT)-based sequence classification models have proven to be effective for
detecting Alzheimer's Disease (AD) from transcripts of human speech. However,
previous research shows it is possible to improve BERT's performance on various
tasks by augmenting the model with additional information. In this work, we use
probing tasks as introspection techniques to identify linguistic information
not well-represented in various layers of BERT, but important for the AD
detection task. We supplement these linguistic features in which
representations from BERT are found to be insufficient with hand-crafted
features externally, and show that jointly fine-tuning BERT in combination with
these features improves the performance of AD classification by upto 5\% over
fine-tuned BERT alone.
| 2,020 |
Computation and Language
|
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity
Dependency Discovery
|
Discovering precise and interpretable rules from knowledge graphs is regarded
as an essential challenge, which can improve the performances of many
downstream tasks and even provide new ways to approach some Natural Language
Processing research topics. In this paper, we present a fundamental theory for
rule-based knowledge graph reasoning, based on which the connectivity
dependencies in the graph are captured via multiple rule types. It is the first
time for some of these rule types in a knowledge graph to be considered. Based
on these rule types, our theory can provide precise interpretations to unknown
triples. Then, we implement our theory by what we call the RuleDict model.
Results show that our RuleDict model not only provides precise rules to
interpret new triples, but also achieves state-of-the-art performances on one
benchmark knowledge graph completion task, and is competitive on other tasks.
| 2,022 |
Computation and Language
|
Towards Semi-Supervised Semantics Understanding from Speech
|
Much recent work on Spoken Language Understanding (SLU) falls short in at
least one of three ways: models were trained on oracle text input and neglected
the Automatics Speech Recognition (ASR) outputs, models were trained to predict
only intents without the slot values, or models were trained on a large amount
of in-house data. We proposed a clean and general framework to learn semantics
directly from speech with semi-supervision from transcribed speech to address
these. Our framework is built upon pretrained end-to-end (E2E) ASR and
self-supervised language models, such as BERT, and fine-tuned on a limited
amount of target SLU corpus. In parallel, we identified two inadequate settings
under which SLU models have been tested: noise-robustness and E2E semantics
evaluation. We tested the proposed framework under realistic environmental
noises and with a new metric, the slots edit F1 score, on two public SLU
corpora. Experiments show that our SLU framework with speech as input can
perform on par with those with oracle text as input in semantics understanding,
while environmental noises are present, and a limited amount of labeled
semantics data is available.
| 2,020 |
Computation and Language
|
Enabling Interactive Transcription in an Indigenous Community
|
We propose a novel transcription workflow which combines spoken term
detection and human-in-the-loop, together with a pilot experiment. This work is
grounded in an almost zero-resource scenario where only a few terms have so far
been identified, involving two endangered languages. We show that in the early
stages of transcription, when the available data is insufficient to train a
robust ASR system, it is possible to take advantage of the transcription of a
small number of isolated words in order to bootstrap the transcription of a
speech collection.
| 2,020 |
Computation and Language
|
Cross-lingual and Multilingual Spoken Term Detection for Low-Resource
Indian Languages
|
Spoken Term Detection (STD) is the task of searching for words or phrases
within audio, given either text or spoken input as a query. In this work, we
use state-of-the-art Hindi, Tamil and Telugu ASR systems cross-lingually for
lexical Spoken Term Detection in ten low-resource Indian languages. Since no
publicly available dataset exists for Spoken Term Detection in these languages,
we create a new dataset using a publicly available TTS dataset. We report a
standard metric for STD, Mean Term Weighted Value (MTWV) and show that ASR
systems built in languages that are phonetically similar to the target
languages have higher accuracy, however, it is also possible to get high MTWV
scores for dissimilar languages by using a relaxed phone matching algorithm. We
propose a technique to bootstrap the Grapheme-to-Phoneme (g2p) mapping between
all the languages under consideration using publicly available resources. Gains
are obtained when we combine the output of multiple ASR systems and when we use
language-specific Language Models. We show that it is possible to perform STD
cross-lingually in a zero-shot manner without the need for any
language-specific speech data. We plan to make the STD dataset available for
other researchers interested in cross-lingual STD.
| 2,020 |
Computation and Language
|
Analyzing Neural Discourse Coherence Models
|
In this work, we systematically investigate how well current models of
coherence can capture aspects of text implicated in discourse organisation. We
devise two datasets of various linguistic alterations that undermine coherence
and test model sensitivity to changes in syntax and semantics. We furthermore
probe discourse embedding space and examine the knowledge that is encoded in
representations of coherence. We hope this study shall provide further insight
into how to frame the task and improve models of coherence assessment further.
Finally, we make our datasets publicly available as a resource for researchers
to use to test discourse coherence models.
| 2,020 |
Computation and Language
|
Biomedical Named Entity Recognition at Scale
|
Named entity recognition (NER) is a widely applicable natural language
processing task and building block of question answering, topic modeling,
information retrieval, etc. In the medical domain, NER plays a crucial role by
extracting meaningful chunks from clinical notes and reports, which are then
fed to downstream tasks like assertion status detection, entity resolution,
relation extraction, and de-identification. Reimplementing a Bi-LSTM-CNN-Char
deep learning architecture on top of Apache Spark, we present a single
trainable NER model that obtains new state-of-the-art results on seven public
biomedical benchmarks without using heavy contextual embeddings like BERT. This
includes improving BC4CHEMD to 93.72% (4.1% gain), Species800 to 80.91% (4.6%
gain), and JNLPBA to 81.29% (5.2% gain). In addition, this model is freely
available within a production-grade code base as part of the open-source Spark
NLP library; can scale up for training and inference in any Spark cluster; has
GPU support and libraries for popular programming languages such as Python, R,
Scala and Java; and can be extended to support other human languages with no
code changes.
| 2,020 |
Computation and Language
|
SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal
Opinion Texts
|
Aspect-Based Sentiment Analysis (ABSA) has been prominent and ongoing
research over many different domains, but it is not widely discussed in the
legal domain. A number of publicly available datasets for a wide range of
domains usually fulfill the needs of researchers to perform their studies in
the field of ABSA. To the best of our knowledge, there is no publicly available
dataset for the Aspect (Party) Based Sentiment Analysis for legal opinion
texts. Therefore, creating a publicly available dataset for the research of
ABSA for the legal domain can be considered as a task with significant
importance. In this study, we introduce a manually annotated legal opinion text
dataset (SigmaLaw-ABSA) intended towards facilitating researchers for ABSA
tasks in the legal domain. SigmaLaw-ABSA consists of legal opinion texts in the
English language which have been annotated by human judges. This study
discusses the sub-tasks of ABSA relevant to the legal domain and how to use the
dataset to perform them. This paper also describes the statistics of the
dataset and as a baseline, we present some results on the performance of some
existing deep learning based systems on the SigmaLaw-ABSA dataset.
| 2,021 |
Computation and Language
|
Towards A Sentiment Analyzer for Low-Resource Languages
|
Twitter is one of the top influenced social media which has a million number
of active users. It is commonly used for microblogging that allows users to
share messages, ideas, thoughts and many more. Thus, millions interaction such
as short messages or tweets are flowing around among the twitter users
discussing various topics that has been happening world-wide. This research
aims to analyse a sentiment of the users towards a particular trending topic
that has been actively and massively discussed at that time. We chose a hashtag
\textit{\#kpujangancurang} that was the trending topic during the Indonesia
presidential election in 2019. We use the hashtag to obtain a set of data from
Twitter to analyse and investigate further the positive or the negative
sentiment of the users from their tweets. This research utilizes rapid miner
tool to generate the twitter data and comparing Naive Bayes, K-Nearest
Neighbor, Decision Tree, and Multi-Layer Perceptron classification methods to
classify the sentiment of the twitter data. There are overall 200 labeled data
in this experiment. Overall, Naive Bayes and Multi-Layer Perceptron
classification outperformed the other two methods on 11 experiments with
different size of training-testing data split. The two classifiers are
potential to be used in creating sentiment analyzer for low-resource languages
with small corpus.
| 2,020 |
Computation and Language
|
Turn-level Dialog Evaluation with Dialog-level Weak Signals for
Bot-Human Hybrid Customer Service Systems
|
We developed a machine learning approach that quantifies multiple aspects of
the success or values in Customer Service contacts, at anytime during the
interaction. Specifically, the value/reward function regarding to the
turn-level behaviors across human agents, chatbots and other hybrid dialog
systems is characterized by the incremental information and confidence gain
between sentences, based on the token-level predictions from a multi-task
neural network trained with only weak signals in dialog-level
attributes/states. The resulting model, named Value Profiler, serves as a
goal-oriented dialog manager that enhances conversations by regulating
automated decisions with its reward and state predictions. It supports both
real-time monitoring and scalable offline customer experience evaluation, for
both bot- and human-handled contacts. We show how it improves Amazon customer
service quality in several applications.
| 2,020 |
Computation and Language
|
World Trade Center responders in their own words: Predicting PTSD
symptom trajectories with AI-based language analyses of interviews
|
Background: Oral histories from 9/11 responders to the World Trade Center
(WTC) attacks provide rich narratives about distress and resilience. Artificial
Intelligence (AI) models promise to detect psychopathology in natural language,
but they have been evaluated primarily in non-clinical settings using social
media. This study sought to test the ability of AI-based language assessments
to predict PTSD symptom trajectories among responders. Methods: Participants
were 124 responders whose health was monitored at the Stony Brook WTC Health
and Wellness Program who completed oral history interviews about their initial
WTC experiences. PTSD symptom severity was measured longitudinally using the
PTSD Checklist (PCL) for up to 7 years post-interview. AI-based indicators were
computed for depression, anxiety, neuroticism, and extraversion along with
dictionary-based measures of linguistic and interpersonal style. Linear
regression and multilevel models estimated associations of AI indicators with
concurrent and subsequent PTSD symptom severity (significance adjusted by false
discovery rate). Results: Cross-sectionally, greater depressive language
(beta=0.32; p=0.043) and first-person singular usage (beta=0.31; p=0.044) were
associated with increased symptom severity. Longitudinally, anxious language
predicted future worsening in PCL scores (beta=0.31; p=0.031), whereas
first-person plural usage (beta=-0.37; p=0.007) and longer words usage
(beta=-0.36; p=0.007) predicted improvement. Conclusions: This is the first
study to demonstrate the value of AI in understanding PTSD in a vulnerable
population. Future studies should extend this application to other trauma
exposures and to other demographic groups, especially under-represented
minorities.
| 2,020 |
Computation and Language
|
Exploiting Cross-Dialectal Gold Syntax for Low-Resource Historical
Languages: Towards a Generic Parser for Pre-Modern Slavic
|
This paper explores the possibility of improving the performance of
specialized parsers for pre-modern Slavic by training them on data from
different related varieties. Because of their linguistic heterogeneity,
pre-modern Slavic varieties are treated as low-resource historical languages,
whereby cross-dialectal treebank data may be exploited to overcome data
scarcity and attempt the training of a variety-agnostic parser. Previous
experiments on early Slavic dependency parsing are discussed, particularly with
regard to their ability to tackle different orthographic, regional and
stylistic features. A generic pre-modern Slavic parser and two specialized
parsers -- one for East Slavic and one for South Slavic -- are trained using
jPTDP (Nguyen & Verspoor 2018), a neural network model for joint part-of-speech
(POS) tagging and dependency parsing which had shown promising results on a
number of Universal Dependency (UD) treebanks, including Old Church Slavonic
(OCS). With these experiments, a new state of the art is obtained for both OCS
(83.79\% unlabelled attachment score (UAS) and 78.43\% labelled attachement
score (LAS)) and Old East Slavic (OES) (85.7\% UAS and 80.16\% LAS).
| 2,020 |
Computation and Language
|
Overview of the Ninth Dialog System Technology Challenge: DSTC9
|
This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9).
This edition of the DSTC focuses on applying end-to-end dialog technologies for
four distinct tasks in dialog systems, namely, 1. Task-oriented dialog Modeling
with unstructured knowledge access, 2. Multi-domain task-oriented dialog, 3.
Interactive evaluation of dialog, and 4. Situated interactive multi-modal
dialog. This paper describes the task definition, provided datasets, baselines
and evaluation set-up for each track. We also summarize the results of the
submitted systems to highlight the overall trends of the state-of-the-art
technologies for the tasks.
| 2,020 |
Computation and Language
|
Natural Language Processing to Detect Cognitive Concerns in Electronic
Health Records Using Deep Learning
|
Dementia is under-recognized in the community, under-diagnosed by healthcare
professionals, and under-coded in claims data. Information on cognitive
dysfunction, however, is often found in unstructured clinician notes within
medical records but manual review by experts is time consuming and often prone
to errors. Automated mining of these notes presents a potential opportunity to
label patients with cognitive concerns who could benefit from an evaluation or
be referred to specialist care. In order to identify patients with cognitive
concerns in electronic medical records, we applied natural language processing
(NLP) algorithms and compared model performance to a baseline model that used
structured diagnosis codes and medication data only. An attention-based deep
learning model outperformed the baseline model and other simpler models.
| 2,020 |
Computation and Language
|
An Interpretable End-to-end Fine-tuning Approach for Long Clinical Text
|
Unstructured clinical text in EHRs contains crucial information for
applications including decision support, trial matching, and retrospective
research. Recent work has applied BERT-based models to clinical information
extraction and text classification, given these models' state-of-the-art
performance in other NLP domains. However, BERT is difficult to apply to
clinical notes because it doesn't scale well to long sequences of text. In this
work, we propose a novel fine-tuning approach called SnipBERT. Instead of using
entire notes, SnipBERT identifies crucial snippets and then feeds them into a
truncated BERT-based model in a hierarchical manner. Empirically, SnipBERT not
only has significant predictive performance gain across three tasks but also
provides improved interpretability, as the model can identify key pieces of
text that led to its prediction.
| 2,020 |
Computation and Language
|
Inference-only sub-character decomposition improves translation of
unseen logographic characters
|
Neural Machine Translation (NMT) on logographic source languages struggles
when translating `unseen' characters, which never appear in the training data.
One possible approach to this problem uses sub-character decomposition for
training and test sentences. However, this approach involves complete
retraining, and its effectiveness for unseen character translation to
non-logographic languages has not been fully explored.
We investigate existing ideograph-based sub-character decomposition
approaches for Chinese-to-English and Japanese-to-English NMT, for both
high-resource and low-resource domains. For each language pair and domain we
construct a test set where all source sentences contain at least one unseen
logographic character. We find that complete sub-character decomposition often
harms unseen character translation, and gives inconsistent results generally.
We offer a simple alternative based on decomposition before inference for
unseen characters only. Our approach allows flexible application, achieving
translation adequacy improvements and requiring no additional models or
training.
| 2,020 |
Computation and Language
|
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
|
We introduce doc2dial, a new dataset of goal-oriented dialogues that are
grounded in the associated documents. Inspired by how the authors compose
documents for guiding end users, we first construct dialogue flows based on the
content elements that corresponds to higher-level relations across text
sections as well as lower-level relations between discourse units within a
section. Then we present these dialogue flows to crowd contributors to create
conversational utterances. The dataset includes about 4800 annotated
conversations with an average of 14 turns that are grounded in over 480
documents from four domains. Compared to the prior document-grounded dialogue
datasets, this dataset covers a variety of dialogue scenes in
information-seeking conversations. For evaluating the versatility of the
dataset, we introduce multiple dialogue modeling tasks and present baseline
approaches.
| 2,020 |
Computation and Language
|
Context-aware Stand-alone Neural Spelling Correction
|
Existing natural language processing systems are vulnerable to noisy inputs
resulting from misspellings. On the contrary, humans can easily infer the
corresponding correct words from their misspellings and surrounding context.
Inspired by this, we address the stand-alone spelling correction problem, which
only corrects the spelling of each token without additional token insertion or
deletion, by utilizing both spelling information and global context
representations. We present a simple yet powerful solution that jointly detects
and corrects misspellings as a sequence labeling task by fine-turning a
pre-trained language model. Our solution outperforms the previous
state-of-the-art result by 12.8% absolute F0.5 score.
| 2,020 |
Computation and Language
|
A Survey on Recent Advances in Sequence Labeling from Deep Learning
Models
|
Sequence labeling (SL) is a fundamental research problem encompassing a
variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition
(NER), text chunking, etc. Though prevalent and effective in many downstream
applications (e.g., information retrieval, question answering, and knowledge
graph embedding), conventional sequence labeling approaches heavily rely on
hand-crafted or language-specific features. Recently, deep learning has been
employed for sequence labeling tasks due to its powerful capability in
automatically learning complex features of instances and effectively yielding
the stat-of-the-art performances. In this paper, we aim to present a
comprehensive review of existing deep learning-based sequence labeling models,
which consists of three related tasks, e.g., part-of-speech tagging, named
entity recognition, and text chunking. Then, we systematically present the
existing approaches base on a scientific taxonomy, as well as the widely-used
experimental datasets and popularly-adopted evaluation metrics in the SL
domain. Furthermore, we also present an in-depth analysis of different SL
models on the factors that may affect the performance and future directions in
the SL domain.
| 2,020 |
Computation and Language
|
Unsupervised Explanation Generation for Machine Reading Comprehension
|
With the blooming of various Pre-trained Language Models (PLMs), Machine
Reading Comprehension (MRC) has embraced significant improvements on various
benchmarks and even surpass human performances. However, the existing works
only target on the accuracy of the final predictions and neglect the importance
of the explanations for the prediction, which is a big obstacle when utilizing
these models in real-life applications to convince humans. In this paper, we
propose a self-explainable framework for the machine reading comprehension
task. The main idea is that the proposed system tries to use less passage
information and achieve similar results compared to the system that uses the
whole passage, while the filtered passage will be used as explanations. We
carried out experiments on three multiple-choice MRC datasets, and found that
the proposed system could achieve consistent improvements over baseline
systems. To evaluate the explainability, we compared our approach with the
traditional attention mechanism in human evaluations and found that the
proposed system has a notable advantage over the latter one.
| 2,020 |
Computation and Language
|
Re-framing Incremental Deep Language Models for Dialogue Processing with
Multi-task Learning
|
We present a multi-task learning framework to enable the training of one
universal incremental dialogue processing model with four tasks of disfluency
detection, language modelling, part-of-speech tagging, and utterance
segmentation in a simple deep recurrent setting. We show that these tasks
provide positive inductive biases to each other with the optimal contribution
of each one relying on the severity of the noise from the task. Our live
multi-task model outperforms similar individual tasks, delivers competitive
performance, and is beneficial for future use in conversational agents in
psychiatric treatment.
| 2,020 |
Computation and Language
|
diagNNose: A Library for Neural Activation Analysis
|
In this paper we introduce diagNNose, an open source library for analysing
the activations of deep neural networks. diagNNose contains a wide array of
interpretability techniques that provide fundamental insights into the inner
workings of neural networks. We demonstrate the functionality of diagNNose with
a case study on subject-verb agreement within language models. diagNNose is
available at https://github.com/i-machine-think/diagnnose.
| 2,020 |
Computation and Language
|
Cross-Domain Learning for Classifying Propaganda in Online Contents
|
As news and social media exhibit an increasing amount of manipulative
polarized content, detecting such propaganda has received attention as a new
task for content analysis. Prior work has focused on supervised learning with
training data from the same domain. However, as propaganda can be subtle and
keeps evolving, manual identification and proper labeling are very demanding.
As a consequence, training data is a major bottleneck. In this paper, we tackle
this bottleneck and present an approach to leverage cross-domain learning,
based on labeled documents and sentences from news and tweets, as well as
political speeches with a clear difference in their degrees of being
propagandistic. We devise informative features and build various classifiers
for propaganda labeling, using cross-domain learning. Our experiments
demonstrate the usefulness of this approach, and identify difficulties and
limitations in various configurations of sources and targets for the transfer
step. We further analyze the influence of various features, and characterize
salient indicators of propaganda.
| 2,020 |
Computation and Language
|
Interpretable Multi-dataset Evaluation for Named Entity Recognition
|
With the proliferation of models for natural language processing tasks, it is
even harder to understand the differences between models and their relative
merits. Simply looking at differences between holistic metrics such as
accuracy, BLEU, or F1 does not tell us why or how particular methods perform
differently and how diverse datasets influence the model design choices. In
this paper, we present a general methodology for interpretable evaluation for
the named entity recognition (NER) task. The proposed evaluation method enables
us to interpret the differences in models and datasets, as well as the
interplay between them, identifying the strengths and weaknesses of current
systems. By making our analysis tool available, we make it easy for future
researchers to run similar analyses and drive progress in this area:
https://github.com/neulab/InterpretEval.
| 2,020 |
Computation and Language
|
RethinkCWS: Is Chinese Word Segmentation a Solved Task?
|
The performance of the Chinese Word Segmentation (CWS) systems has gradually
reached a plateau with the rapid development of deep neural networks,
especially the successful use of large pre-trained models. In this paper, we
take stock of what we have achieved and rethink what's left in the CWS task.
Methodologically, we propose a fine-grained evaluation for existing CWS
systems, which not only allows us to diagnose the strengths and weaknesses of
existing models (under the in-dataset setting), but enables us to quantify the
discrepancy between different criterion and alleviate the negative transfer
problem when doing multi-criteria learning. Strategically, despite not aiming
to propose a novel model in this paper, our comprehensive experiments on eight
models and seven datasets, as well as thorough analysis, could search for some
promising direction for future research. We make all codes publicly available
and release an interface that can quickly evaluate and diagnose user's models:
https://github.com/neulab/InterpretEval.
| 2,020 |
Computation and Language
|
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine
Translation with Soft Lexical Constraints
|
We introduce an Edit-Based Transformer with Repositioning (EDITOR), which
makes sequence generation flexible by seamlessly allowing users to specify
preferences in output lexical choice. Building on recent models for
non-autoregressive sequence generation (Gu et al., 2019), EDITOR generates new
sequences by iteratively editing hypotheses. It relies on a novel reposition
operation designed to disentangle lexical choice from word positioning
decisions, while enabling efficient oracles for imitation learning and parallel
edits at decoding time. Empirically, EDITOR uses soft lexical constraints more
effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding
up decoding dramatically compared to constrained beam search (Post and Vilar,
2018). EDITOR also achieves comparable or better translation quality with
faster decoding speed than the Levenshtein Transformer on standard
Romanian-English, English-German, and English-Japanese machine translation
tasks.
| 2,021 |
Computation and Language
|
Medical symptom recognition from patient text: An active learning
approach for long-tailed multilabel distributions
|
We study the problem of medical symptoms recognition from patient text, for
the purposes of gathering pertinent information from the patient (known as
history-taking). A typical patient text is often descriptive of the symptoms
the patient is experiencing and a single instance of such a text can be
"labeled" with multiple symptoms. This makes learning a medical symptoms
recognizer challenging on account of i) the lack of availability of voluminous
annotated data as well as ii) the large unknown universe of multiple symptoms
that a single text can map to. Furthermore, patient text is often characterized
by a long tail in the data (i.e., some labels/symptoms occur more frequently
than others for e.g "fever" vs "hematochezia"). In this paper, we introduce an
active learning method that leverages underlying structure of a continually
refined, learned latent space to select the most informative examples to label.
This enables the selection of the most informative examples that progressively
increases the coverage on the universe of symptoms via the learned model,
despite the long tail in data distribution.
| 2,021 |
Computation and Language
|
Learning language variations in news corpora through differential
embeddings
|
There is an increasing interest in the NLP community in capturing variations
in the usage of language, either through time (i.e., semantic drift), across
regions (as dialects or variants) or in different social contexts (i.e.,
professional or media technolects). Several successful dynamical embeddings
have been proposed that can track semantic change through time. Here we show
that a model with a central word representation and a slice-dependent
contribution can learn word embeddings from different corpora simultaneously.
This model is based on a star-like representation of the slices. We apply it to
The New York Times and The Guardian newspapers, and we show that it can capture
both temporal dynamics in the yearly slices of each corpus, and language
variations between US and UK English in a curated multi-source corpus. We
provide an extensive evaluation of this methodology.
| 2,020 |
Computation and Language
|
Arabic Dialect Identification Using BERT-Based Domain Adaptation
|
Arabic is one of the most important and growing languages in the world. With
the rise of social media platforms such as Twitter, Arabic spoken dialects have
become more in use. In this paper, we describe our approach on the NADI Shared
Task 1 that requires us to build a system to differentiate between different 21
Arabic dialects, we introduce a deep learning semi-supervised fashion approach
along with pre-processing that was reported on NADI shared Task 1 Corpus. Our
system ranks 4th in NADI's shared task competition achieving a 23.09% F1 macro
average score with a simple yet efficient approach to differentiating between
21 Arabic Dialects given tweets.
| 2,020 |
Computation and Language
|
FLERT: Document-Level Features for Named Entity Recognition
|
Current state-of-the-art approaches for named entity recognition (NER)
typically consider text at the sentence-level and thus do not model information
that crosses sentence boundaries. However, the use of transformer-based models
for NER offers natural options for capturing document-level features. In this
paper, we perform a comparative evaluation of document-level features in the
two standard NER architectures commonly considered in the literature, namely
"fine-tuning" and "feature-based LSTM-CRF". We evaluate different
hyperparameters for document-level features such as context window size and
enforcing document-locality. We present experiments from which we derive
recommendations for how to model document context and present new
state-of-the-art scores on several CoNLL-03 benchmark datasets. Our approach is
integrated into the Flair framework to facilitate reproduction of our
experiments.
| 2,021 |
Computation and Language
|
Matching Theory and Data with Personal-ITY: What a Corpus of Italian
YouTube Comments Reveals About Personality
|
As a contribution to personality detection in languages other than English,
we rely on distant supervision to create Personal-ITY, a novel corpus of
YouTube comments in Italian, where authors are labelled with personality
traits. The traits are derived from one of the mainstream personality theories
in psychology research, named MBTI. Using personality prediction experiments,
we (i) study the task of personality prediction in itself on our corpus as well
as on TwiSty, a Twitter dataset also annotated with MBTI labels; (ii) carry out
an extensive, in-depth analysis of the features used by the classifier, and
view them specifically under the light of the original theory that we used to
create the corpus in the first place. We observe that no single model is best
at personality detection, and that while some traits are easier than others to
detect, and also to match back to theory, for other, less frequent traits the
picture is much more blurred.
| 2,020 |
Computation and Language
|
Deconstructing word embedding algorithms
|
Word embeddings are reliable feature representations of words used to obtain
high quality results for various NLP applications. Uncontextualized word
embeddings are used in many NLP tasks today, especially in resource-limited
settings where high memory capacity and GPUs are not available. Given the
historical success of word embeddings in NLP, we propose a retrospective on
some of the most well-known word embedding algorithms. In this work, we
deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of
the common conditions that seem to be required for making performant word
embeddings. We believe that the theoretical findings in this paper can provide
a basis for more informed development of future models.
| 2,020 |
Computation and Language
|
The Teacher-Student Chatroom Corpus
|
The Teacher-Student Chatroom Corpus (TSCC) is a collection of written
conversations captured during one-to-one lessons between teachers and learners
of English. The lessons took place in an online chatroom and therefore involve
more interactive, immediate and informal language than might be found in
asynchronous exchanges such as email correspondence. The fact that the lessons
were one-to-one means that the teacher was able to focus exclusively on the
linguistic abilities and errors of the student, and to offer personalised
exercises, scaffolding and correction. The TSCC contains more than one hundred
lessons between two teachers and eight students, amounting to 13.5K
conversational turns and 133K words: it is freely available for research use.
We describe the corpus design, data collection procedure and annotations added
to the text. We perform some preliminary descriptive analyses of the data and
consider possible uses of the TSCC.
| 2,020 |
Computation and Language
|
Streaming Attention-Based Models with Augmented Memory for End-to-End
Speech Recognition
|
Attention-based models have been gaining popularity recently for their strong
performance demonstrated in fields such as machine translation and automatic
speech recognition. One major challenge of attention-based models is the need
of access to the full sequence and the quadratically growing computational cost
concerning the sequence length. These characteristics pose challenges,
especially for low-latency scenarios, where the system is often required to be
streaming. In this paper, we build a compact and streaming speech recognition
system on top of the end-to-end neural transducer architecture with
attention-based modules augmented with convolution. The proposed system equips
the end-to-end models with the streaming capability and reduces the large
footprint from the streaming attention-based model using augmented memory. On
the LibriSpeech dataset, our proposed system achieves word error rates 2.7% on
test-clean and 5.8% on test-other, to our best knowledge the lowest among
streaming approaches reported so far.
| 2,020 |
Computation and Language
|
Zero-shot Relation Classification from Side Information
|
We propose a zero-shot learning relation classification (ZSLRC) framework
that improves on state-of-the-art by its ability to recognize novel relations
that were not present in training data. The zero-shot learning approach mimics
the way humans learn and recognize new concepts with no prior knowledge. To
achieve this, ZSLRC uses advanced prototypical networks that are modified to
utilize weighted side (auxiliary) information. ZSLRC's side information is
built from keywords, hypernyms of name entities, and labels and their synonyms.
ZSLRC also includes an automatic hypernym extraction framework that acquires
hypernyms of various name entities directly from the web. ZSLRC improves on
state-of-the-art few-shot learning relation classification methods that rely on
labeled training data and is therefore applicable more widely even in
real-world scenarios where some relations have no corresponding labeled
examples for training. We present results using extensive experiments on two
public datasets (NYT and FewRel) and show that ZSLRC significantly outperforms
state-of-the-art methods on supervised learning, few-shot learning, and
zero-shot learning tasks. Our experimental results also demonstrate the
effectiveness and robustness of our proposed model.
| 2,021 |
Computation and Language
|
IIRC: A Dataset of Incomplete Information Reading Comprehension
Questions
|
Humans often have to read multiple documents to address their information
needs. However, most existing reading comprehension (RC) tasks only focus on
questions for which the contexts provide all the information required to answer
them, thus not evaluating a system's performance at identifying a potential
lack of sufficient information and locating sources for that information. To
fill this gap, we present a dataset, IIRC, with more than 13K questions over
paragraphs from English Wikipedia that provide only partial information to
answer them, with the missing information occurring in one or more linked
documents. The questions were written by crowd workers who did not have access
to any of the linked documents, leading to questions that have little lexical
overlap with the contexts where the answers appear. This process also gave many
questions without answers, and those that require discrete reasoning,
increasing the difficulty of the task. We follow recent modeling work on
various reading comprehension datasets to construct a baseline model for this
dataset, finding that it achieves 31.1% F1 on this task, while estimated human
performance is 88.4%. The dataset, code for the baseline system, and a
leaderboard can be found at https://allennlp.org/iirc.
| 2,020 |
Computation and Language
|
Language Models not just for Pre-training: Fast Online Neural Noisy
Channel Modeling
|
Pre-training models on vast quantities of unlabeled data has emerged as an
effective approach to improving accuracy on many NLP tasks. On the other hand,
traditional machine translation has a long history of leveraging unlabeled data
through noisy channel modeling. The same idea has recently been shown to
achieve strong improvements for neural machine translation. Unfortunately,
na\"{i}ve noisy channel modeling with modern sequence to sequence models is up
to an order of magnitude slower than alternatives. We address this issue by
introducing efficient approximations to make inference with the noisy channel
approach as fast as strong ensembles while increasing accuracy. We also show
that the noisy channel approach can outperform strong pre-training results by
achieving a new state of the art on WMT Romanian-English translation.
| 2,020 |
Computation and Language
|
Utilizing Bidirectional Encoder Representations from Transformers for
Answer Selection
|
Pre-training a transformer-based model for the language modeling task in a
large dataset and then fine-tuning it for downstream tasks has been found very
useful in recent years. One major advantage of such pre-trained language models
is that they can effectively absorb the context of each word in a sentence.
However, for tasks such as the answer selection task, the pre-trained language
models have not been extensively used yet. To investigate their effectiveness
in such tasks, in this paper, we adopt the pre-trained Bidirectional Encoder
Representations from Transformer (BERT) language model and fine-tune it on two
Question Answering (QA) datasets and three Community Question Answering (CQA)
datasets for the answer selection task. We find that fine-tuning the BERT model
for the answer selection task is very effective and observe a maximum
improvement of 13.1% in the QA datasets and 18.7% in the CQA datasets compared
to the previous state-of-the-art.
| 2,020 |
Computation and Language
|
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