Titles
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On Biasing Transformer Attention Towards Monotonicity
|
Many sequence-to-sequence tasks in natural language processing are roughly
monotonic in the alignment between source and target sequence, and previous
work has facilitated or enforced learning of monotonic attention behavior via
specialized attention functions or pretraining. In this work, we introduce a
monotonicity loss function that is compatible with standard attention
mechanisms and test it on several sequence-to-sequence tasks:
grapheme-to-phoneme conversion, morphological inflection, transliteration, and
dialect normalization. Experiments show that we can achieve largely monotonic
behavior. Performance is mixed, with larger gains on top of RNN baselines.
General monotonicity does not benefit transformer multihead attention, however,
we see isolated improvements when only a subset of heads is biased towards
monotonic behavior.
| 2,021 |
Computation and Language
|
GrASP: A Library for Extracting and Exploring Human-Interpretable
Textual Patterns
|
Data exploration is an important step of every data science and machine
learning project, including those involving textual data. We provide a novel
language tool, in the form of a publicly available Python library for
extracting patterns from textual data. The library integrates a first public
implementation of the existing GrASP algorithm. It allows users to extract
patterns using a number of general-purpose built-in linguistic attributes (such
as hypernyms, part-of-speech tags, and syntactic dependency tags), as envisaged
for the original algorithm, as well as domain-specific custom attributes which
can be incorporated into the library by implementing two functions. The library
is equipped with a web-based interface empowering human users to conveniently
explore data via the extracted patterns, using complementary pattern-centric
and example-centric views: the former includes a reading in natural language
and statistics of each extracted pattern; the latter shows applications of each
extracted pattern to training examples. We demonstrate the usefulness of the
library in classification (spam detection and argument mining), model analysis
(machine translation), and artifact discovery in datasets (SNLI and
20Newsgroups).
| 2,022 |
Computation and Language
|
Detecting of a Patient's Condition From Clinical Narratives Using
Natural Language Representation
|
The rapid progress in clinical data management systems and artificial
intelligence approaches enable the era of personalized medicine. Intensive care
units (ICUs) are the ideal clinical research environment for such development
because they collect many clinical data and are highly computerized
environments. We designed a retrospective clinical study on a prospective ICU
database using clinical natural language to help in the early diagnosis of
heart failure in critically ill children. The methodology consisted of
empirical experiments of a learning algorithm to learn the hidden
interpretation and presentation of the French clinical note data. This study
included 1386 patients' clinical notes with 5444 single lines of notes. There
were 1941 positive cases (36 % of total) and 3503 negative cases classified by
two independent physicians using a standardized approach. The multilayer
perceptron neural network outperforms other discriminative and generative
classifiers. Consequently, the proposed framework yields an overall
classification performance with 89 % accuracy, 88 % recall, and 89 % precision.
This study successfully applied learning representation and machine learning
algorithms to detect heart failure from clinical natural language in a single
French institution. Further work is needed to use the same methodology in other
institutions and other languages.
| 2,022 |
Computation and Language
|
Plug-and-Blend: A Framework for Controllable Story Generation with
Blended Control Codes
|
Large pre-trained neural language models (LM) have very powerful text
generation capabilities. However, in practice, they are hard to control for
creative purposes. We describe a Plug-and-Play controllable language generation
framework, Plug-and-Blend, that allows a human user to input multiple control
codes (topics). In the context of automated story generation, this allows a
human user loose or fine-grained control of the topics and transitions between
them that will appear in the generated story, and can even allow for
overlapping, blended topics. Automated evaluations show our framework, working
with different generative LMs, controls the generation towards given
continuous-weighted control codes while keeping the generated sentences fluent,
demonstrating strong blending capability. A human participant evaluation shows
that the generated stories are observably transitioning between two topics.
| 2,021 |
Computation and Language
|
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your
Hebrew NLP Application With
|
Large Pre-trained Language Models (PLMs) have become ubiquitous in the
development of language understanding technology and lie at the heart of many
artificial intelligence advances. While advances reported for English using
PLMs are unprecedented, reported advances using PLMs in Hebrew are few and far
between. The problem is twofold. First, Hebrew resources available for training
NLP models are not at the same order of magnitude as their English
counterparts. Second, there are no accepted tasks and benchmarks to evaluate
the progress of Hebrew PLMs on. In this work we aim to remedy both aspects.
First, we present AlephBERT, a large pre-trained language model for Modern
Hebrew, which is trained on larger vocabulary and a larger dataset than any
Hebrew PLM before. Second, using AlephBERT we present new state-of-the-art
results on multiple Hebrew tasks and benchmarks, including: Segmentation,
Part-of-Speech Tagging, full Morphological Tagging, Named-Entity Recognition
and Sentiment Analysis. We make our AlephBERT model publicly available,
providing a single point of entry for the development of Hebrew NLP
applications.
| 2,021 |
Computation and Language
|
A Sketch-Based Neural Model for Generating Commit Messages from Diffs
|
Commit messages have an important impact in software development, especially
when working in large teams. Multiple developers who have a different style of
writing may often be involved in the same project. For this reason, it may be
difficult to maintain a strict pattern of writing informative commit messages,
with the most frequent issue being that these messages are not descriptive
enough. In this paper we apply neural machine translation (NMT) techniques to
convert code diffs into commit messages and we present an improved sketch-based
encoder for this task. We split the approach into three parts. Firstly, we
focus on finding a more suitable NMT baseline for this problem. Secondly, we
show that the performance of the NMT models can be improved by training on
examples containing a specific file type. Lastly, we introduce a novel
sketch-based neural model inspired by recent approaches used for code
generation and we show that the sketch-based encoder significantly outperforms
existing state of the art solutions. The results highlight that this
improvement is relevant especially for Java source code files, by examining two
different datasets introduced in recent years for this task.
| 2,021 |
Computation and Language
|
Grapheme-to-Phoneme Transformer Model for Transfer Learning Dialects
|
Grapheme-to-Phoneme (G2P) models convert words to their phonetic
pronunciations. Classic G2P methods include rule-based systems and
pronunciation dictionaries, while modern G2P systems incorporate learning, such
as, LSTM and Transformer-based attention models. Usually, dictionary-based
methods require significant manual effort to build, and have limited adaptivity
on unseen words. And transformer-based models require significant training
data, and do not generalize well, especially for dialects with limited data.
We propose a novel use of transformer-based attention model that can adapt to
unseen dialects of English language, while using a small dictionary. We show
that our method has potential applications for accent transfer for
text-to-speech, and for building robust G2P models for dialects with limited
pronunciation dictionary size.
We experiment with two English dialects: Indian and British. A model trained
from scratch using 1000 words from British English dictionary, with 14211 words
held out, leads to phoneme error rate (PER) of 26.877%, on a test set generated
using the full dictionary. The same model pretrained on CMUDict American
English dictionary, and fine-tuned on the same dataset leads to PER of 2.469%
on the test set.
| 2,021 |
Computation and Language
|
XFORMAL: A Benchmark for Multilingual Formality Style Transfer
|
We take the first step towards multilingual style transfer by creating and
releasing XFORMAL, a benchmark of multiple formal reformulations of informal
text in Brazilian Portuguese, French, and Italian. Results on XFORMAL suggest
that state-of-the-art style transfer approaches perform close to simple
baselines, indicating that style transfer is even more challenging when moving
multilingual.
| 2,021 |
Computation and Language
|
Design and Implementation of English To Yor\`ub\'a Verb Phrase Machine
Translation System
|
We aim to develop an English-to-Yoruba machine translation system which can
translate English verb phrase text to its Yoruba equivalent.Words from both
languages Source Language and Target Language were collected for the verb
phrase group in the home domain. The lexical translation is done by assigning
values of the matching word in the dictionary. The syntax of the two languages
was realized using Context-Free Grammar, we validated the rewrite rules with
finite state automata. The human evaluation method was used and expert fluency
was scored. The evaluation shows the system performed better than that of
sampled Google translation with over 70 percent of the response matching that
of the system's output.
| 2,023 |
Computation and Language
|
An Empirical Comparison of Instance Attribution Methods for NLP
|
Widespread adoption of deep models has motivated a pressing need for
approaches to interpret network outputs and to facilitate model debugging.
Instance attribution methods constitute one means of accomplishing these goals
by retrieving training instances that (may have) led to a particular
prediction. Influence functions (IF; Koh and Liang 2017) provide machinery for
doing this by quantifying the effect that perturbing individual train instances
would have on a specific test prediction. However, even approximating the IF is
computationally expensive, to the degree that may be prohibitive in many cases.
Might simpler approaches (e.g., retrieving train examples most similar to a
given test point) perform comparably? In this work, we evaluate the degree to
which different potential instance attribution agree with respect to the
importance of training samples. We find that simple retrieval methods yield
training instances that differ from those identified via gradient-based methods
(such as IFs), but that nonetheless exhibit desirable characteristics similar
to more complex attribution methods. Code for all methods and experiments in
this paper is available at:
https://github.com/successar/instance_attributions_NLP.
| 2,021 |
Computation and Language
|
The Road to Know-Where: An Object-and-Room Informed Sequential BERT for
Indoor Vision-Language Navigation
|
Vision-and-Language Navigation (VLN) requires an agent to find a path to a
remote location on the basis of natural-language instructions and a set of
photo-realistic panoramas. Most existing methods take the words in the
instructions and the discrete views of each panorama as the minimal unit of
encoding. However, this requires a model to match different nouns (e.g., TV,
table) against the same input view feature. In this work, we propose an
object-informed sequential BERT to encode visual perceptions and linguistic
instructions at the same fine-grained level, namely objects and words. Our
sequential BERT also enables the visual-textual clues to be interpreted in
light of the temporal context, which is crucial to multi-round VLN tasks.
Additionally, we enable the model to identify the relative direction (e.g.,
left/right/front/back) of each navigable location and the room type (e.g.,
bedroom, kitchen) of its current and final navigation goal, as such information
is widely mentioned in instructions implying the desired next and final
locations. We thus enable the model to know-where the objects lie in the
images, and to know-where they stand in the scene. Extensive experiments
demonstrate the effectiveness compared against several state-of-the-art methods
on three indoor VLN tasks: REVERIE, NDH, and R2R. Project repository:
https://github.com/YuankaiQi/ORIST
| 2,021 |
Computation and Language
|
BERT-based Chinese Text Classification for Emergency Domain with a Novel
Loss Function
|
This paper proposes an automatic Chinese text categorization method for
solving the emergency event report classification problem. Since bidirectional
encoder representations from transformers (BERT) has achieved great success in
natural language processing domain, it is employed to derive emergency text
features in this study. To overcome the data imbalance problem in the
distribution of emergency event categories, a novel loss function is proposed
to improve the performance of the BERT-based model. Meanwhile, to avoid the
impact of the extreme learning rate, the Adabound optimization algorithm that
achieves a gradual smooth transition from Adam to SGD is employed to learn
parameters of the model. To verify the feasibility and effectiveness of the
proposed method, a Chinese emergency text dataset collected from the Internet
is employed. Compared with benchmarking methods, the proposed method has
achieved the best performance in terms of accuracy, weighted-precision,
weighted-recall, and weighted-F1 values. Therefore, it is promising to employ
the proposed method for real applications in smart emergency management
systems.
| 2,021 |
Computation and Language
|
Incorporating External Knowledge to Enhance Tabular Reasoning
|
Reasoning about tabular information presents unique challenges to modern NLP
approaches which largely rely on pre-trained contextualized embeddings of text.
In this paper, we study these challenges through the problem of tabular natural
language inference. We propose easy and effective modifications to how
information is presented to a model for this task. We show via systematic
experiments that these strategies substantially improve tabular inference
performance.
| 2,021 |
Computation and Language
|
Annotating and Modeling Fine-grained Factuality in Summarization
|
Recent pre-trained abstractive summarization systems have started to achieve
credible performance, but a major barrier to their use in practice is their
propensity to output summaries that are not faithful to the input and that
contain factual errors. While a number of annotated datasets and statistical
models for assessing factuality have been explored, there is no clear picture
of what errors are most important to target or where current techniques are
succeeding and failing. We explore both synthetic and human-labeled data
sources for training models to identify factual errors in summarization, and
study factuality at the word-, dependency-, and sentence-level. Our
observations are threefold. First, exhibited factual errors differ
significantly across datasets, and commonly-used training sets of simple
synthetic errors do not reflect errors made on abstractive datasets like XSum.
Second, human-labeled data with fine-grained annotations provides a more
effective training signal than sentence-level annotations or synthetic data.
Finally, we show that our best factuality detection model enables training of
more factual XSum summarization models by allowing us to identify non-factual
tokens in the training data.
| 2,021 |
Computation and Language
|
Noisy-Labeled NER with Confidence Estimation
|
Recent studies in deep learning have shown significant progress in named
entity recognition (NER). Most existing works assume clean data annotation, yet
a fundamental challenge in real-world scenarios is the large amount of noise
from a variety of sources (e.g., pseudo, weak, or distant annotations). This
work studies NER under a noisy labeled setting with calibrated confidence
estimation. Based on empirical observations of different training dynamics of
noisy and clean labels, we propose strategies for estimating confidence scores
based on local and global independence assumptions. We partially marginalize
out labels of low confidence with a CRF model. We further propose a calibration
method for confidence scores based on the structure of entity labels. We
integrate our approach into a self-training framework for boosting performance.
Experiments in general noisy settings with four languages and distantly labeled
settings demonstrate the effectiveness of our method. Our code can be found at
https://github.com/liukun95/Noisy-NER-Confidence-Estimation
| 2,021 |
Computation and Language
|
A preliminary study on evaluating Consultation Notes with Post-Editing
|
Automatic summarisation has the potential to aid physicians in streamlining
clerical tasks such as note taking. But it is notoriously difficult to evaluate
these systems and demonstrate that they are safe to be used in a clinical
setting. To circumvent this issue, we propose a semi-automatic approach whereby
physicians post-edit generated notes before submitting them. We conduct a
preliminary study on the time saving of automatically generated consultation
notes with post-editing. Our evaluators are asked to listen to mock
consultations and to post-edit three generated notes. We time this and find
that it is faster than writing the note from scratch. We present insights and
lessons learnt from this experiment.
| 2,021 |
Computation and Language
|
Towards objectively evaluating the quality of generated medical
summaries
|
We propose a method for evaluating the quality of generated text by asking
evaluators to count facts, and computing precision, recall, f-score, and
accuracy from the raw counts. We believe this approach leads to a more
objective and easier to reproduce evaluation. We apply this to the task of
medical report summarisation, where measuring objective quality and accuracy is
of paramount importance.
| 2,021 |
Computation and Language
|
Studying Alignment in a Collaborative Learning Activity via Automatic
Methods: The Link Between What We Say and Do
|
A dialogue is successful when there is alignment between the speakers at
different linguistic levels. In this work, we consider the dialogue occurring
between interlocutors engaged in a collaborative learning task, where they are
not only evaluated on how well they performed, but also on how much they
learnt. The main contribution of this work is to propose new automatic measures
to study alignment; focusing on verbal (lexical) alignment, and behavioral
alignment (when an instruction given by one was followed with concrete actions
by another). A second contribution of our work is to study how spontaneous
speech phenomena are used in the process of alignment. Lastly, we make public
the dataset to study alignment in educational dialogues. Our results show that
all teams verbally and behaviourally align to some degree regardless of their
performance and learning, and our measures capture that teams that did not
succeed in the task were simply slower to collaborate. Thus we find that teams
that performed better, were faster to align. Furthermore, our methodology
captures a productive period that includes the time where the interlocutors
came up with their best solutions. We also find that well-performing teams
verbalise the marker "oh" more when they are behaviourally aligned, compared to
other times in the dialogue; showing that this marker is an important cue in
alignment. To the best of our knowledge, we are the first to study the role of
"oh" as an information management marker in a behavioral context (i.e. in
connection to actions taken in a physical environment), compared to only a
verbal one. Our measures contribute to the research in the field of educational
dialogue and the intersection between dialogue and collaborative learning
research.
| 2,022 |
Computation and Language
|
Larger-Context Tagging: When and Why Does It Work?
|
The development of neural networks and pretraining techniques has spawned
many sentence-level tagging systems that achieved superior performance on
typical benchmarks. However, a relatively less discussed topic is what if more
context information is introduced into current top-scoring tagging systems.
Although several existing works have attempted to shift tagging systems from
sentence-level to document-level, there is still no consensus conclusion about
when and why it works, which limits the applicability of the larger-context
approach in tagging tasks. In this paper, instead of pursuing a
state-of-the-art tagging system by architectural exploration, we focus on
investigating when and why the larger-context training, as a general strategy,
can work.
To this end, we conduct a thorough comparative study on four proposed
aggregators for context information collecting and present an attribute-aided
evaluation method to interpret the improvement brought by larger-context
training. Experimentally, we set up a testbed based on four tagging tasks and
thirteen datasets. Hopefully, our preliminary observations can deepen the
understanding of larger-context training and enlighten more follow-up works on
the use of contextual information.
| 2,021 |
Computation and Language
|
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking
|
Dialogue State Tracking is central to multi-domain task-oriented dialogue
systems, responsible for extracting information from user utterances. We
present a novel hybrid architecture that augments GPT-2 with representations
derived from Graph Attention Networks in such a way to allow causal, sequential
prediction of slot values. The model architecture captures inter-slot
relationships and dependencies across domains that otherwise can be lost in
sequential prediction. We report improvements in state tracking performance in
MultiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified
sparse training scenario in which DST models are trained only on session-level
annotations but evaluated at the turn level. We further report detailed
analyses to demonstrate the effectiveness of graph models in DST by showing
that the proposed graph modules capture inter-slot dependencies and improve the
predictions of values that are common to multiple domains.
| 2,021 |
Computation and Language
|
Did they answer? Subjective acts and intents in conversational discourse
|
Discourse signals are often implicit, leaving it up to the interpreter to
draw the required inferences. At the same time, discourse is embedded in a
social context, meaning that interpreters apply their own assumptions and
beliefs when resolving these inferences, leading to multiple, valid
interpretations. However, current discourse data and frameworks ignore the
social aspect, expecting only a single ground truth. We present the first
discourse dataset with multiple and subjective interpretations of English
conversation in the form of perceived conversation acts and intents. We
carefully analyze our dataset and create computational models to (1) confirm
our hypothesis that taking into account the bias of the interpreters leads to
better predictions of the interpretations, (2) and show disagreements are
nuanced and require a deeper understanding of the different contextual factors.
We share our dataset and code at http://github.com/elisaF/subjective_discourse.
| 2,021 |
Computation and Language
|
Efficient Large-Scale Language Model Training on GPU Clusters Using
Megatron-LM
|
Large language models have led to state-of-the-art accuracies across a range
of tasks. However, training these models efficiently is challenging for two
reasons: a) GPU memory capacity is limited, making it impossible to fit large
models on even a multi-GPU server, and b) the number of compute operations
required to train these models can result in unrealistically long training
times. Consequently, new methods of model parallelism such as tensor and
pipeline parallelism have been proposed. Unfortunately, naive usage of these
methods leads to fundamental scaling issues at thousands of GPUs, e.g., due to
expensive cross-node communication or devices spending significant time waiting
on other devices to make progress.
In this paper, we show how different types of parallelism methods (tensor,
pipeline, and data parallelism) can be composed to scale to thousands of GPUs
and models with trillions of parameters. We survey techniques for pipeline
parallelism and propose a novel interleaved pipeline parallelism schedule that
can improve throughput by 10+% with memory footprint comparable to existing
approaches. We quantitatively study the trade-offs between tensor, pipeline,
and data parallelism, and provide intuition as to how to configure distributed
training of a large model. Our approach allows us to perform training
iterations on a model with 1 trillion parameters at 502 petaFLOP/s on 3072 GPUs
with achieved per-GPU throughput of 52% of theoretical peak. Our code is open
sourced at https://github.com/nvidia/megatron-lm.
| 2,021 |
Computation and Language
|
Language model fusion for streaming end to end speech recognition
|
Streaming processing of speech audio is required for many contemporary
practical speech recognition tasks. Even with the large corpora of manually
transcribed speech data available today, it is impossible for such corpora to
cover adequately the long tail of linguistic content that's important for tasks
such as open-ended dictation and voice search. We seek to address both the
streaming and the tail recognition challenges by using a language model (LM)
trained on unpaired text data to enhance the end-to-end (E2E) model. We extend
shallow fusion and cold fusion approaches to streaming Recurrent Neural Network
Transducer (RNNT), and also propose two new competitive fusion approaches that
further enhance the RNNT architecture. Our results on multiple languages with
varying training set sizes show that these fusion methods improve streaming
RNNT performance through introducing extra linguistic features. Cold fusion
works consistently better on streaming RNNT with up to a 8.5% WER improvement.
| 2,021 |
Computation and Language
|
Explaining Neural Network Predictions on Sentence Pairs via Learning
Word-Group Masks
|
Explaining neural network models is important for increasing their
trustworthiness in real-world applications. Most existing methods generate
post-hoc explanations for neural network models by identifying individual
feature attributions or detecting interactions between adjacent features.
However, for models with text pairs as inputs (e.g., paraphrase
identification), existing methods are not sufficient to capture feature
interactions between two texts and their simple extension of computing all
word-pair interactions between two texts is computationally inefficient. In
this work, we propose the Group Mask (GMASK) method to implicitly detect word
correlations by grouping correlated words from the input text pair together and
measure their contribution to the corresponding NLP tasks as a whole. The
proposed method is evaluated with two different model architectures
(decomposable attention model and BERT) across four datasets, including natural
language inference and paraphrase identification tasks. Experiments show the
effectiveness of GMASK in providing faithful explanations to these models.
| 2,021 |
Computation and Language
|
Chinese Character Decomposition for Neural MT with Multi-Word
Expressions
|
Chinese character decomposition has been used as a feature to enhance Machine
Translation (MT) models, combining radicals into character and word level
models. Recent work has investigated ideograph or stroke level embedding.
However, questions remain about different decomposition levels of Chinese
character representations, radical and strokes, best suited for MT. To
investigate the impact of Chinese decomposition embedding in detail, i.e.,
radical, stroke, and intermediate levels, and how well these decompositions
represent the meaning of the original character sequences, we carry out
analysis with both automated and human evaluation of MT. Furthermore, we
investigate if the combination of decomposed Multiword Expressions (MWEs) can
enhance the model learning. MWE integration into MT has seen more than a decade
of exploration. However, decomposed MWEs has not previously been explored.
| 2,021 |
Computation and Language
|
Connecting Attributions and QA Model Behavior on Realistic
Counterfactuals
|
When a model attribution technique highlights a particular part of the input,
a user might understand this highlight as making a statement about
counterfactuals (Miller, 2019): if that part of the input were to change, the
model's prediction might change as well. This paper investigates how well
different attribution techniques align with this assumption on realistic
counterfactuals in the case of reading comprehension (RC). RC is a particularly
challenging test case, as token-level attributions that have been extensively
studied in other NLP tasks such as sentiment analysis are less suitable to
represent the reasoning that RC models perform. We construct counterfactual
sets for three different RC settings, and through heuristics that can connect
attribution methods' outputs to high-level model behavior, we can evaluate how
useful different attribution methods and even different formats are for
understanding counterfactuals. We find that pairwise attributions are better
suited to RC than token-level attributions across these different RC settings,
with our best performance coming from a modification that we propose to an
existing pairwise attribution method.
| 2,021 |
Computation and Language
|
AdCOFE: Advanced Contextual Feature Extraction in Conversations for
emotion classification
|
Emotion recognition in conversations is an important step in various virtual
chat bots which require opinion-based feedback, like in social media threads,
online support and many more applications. Current Emotion recognition in
conversations models face issues like (a) loss of contextual information in
between two dialogues of a conversation, (b) failure to give appropriate
importance to significant tokens in each utterance and (c) inability to pass on
the emotional information from previous utterances.The proposed model of
Advanced Contextual Feature Extraction (AdCOFE) addresses these issues by
performing unique feature extraction using knowledge graphs, sentiment lexicons
and phrases of natural language at all levels (word and position embedding) of
the utterances. Experiments on the Emotion recognition in conversations dataset
show that AdCOFE is beneficial in capturing emotions in conversations.
| 2,021 |
Computation and Language
|
UPB at SemEval-2021 Task 8: Extracting Semantic Information on
Measurements as Multi-Turn Question Answering
|
Extracting semantic information on measurements and counts is an important
topic in terms of analyzing scientific discourses. The 8th task of
SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in
this direction by providing a new dataset on which participants train their
models to extract meaningful information on measurements from scientific texts.
The competition is composed of five subtasks that build on top of each other:
(1) quantity span identification, (2) unit extraction from the identified
quantities and their value modifier classification, (3) span identification for
measured entities and measured properties, (4) qualifier span identification,
and (5) relation extraction between the identified quantities, measured
entities, measured properties, and qualifiers. We approached these challenges
by first identifying the quantities, extracting their units of measurement,
classifying them with corresponding modifiers, and afterwards using them to
jointly solve the last three subtasks in a multi-turn question answering
manner. Our best performing model obtained an overlapping F1-score of 36.91% on
the test set.
| 2,021 |
Computation and Language
|
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition
|
We introduce Lookup-Table Language Models (LookupLM), a method for scaling up
the size of RNN language models with only a constant increase in the floating
point operations, by increasing the expressivity of the embedding table. In
particular, we instantiate an (additional) embedding table which embeds the
previous n-gram token sequence, rather than a single token. This allows the
embedding table to be scaled up arbitrarily -- with a commensurate increase in
performance -- without changing the token vocabulary. Since embeddings are
sparsely retrieved from the table via a lookup; increasing the size of the
table adds neither extra operations to each forward pass nor extra parameters
that need to be stored on limited GPU/TPU memory. We explore scaling n-gram
embedding tables up to nearly a billion parameters. When trained on a 3-billion
sentence corpus, we find that LookupLM improves long tail log perplexity by
2.44 and long tail WER by 23.4% on a downstream speech recognition task over a
standard RNN language model baseline, an improvement comparable to a scaling up
the baseline by 6.2x the number of floating point operations.
| 2,021 |
Computation and Language
|
Text2Chart: A Multi-Staged Chart Generator from Natural Language Text
|
Generation of scientific visualization from analytical natural language text
is a challenging task. In this paper, we propose Text2Chart, a multi-staged
chart generator method. Text2Chart takes natural language text as input and
produce visualization as two-dimensional charts. Text2Chart approaches the
problem in three stages. Firstly, it identifies the axis elements of a chart
from the given text known as x and y entities. Then it finds a mapping of
x-entities with its corresponding y-entities. Next, it generates a chart type
suitable for the given text: bar, line or pie. Combination of these three
stages is capable of generating visualization from the given analytical text.
We have also constructed a dataset for this problem. Experiments show that
Text2Chart achieves best performances with BERT based encodings with LSTM
models in the first stage to label x and y entities, Random Forest classifier
for the mapping stage and fastText embedding with LSTM for the chart type
prediction. In our experiments, all the stages show satisfactory results and
effectiveness considering formation of charts from analytical text, achieving a
commendable overall performance.
| 2,021 |
Computation and Language
|
WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for
Detecting Toxic Spans
|
In recent years, the widespread use of social media has led to an increase in
the generation of toxic and offensive content on online platforms. In response,
social media platforms have worked on developing automatic detection methods
and employing human moderators to cope with this deluge of offensive content.
While various state-of-the-art statistical models have been applied to detect
toxic posts, there are only a few studies that focus on detecting the words or
expressions that make a post offensive. This motivates the organization of the
SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided
participants with a dataset containing toxic spans annotation in English posts.
In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Our
best performing neural transformer model achieves an $0.68$ F1-Score.
Furthermore, we develop an open-source framework for multilingual detection of
offensive spans, i.e., MUDES, based on neural transformers that detect toxic
spans in texts.
| 2,021 |
Computation and Language
|
TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and
Cross-lingual Word-in-Context Disambiguation
|
Identifying whether a word carries the same meaning or different meaning in
two contexts is an important research area in natural language processing which
plays a significant role in many applications such as question answering,
document summarisation, information retrieval and information extraction. Most
of the previous work in this area rely on language-specific resources making it
difficult to generalise across languages. Considering this limitation, our
approach to SemEval-2021 Task 2 is based only on pretrained transformer models
and does not use any language-specific processing and resources. Despite that,
our best model achieves 0.90 accuracy for English-English subtask which is very
compatible compared to the best result of the subtask; 0.93 accuracy. Our
approach also achieves satisfactory results in other monolingual and
cross-lingual language pairs as well.
| 2,021 |
Computation and Language
|
Adapting Language Models for Zero-shot Learning by Meta-tuning on
Dataset and Prompt Collections
|
Large pre-trained language models (LMs) such as GPT-3 have acquired a
surprising ability to perform zero-shot learning. For example, to classify
sentiment without any training examples, we can "prompt" the LM with the review
and the label description "Does the user like this movie?", and ask whether the
next word is "yes" or "no". However, the next word prediction training
objective is still misaligned with the target zero-shot learning objective. To
address this weakness, we propose meta-tuning, which directly optimizes the
zero-shot learning objective by fine-tuning pre-trained language models on a
collection of datasets. We focus on classification tasks, and construct the
meta-dataset by aggregating 43 existing datasets and annotating 441 label
descriptions in a question-answering (QA) format. When evaluated on unseen
tasks, meta-tuned models outperform a same-sized QA model and the previous SOTA
zero-shot learning system based on natural language inference. Additionally,
increasing parameter count from 220M to 770M improves AUC-ROC scores by 6.3%,
and we forecast that even larger models would perform better. Therefore,
measuring zero-shot learning performance on language models out-of-the-box
might underestimate their true potential, and community-wide efforts on
aggregating datasets and unifying their formats can help build models that
answer prompts better.
| 2,021 |
Computation and Language
|
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
|
Given a database schema, Text-to-SQL aims to translate a natural language
question into the corresponding SQL query. Under the setup of cross-domain,
traditional semantic parsing models struggle to adapt to unseen database
schemas. To improve the model generalization capability for rare and unseen
schemas, we propose a new architecture, ShadowGNN, which processes schemas at
abstract and semantic levels. By ignoring names of semantic items in databases,
abstract schemas are exploited in a well-designed graph projection neural
network to obtain delexicalized representation of question and schema. Based on
the domain-independent representations, a relation-aware transformer is
utilized to further extract logical linking between question and schema.
Finally, a SQL decoder with context-free grammar is applied. On the challenging
Text-to-SQL benchmark Spider, empirical results show that ShadowGNN outperforms
state-of-the-art models. When the annotated data is extremely limited (only
10\% training set), ShadowGNN gets over absolute 5\% performance gain, which
shows its powerful generalization ability. Our implementation will be
open-sourced at \url{https://github.com/WowCZ/shadowgnn}.
| 2,021 |
Computation and Language
|
Not All Attention Is All You Need
|
Beyond the success story of pre-trained language models (PrLMs) in recent
natural language processing, they are susceptible to over-fitting due to
unusual large model size. To this end, dropout serves as a therapy. However,
existing methods like random-based, knowledge-based and search-based dropout
are more general but less effective onto self-attention based models, which are
broadly chosen as the fundamental architecture of PrLMs. In this paper, we
propose a novel dropout method named AttendOut to let self-attention empowered
PrLMs capable of more robust task-specific tuning. We demonstrate that
state-of-the-art models with elaborate training design may achieve much
stronger results. We verify the universality of our approach on extensive
natural language processing tasks.
| 2,021 |
Computation and Language
|
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute
Representation Learning
|
While relation extraction is an essential task in knowledge acquisition and
representation, and new-generated relations are common in the real world, less
effort is made to predict unseen relations that cannot be observed at the
training stage. In this paper, we formulate the zero-shot relation extraction
problem by incorporating the text description of seen and unseen relations. We
propose a novel multi-task learning model, zero-shot BERT (ZS-BERT), to
directly predict unseen relations without hand-crafted attribute labeling and
multiple pairwise classifications. Given training instances consisting of input
sentences and the descriptions of their relations, ZS-BERT learns two functions
that project sentences and relation descriptions into an embedding space by
jointly minimizing the distances between them and classifying seen relations.
By generating the embeddings of unseen relations and new-coming sentences based
on such two functions, we use nearest neighbor search to obtain the prediction
of unseen relations. Experiments conducted on two well-known datasets exhibit
that ZS-BERT can outperform existing methods by at least 13.54\% improvement on
F1 score.
| 2,021 |
Computation and Language
|
Fool Me Twice: Entailment from Wikipedia Gamification
|
We release FoolMeTwice (FM2 for short), a large dataset of challenging
entailment pairs collected through a fun multi-player game. Gamification
encourages adversarial examples, drastically lowering the number of examples
that can be solved using "shortcuts" compared to other popular entailment
datasets. Players are presented with two tasks. The first task asks the player
to write a plausible claim based on the evidence from a Wikipedia page. The
second one shows two plausible claims written by other players, one of which is
false, and the goal is to identify it before the time runs out. Players "pay"
to see clues retrieved from the evidence pool: the more evidence the player
needs, the harder the claim. Game-play between motivated players leads to
diverse strategies for crafting claims, such as temporal inference and
diverting to unrelated evidence, and results in higher quality data for the
entailment and evidence retrieval tasks. We open source the dataset and the
game code.
| 2,021 |
Computation and Language
|
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
|
Meta-learning, or learning to learn, is a technique that can help to overcome
resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to
new tasks. We apply model-agnostic meta-learning (MAML) to the task of
cross-lingual dependency parsing. We train our model on a diverse set of
languages to learn a parameter initialization that can adapt quickly to new
languages. We find that meta-learning with pre-training can significantly
improve upon the performance of language transfer and standard supervised
learning baselines for a variety of unseen, typologically diverse, and
low-resource languages, in a few-shot learning setup.
| 2,022 |
Computation and Language
|
MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with
Pre-trained Language Models for Toxic Spans Detection
|
This paper describes our system for SemEval-2021 Task 5 on Toxic Spans
Detection. We developed ensemble models using BERT-based neural architectures
and post-processing to combine tokens into spans. We evaluated several
pre-trained language models using various ensemble techniques for toxic span
identification and achieved sizable improvements over our baseline fine-tuned
BERT models. Finally, our system obtained a F1-score of 67.55% on test data.
| 2,021 |
Computation and Language
|
Imperfect also Deserves Reward: Multi-Level and Sequential Reward
Modeling for Better Dialog Management
|
For task-oriented dialog systems, training a Reinforcement Learning (RL)
based Dialog Management module suffers from low sample efficiency and slow
convergence speed due to the sparse rewards in RL.To solve this problem, many
strategies have been proposed to give proper rewards when training RL, but
their rewards lack interpretability and cannot accurately estimate the
distribution of state-action pairs in real dialogs. In this paper, we propose a
multi-level reward modeling approach that factorizes a reward into a
three-level hierarchy: domain, act, and slot. Based on inverse adversarial
reinforcement learning, our designed reward model can provide more accurate and
explainable reward signals for state-action pairs.Extensive evaluations show
that our approach can be applied to a wide range of reinforcement
learning-based dialog systems and significantly improves both the performance
and the speed of convergence.
| 2,021 |
Computation and Language
|
NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model
Performance
|
Pre-trained neural language models give high performance on natural language
inference (NLI) tasks. But whether they actually understand the meaning of the
processed sequences remains unclear. We propose a new diagnostics test suite
which allows to assess whether a dataset constitutes a good testbed for
evaluating the models' meaning understanding capabilities. We specifically
apply controlled corruption transformations to widely used benchmarks (MNLI and
ANLI), which involve removing entire word classes and often lead to
non-sensical sentence pairs. If model accuracy on the corrupted data remains
high, then the dataset is likely to contain statistical biases and artefacts
that guide prediction. Inversely, a large decrease in model accuracy indicates
that the original dataset provides a proper challenge to the models' reasoning
capabilities. Hence, our proposed controls can serve as a crash test for
developing high quality data for NLI tasks.
| 2,021 |
Computation and Language
|
UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span
Detection using Attention-based, Named Entity Recognition, and Ensemble
Models
|
Detecting which parts of a sentence contribute to that sentence's toxicity --
rather than providing a sentence-level verdict of hatefulness -- would increase
the interpretability of models and allow human moderators to better understand
the outputs of the system. This paper presents our team's, UTNLP, methodology
and results in the SemEval-2021 shared task 5 on toxic spans detection. We test
multiple models and contextual embeddings and report the best setting out of
all. The experiments start with keyword-based models and are followed by
attention-based, named entity-based, transformers-based, and ensemble models.
Our best approach, an ensemble model, achieves an F1 of 0.684 in the
competition's evaluation phase.
| 2,021 |
Computation and Language
|
Non-autoregressive Transformer-based End-to-end ASR using BERT
|
Transformer-based models have led to significant innovation in classical and
practical subjects as varied as speech processing, natural language processing,
and computer vision. On top of the Transformer, attention-based end-to-end
automatic speech recognition (ASR) models have recently become popular.
Specifically, non-autoregressive modeling, which boasts fast inference and
performance comparable to conventional autoregressive methods, is an emerging
research topic. In the context of natural language processing, the
bidirectional encoder representations from Transformers (BERT) model has
received widespread attention, partially due to its ability to infer
contextualized word representations and to enable superior performance for
downstream tasks while needing only simple fine-tuning. Motivated by the
success, we intend to view speech recognition as a downstream task of BERT,
thus an ASR system is expected to be deduced by performing fine-tuning.
Consequently, to not only inherit the advantages of non-autoregressive ASR
models but also enjoy the benefits of a pre-trained language model (e.g.,
BERT), we propose a non-autoregressive Transformer-based end-to-end ASR model
based on BERT. We conduct a series of experiments on the AISHELL-1 dataset that
demonstrate competitive or superior results for the model when compared to
state-of-the-art ASR systems.
| 2,022 |
Computation and Language
|
FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection
|
In this paper, we introduce FreSaDa, a French Satire Data Set, which is
composed of 11,570 articles from the news domain. In order to avoid reporting
unreasonably high accuracy rates due to the learning of characteristics
specific to publication sources, we divided our samples into training,
validation and test, such that the training publication sources are distinct
from the validation and test publication sources. This gives rise to a
cross-domain (cross-source) satire detection task. We employ two classification
methods as baselines for our new data set, one based on low-level features
(character n-grams) and one based on high-level features (average of CamemBERT
word embeddings). As an additional contribution, we present an unsupervised
domain adaptation method based on regarding the pairwise similarities (given by
the dot product) between the training samples and the validation samples as
features. By including these domain-specific features, we attain significant
improvements for both character n-grams and CamemBERT embeddings.
| 2,021 |
Computation and Language
|
FRAKE: Fusional Real-time Automatic Keyword Extraction
|
Keyword extraction is the process of identifying the words or phrases that
express the main concepts of text to the best of one's ability. Electronic
infrastructure creates a considerable amount of text every day and at all
times. This massive volume of documents makes it practically impossible for
human resources to study and manage them. Nevertheless, the need for these
documents to be accessed efficiently and effectively is evident in numerous
purposes. A blog, news article, or technical note is considered a relatively
long text since the reader aims to learn the subject based on keywords or
topics. Our approach consists of a combination of two models: graph centrality
features and textural features. The proposed method has been used to extract
the best keyword among the candidate keywords with an optimal combination of
graph centralities, such as degree, betweenness, eigenvector, closeness
centrality and etc, and textural, such as Casing, Term position, Term frequency
normalization, Term different sentence, Part Of Speech tagging. There have also
been attempts to distinguish keywords from candidate phrases and consider them
on separate keywords. For evaluating the proposed method, seven datasets were
used: Semeval2010, SemEval2017, Inspec, fao30, Thesis100, pak2018, and
Wikinews, with results reported as Precision, Recall, and F- measure. Our
proposed method performed much better in terms of evaluation metrics in all
reviewed datasets compared with available methods in literature. An approximate
16.9% increase was witnessed in F-score metric and this was much more for the
Inspec in English datasets and WikiNews in forgone languages.
| 2,021 |
Computation and Language
|
Sentiment-based Candidate Selection for NMT
|
The explosion of user-generated content (UGC)--e.g. social media posts,
comments, and reviews--has motivated the development of NLP applications
tailored to these types of informal texts. Prevalent among these applications
have been sentiment analysis and machine translation (MT). Grounded in the
observation that UGC features highly idiomatic, sentiment-charged language, we
propose a decoder-side approach that incorporates automatic sentiment scoring
into the MT candidate selection process. We train separate English and Spanish
sentiment classifiers, then, using n-best candidates generated by a baseline MT
model with beam search, select the candidate that minimizes the absolute
difference between the sentiment score of the source sentence and that of the
translation, and perform a human evaluation to assess the produced
translations. Unlike previous work, we select this minimally divergent
translation by considering the sentiment scores of the source sentence and
translation on a continuous interval, rather than using e.g. binary
classification, allowing for more fine-grained selection of translation
candidates. The results of human evaluations show that, in comparison to the
open-source MT baseline model on top of which our sentiment-based pipeline is
built, our pipeline produces more accurate translations of colloquial,
sentiment-heavy source texts.
| 2,021 |
Computation and Language
|
Identifying and Categorizing Offensive Language in Social Media
|
Offensive language is pervasive in social media. Individuals frequently take
advantage of the perceived anonymity of computer-mediated communication, using
this to engage in behavior that many of them would not consider in real life.
The automatic identification of offensive content online is an important task
that has gained more attention in recent years. This task can be modeled as a
supervised classification problem in which systems are trained using a dataset
containing posts that are annotated with respect to the presence of some
form(s) of abusive or offensive content. The objective of this study is to
provide a description of a classification system built for SemEval-2019 Task 6:
OffensEval. This system classifies a tweet as either offensive or not offensive
(Sub-task A) and further classifies offensive tweets into categories (Sub-tasks
B \& C). We trained machine learning and deep learning models along with data
preprocessing and sampling techniques to come up with the best results. Models
discussed include Naive Bayes, SVM, Logistic Regression, Random Forest and
LSTM.
| 2,021 |
Computation and Language
|
Disentangled Contrastive Learning for Learning Robust Textual
Representations
|
Although the self-supervised pre-training of transformer models has resulted
in the revolutionizing of natural language processing (NLP) applications and
the achievement of state-of-the-art results with regard to various benchmarks,
this process is still vulnerable to small and imperceptible permutations
originating from legitimate inputs. Intuitively, the representations should be
similar in the feature space with subtle input permutations, while large
variations occur with different meanings. This motivates us to investigate the
learning of robust textual representation in a contrastive manner. However, it
is non-trivial to obtain opposing semantic instances for textual samples. In
this study, we propose a disentangled contrastive learning method that
separately optimizes the uniformity and alignment of representations without
negative sampling. Specifically, we introduce the concept of momentum
representation consistency to align features and leverage power normalization
while conforming the uniformity. Our experimental results for the NLP
benchmarks demonstrate that our approach can obtain better results compared
with the baselines, as well as achieve promising improvements with invariance
tests and adversarial attacks. The code is available in
https://github.com/zxlzr/DCL.
| 2,023 |
Computation and Language
|
Edge: Enriching Knowledge Graph Embeddings with External Text
|
Knowledge graphs suffer from sparsity which degrades the quality of
representations generated by various methods. While there is an abundance of
textual information throughout the web and many existing knowledge bases,
aligning information across these diverse data sources remains a challenge in
the literature. Previous work has partially addressed this issue by enriching
knowledge graph entities based on "hard" co-occurrence of words present in the
entities of the knowledge graphs and external text, while we achieve "soft"
augmentation by proposing a knowledge graph enrichment and embedding framework
named Edge. Given an original knowledge graph, we first generate a rich but
noisy augmented graph using external texts in semantic and structural level. To
distill the relevant knowledge and suppress the introduced noise, we design a
graph alignment term in a shared embedding space between the original graph and
augmented graph. To enhance the embedding learning on the augmented graph, we
further regularize the locality relationship of target entity based on negative
sampling. Experimental results on four benchmark datasets demonstrate the
robustness and effectiveness of Edge in link prediction and node
classification.
| 2,021 |
Computation and Language
|
Cross-Lingual Word Embedding Refinement by $\ell_{1}$ Norm Optimisation
|
Cross-Lingual Word Embeddings (CLWEs) encode words from two or more languages
in a shared high-dimensional space in which vectors representing words with
similar meaning (regardless of language) are closely located. Existing methods
for building high-quality CLWEs learn mappings that minimise the $\ell_{2}$
norm loss function. However, this optimisation objective has been demonstrated
to be sensitive to outliers. Based on the more robust Manhattan norm (aka.
$\ell_{1}$ norm) goodness-of-fit criterion, this paper proposes a simple
post-processing step to improve CLWEs. An advantage of this approach is that it
is fully agnostic to the training process of the original CLWEs and can
therefore be applied widely. Extensive experiments are performed involving ten
diverse languages and embeddings trained on different corpora. Evaluation
results based on bilingual lexicon induction and cross-lingual transfer for
natural language inference tasks show that the $\ell_{1}$ refinement
substantially outperforms four state-of-the-art baselines in both supervised
and unsupervised settings. It is therefore recommended that this strategy be
adopted as a standard for CLWE methods.
| 2,021 |
Computation and Language
|
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented
Dialog
|
Semantic parsing using sequence-to-sequence models allows parsing of deeper
representations compared to traditional word tagging based models. In spite of
these advantages, widespread adoption of these models for real-time
conversational use cases has been stymied by higher compute requirements and
thus higher latency. In this work, we propose a non-autoregressive approach to
predict semantic parse trees with an efficient seq2seq model architecture. By
combining non-autoregressive prediction with convolutional neural networks, we
achieve significant latency gains and parameter size reduction compared to
traditional RNN models. Our novel architecture achieves up to an 81% reduction
in latency on TOP dataset and retains competitive performance to non-pretrained
models on three different semantic parsing datasets. Our code is available at
https://github.com/facebookresearch/pytext
| 2,021 |
Computation and Language
|
UniDrop: A Simple yet Effective Technique to Improve Transformer without
Extra Cost
|
Transformer architecture achieves great success in abundant natural language
processing tasks. The over-parameterization of the Transformer model has
motivated plenty of works to alleviate its overfitting for superior
performances. With some explorations, we find simple techniques such as
dropout, can greatly boost model performance with a careful design. Therefore,
in this paper, we integrate different dropout techniques into the training of
Transformer models. Specifically, we propose an approach named UniDrop to
unites three different dropout techniques from fine-grain to coarse-grain,
i.e., feature dropout, structure dropout, and data dropout. Theoretically, we
demonstrate that these three dropouts play different roles from regularization
perspectives. Empirically, we conduct experiments on both neural machine
translation and text classification benchmark datasets. Extensive results
indicate that Transformer with UniDrop can achieve around 1.5 BLEU improvement
on IWSLT14 translation tasks, and better accuracy for the classification even
using strong pre-trained RoBERTa as backbone.
| 2,021 |
Computation and Language
|
Conversational Semantic Role Labeling
|
Semantic role labeling (SRL) aims to extract the arguments for each predicate
in an input sentence. Traditional SRL can fail to analyze dialogues because it
only works on every single sentence, while ellipsis and anaphora frequently
occur in dialogues. To address this problem, we propose the conversational SRL
task, where an argument can be the dialogue participants, a phrase in the
dialogue history or the current sentence. As the existing SRL datasets are in
the sentence level, we manually annotate semantic roles for 3,000 chit-chat
dialogues (27,198 sentences) to boost the research in this direction.
Experiments show that while traditional SRL systems (even with the help of
coreference resolution or rewriting) perform poorly for analyzing dialogues,
modeling dialogue histories and participants greatly helps the performance,
indicating that adapting SRL to conversations is very promising for universal
dialogue understanding. Our initial study by applying CSRL to two mainstream
conversational tasks, dialogue response generation and dialogue context
rewriting, also confirms the usefulness of CSRL.
| 2,021 |
Computation and Language
|
Innovative Bert-based Reranking Language Models for Speech Recognition
|
More recently, Bidirectional Encoder Representations from Transformers (BERT)
was proposed and has achieved impressive success on many natural language
processing (NLP) tasks such as question answering and language understanding,
due mainly to its effective pre-training then fine-tuning paradigm as well as
strong local contextual modeling ability. In view of the above, this paper
presents a novel instantiation of the BERT-based contextualized language models
(LMs) for use in reranking of N-best hypotheses produced by automatic speech
recognition (ASR). To this end, we frame N-best hypothesis reranking with BERT
as a prediction problem, which aims to predict the oracle hypothesis that has
the lowest word error rate (WER) given the N-best hypotheses (denoted by
PBERT). In particular, we also explore to capitalize on task-specific global
topic information in an unsupervised manner to assist PBERT in N-best
hypothesis reranking (denoted by TPBERT). Extensive experiments conducted on
the AMI benchmark corpus demonstrate the effectiveness and feasibility of our
methods in comparison to the conventional autoregressive models like the
recurrent neural network (RNN) and a recently proposed method that employed
BERT to compute pseudo-log-likelihood (PLL) scores for N-best hypothesis
reranking.
| 2,021 |
Computation and Language
|
Does syntax matter? A strong baseline for Aspect-based Sentiment
Analysis with RoBERTa
|
Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities
for aspects, is a fine-grained task in the field of sentiment analysis.
Previous work showed syntactic information, e.g. dependency trees, can
effectively improve the ABSA performance. Recently, pre-trained models (PTMs)
also have shown their effectiveness on ABSA. Therefore, the question naturally
arises whether PTMs contain sufficient syntactic information for ABSA so that
we can obtain a good ABSA model only based on PTMs. In this paper, we firstly
compare the induced trees from PTMs and the dependency parsing trees on several
popular models for the ABSA task, showing that the induced tree from fine-tuned
RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. The further analysis
experiments reveal that the FT-RoBERTa Induced Tree is more
sentiment-word-oriented and could benefit the ABSA task. The experiments also
show that the pure RoBERTa-based model can outperform or approximate to the
previous SOTA performances on six datasets across four languages since it
implicitly incorporates the task-oriented syntactic information.
| 2,021 |
Computation and Language
|
NorDial: A Preliminary Corpus of Written Norwegian Dialect Use
|
Norway has a large amount of dialectal variation, as well as a general
tolerance to its use in the public sphere. There are, however, few available
resources to study this variation and its change over time and in more informal
areas, \eg on social media. In this paper, we propose a first step to creating
a corpus of dialectal variation of written Norwegian. We collect a small corpus
of tweets and manually annotate them as Bokm{\aa}l, Nynorsk, any dialect, or a
mix. We further perform preliminary experiments with state-of-the-art models,
as well as an analysis of the data to expand this corpus in the future.
Finally, we make the annotations and models available for future work.
| 2,021 |
Computation and Language
|
Unsupervised Learning of Explainable Parse Trees for Improved
Generalisation
|
Recursive neural networks (RvNN) have been shown useful for learning sentence
representations and helped achieve competitive performance on several natural
language inference tasks. However, recent RvNN-based models fail to learn
simple grammar and meaningful semantics in their intermediate tree
representation. In this work, we propose an attention mechanism over Tree-LSTMs
to learn more meaningful and explainable parse tree structures. We also
demonstrate the superior performance of our proposed model on natural language
inference, semantic relatedness, and sentiment analysis tasks and compare them
with other state-of-the-art RvNN based methods. Further, we present a detailed
qualitative and quantitative analysis of the learned parse trees and show that
the discovered linguistic structures are more explainable, semantically
meaningful, and grammatically correct than recent approaches. The source code
of the paper is available at
https://github.com/atul04/Explainable-Latent-Structures-Using-Attention.
| 2,021 |
Computation and Language
|
The structure of online social networks modulates the rate of lexical
change
|
New words are regularly introduced to communities, yet not all of these words
persist in a community's lexicon. Among the many factors contributing to
lexical change, we focus on the understudied effect of social networks. We
conduct a large-scale analysis of over 80k neologisms in 4420 online
communities across a decade. Using Poisson regression and survival analysis,
our study demonstrates that the community's network structure plays a
significant role in lexical change. Apart from overall size, properties
including dense connections, the lack of local clusters and more external
contacts promote lexical innovation and retention. Unlike offline communities,
these topic-based communities do not experience strong lexical levelling
despite increased contact but accommodate more niche words. Our work provides
support for the sociolinguistic hypothesis that lexical change is partially
shaped by the structure of the underlying network but also uncovers findings
specific to online communities.
| 2,021 |
Computation and Language
|
WEC: Deriving a Large-scale Cross-document Event Coreference dataset
from Wikipedia
|
Cross-document event coreference resolution is a foundational task for NLP
applications involving multi-text processing. However, existing corpora for
this task are scarce and relatively small, while annotating only modest-size
clusters of documents belonging to the same topic. To complement these
resources and enhance future research, we present Wikipedia Event Coreference
(WEC), an efficient methodology for gathering a large-scale dataset for
cross-document event coreference from Wikipedia, where coreference links are
not restricted within predefined topics. We apply this methodology to the
English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our
dataset creation method is generic and can be applied with relatively little
effort to other Wikipedia languages. To set baseline results, we develop an
algorithm that adapts components of state-of-the-art models for within-document
coreference resolution to the cross-document setting. Our model is suitably
efficient and outperforms previously published state-of-the-art results for the
task.
| 2,021 |
Computation and Language
|
NeMo Inverse Text Normalization: From Development To Production
|
Inverse text normalization (ITN) converts spoken-domain automatic speech
recognition (ASR) output into written-domain text to improve the readability of
the ASR output. Many state-of-the-art ITN systems use hand-written weighted
finite-state transducer(WFST) grammars since this task has extremely low
tolerance to unrecoverable errors. We introduce an open-source Python
WFST-based library for ITN which enables a seamless path from development to
production. We describe the specification of ITN grammar rules for English, but
the library can be adapted for other languages. It can also be used for
written-to-spoken text normalization. We evaluate the NeMo ITN library using a
modified version of the Google Text normalization dataset.
| 2,021 |
Computation and Language
|
Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual
Neural Topic Modeling
|
Neural topic models can augment or replace bag-of-words inputs with the
learned representations of deep pre-trained transformer-based word prediction
models. One added benefit when using representations from multilingual models
is that they facilitate zero-shot polylingual topic modeling. However, while it
has been widely observed that pre-trained embeddings should be fine-tuned to a
given task, it is not immediately clear what supervision should look like for
an unsupervised task such as topic modeling. Thus, we propose several methods
for fine-tuning encoders to improve both monolingual and zero-shot polylingual
neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing
a new topic classification task, integrating the topic classification objective
directly into topic model training, and continued pre-training. We find that
fine-tuning encoder representations on topic classification and integrating the
topic classification task directly into topic modeling improves topic quality,
and that fine-tuning encoder representations on any task is the most important
factor for facilitating cross-lingual transfer.
| 2,021 |
Computation and Language
|
Constructing Contrastive samples via Summarization for Text
Classification with limited annotations
|
Contrastive Learning has emerged as a powerful representation learning method
and facilitates various downstream tasks especially when supervised data is
limited. How to construct efficient contrastive samples through data
augmentation is key to its success. Unlike vision tasks, the data augmentation
method for contrastive learning has not been investigated sufficiently in
language tasks. In this paper, we propose a novel approach to construct
contrastive samples for language tasks using text summarization. We use these
samples for supervised contrastive learning to gain better text representations
which greatly benefit text classification tasks with limited annotations. To
further improve the method, we mix up samples from different classes and add an
extra regularization, named Mixsum, in addition to the cross-entropy-loss.
Experiments on real-world text classification datasets (Amazon-5, Yelp-5, AG
News, and IMDb) demonstrate the effectiveness of the proposed contrastive
learning framework with summarization-based data augmentation and Mixsum
regularization.
| 2,021 |
Computation and Language
|
Disentangling Semantics and Syntax in Sentence Embeddings with
Pre-trained Language Models
|
Pre-trained language models have achieved huge success on a wide range of NLP
tasks. However, contextual representations from pre-trained models contain
entangled semantic and syntactic information, and therefore cannot be directly
used to derive useful semantic sentence embeddings for some tasks. Paraphrase
pairs offer an effective way of learning the distinction between semantics and
syntax, as they naturally share semantics and often vary in syntax. In this
work, we present ParaBART, a semantic sentence embedding model that learns to
disentangle semantics and syntax in sentence embeddings obtained by pre-trained
language models. ParaBART is trained to perform syntax-guided paraphrasing,
based on a source sentence that shares semantics with the target paraphrase,
and a parse tree that specifies the target syntax. In this way, ParaBART learns
disentangled semantic and syntactic representations from their respective
inputs with separate encoders. Experiments in English show that ParaBART
outperforms state-of-the-art sentence embedding models on unsupervised semantic
similarity tasks. Additionally, we show that our approach can effectively
remove syntactic information from semantic sentence embeddings, leading to
better robustness against syntactic variation on downstream semantic tasks.
| 2,021 |
Computation and Language
|
Assessing Reference-Free Peer Evaluation for Machine Translation
|
Reference-free evaluation has the potential to make machine translation
evaluation substantially more scalable, allowing us to pivot easily to new
languages or domains. It has been recently shown that the probabilities given
by a large, multilingual model can achieve state of the art results when used
as a reference-free metric. We experiment with various modifications to this
model and demonstrate that by scaling it up we can match the performance of
BLEU. We analyze various potential weaknesses of the approach and find that it
is surprisingly robust and likely to offer reasonable performance across a
broad spectrum of domains and different system qualities.
| 2,021 |
Computation and Language
|
Estimation of Summary-to-Text Inconsistency by Mismatched Embeddings
|
We propose a new reference-free summary quality evaluation measure, with
emphasis on the faithfulness. The measure is designed to find and count all
possible minute inconsistencies of the summary with respect to the source
document. The proposed ESTIME, Estimator of Summary-to-Text Inconsistency by
Mismatched Embeddings, correlates with expert scores in summary-level SummEval
dataset stronger than other common evaluation measures not only in Consistency
but also in Fluency. We also introduce a method of generating subtle factual
errors in human summaries. We show that ESTIME is more sensitive to subtle
errors than other common evaluation measures.
| 2,021 |
Computation and Language
|
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text
Style Transfer
|
Text style transfer aims to controllably generate text with targeted
stylistic changes while maintaining core meaning from the source sentence
constant. Many of the existing style transfer benchmarks primarily focus on
individual high-level semantic changes (e.g. positive to negative), which
enable controllability at a high level but do not offer fine-grained control
involving sentence structure, emphasis, and content of the sentence. In this
paper, we introduce a large-scale benchmark, StylePTB, with (1) paired
sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical,
syntactic, semantic, and thematic transfers of text, as well as (2)
compositions of multiple transfers which allow modeling of fine-grained
stylistic changes as building blocks for more complex, high-level transfers. By
benchmarking existing methods on StylePTB, we find that they struggle to model
fine-grained changes and have an even more difficult time composing multiple
styles. As a result, StylePTB brings novel challenges that we hope will
encourage future research in controllable text style transfer, compositional
models, and learning disentangled representations. Solving these challenges
would present important steps towards controllable text generation.
| 2,021 |
Computation and Language
|
Contextualized Knowledge-aware Attentive Neural Network: Enhancing
Answer Selection with Knowledge
|
Answer selection, which is involved in many natural language processing
applications such as dialog systems and question answering (QA), is an
important yet challenging task in practice, since conventional methods
typically suffer from the issues of ignoring diverse real-world background
knowledge. In this paper, we extensively investigate approaches to enhancing
the answer selection model with external knowledge from knowledge graph (KG).
First, we present a context-knowledge interaction learning framework,
Knowledge-aware Neural Network (KNN), which learns the QA sentence
representations by considering a tight interaction with the external knowledge
from KG and the textual information. Then, we develop two kinds of
knowledge-aware attention mechanism to summarize both the context-based and
knowledge-based interactions between questions and answers. To handle the
diversity and complexity of KG information, we further propose a Contextualized
Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge
representation learning with structure information via a customized Graph
Convolutional Network (GCN) and comprehensively learns context-based and
knowledge-based sentence representation via the multi-view knowledge-aware
attention mechanism. We evaluate our method on four widely-used benchmark QA
datasets, including WikiQA, TREC QA, InsuranceQA and Yahoo QA. Results verify
the benefits of incorporating external knowledge from KG, and show the robust
superiority and extensive applicability of our method.
| 2,021 |
Computation and Language
|
FUDGE: Controlled Text Generation With Future Discriminators
|
We propose Future Discriminators for Generation (FUDGE), a flexible and
modular method for controlled text generation. Given a pre-existing model G for
generating text from a distribution of interest, FUDGE enables conditioning on
a desired attribute a (for example, formality) while requiring access only to
G's output logits. FUDGE learns an attribute predictor operating on a partial
sequence, and uses this predictor's outputs to adjust G's original
probabilities. We show that FUDGE models terms corresponding to a Bayesian
decomposition of the conditional distribution of G given attribute a. Moreover,
FUDGE can easily compose predictors for multiple desired attributes. We
evaluate FUDGE on three tasks -- couplet completion in poetry, topic control in
language generation, and formality change in machine translation -- and observe
gains in all three tasks.
| 2,021 |
Computation and Language
|
HTCInfoMax: A Global Model for Hierarchical Text Classification via
Information Maximization
|
The current state-of-the-art model HiAGM for hierarchical text classification
has two limitations. First, it correlates each text sample with all labels in
the dataset which contains irrelevant information. Second, it does not consider
any statistical constraint on the label representations learned by the
structure encoder, while constraints for representation learning are proved to
be helpful in previous work. In this paper, we propose HTCInfoMax to address
these issues by introducing information maximization which includes two
modules: text-label mutual information maximization and label prior matching.
The first module can model the interaction between each text sample and its
ground truth labels explicitly which filters out irrelevant information. The
second one encourages the structure encoder to learn better representations
with desired characteristics for all labels which can better handle label
imbalance in hierarchical text classification. Experimental results on two
benchmark datasets demonstrate the effectiveness of the proposed HTCInfoMax.
| 2,021 |
Computation and Language
|
Estimating Subjective Crowd-Evaluations as an Additional Objective to
Improve Natural Language Generation
|
Human ratings are one of the most prevalent methods to evaluate the
performance of natural language processing algorithms. Similarly, it is common
to measure the quality of sentences generated by a natural language generation
model using human raters. In this paper, we argue for exploring the use of
subjective evaluations within the process of training language generation
models in a multi-task learning setting. As a case study, we use a
crowd-authored dialogue corpus to fine-tune six different language generation
models. Two of these models incorporate multi-task learning and use subjective
ratings of lines as part of an explicit learning goal. A human evaluation of
the generated dialogue lines reveals that utterances generated by the
multi-tasking models were subjectively rated as the most typical, most moving
the conversation forward, and least offensive. Based on these promising first
results, we discuss future research directions for incorporating subjective
human evaluations into language model training and to hence keep the human user
in the loop during the development process.
| 2,021 |
Computation and Language
|
SuperSim: a test set for word similarity and relatedness in Swedish
|
Language models are notoriously difficult to evaluate. We release SuperSim, a
large-scale similarity and relatedness test set for Swedish built with expert
human judgments. The test set is composed of 1,360 word-pairs independently
judged for both relatedness and similarity by five annotators. We evaluate
three different models (Word2Vec, fastText, and GloVe) trained on two separate
Swedish datasets, namely the Swedish Gigaword corpus and a Swedish Wikipedia
dump, to provide a baseline for future comparison. We release the fully
annotated test set, code, baseline models, and data.
| 2,021 |
Computation and Language
|
Double Perturbation: On the Robustness of Robustness and Counterfactual
Bias Evaluation
|
Robustness and counterfactual bias are usually evaluated on a test dataset.
However, are these evaluations robust? If the test dataset is perturbed
slightly, will the evaluation results keep the same? In this paper, we propose
a "double perturbation" framework to uncover model weaknesses beyond the test
dataset. The framework first perturbs the test dataset to construct abundant
natural sentences similar to the test data, and then diagnoses the prediction
change regarding a single-word substitution. We apply this framework to study
two perturbation-based approaches that are used to analyze models' robustness
and counterfactual bias in English. (1) For robustness, we focus on synonym
substitutions and identify vulnerable examples where prediction can be altered.
Our proposed attack attains high success rates (96.0%-99.8%) in finding
vulnerable examples on both original and robustly trained CNNs and
Transformers. (2) For counterfactual bias, we focus on substituting demographic
tokens (e.g., gender, race) and measure the shift of the expected prediction
among constructed sentences. Our method is able to reveal the hidden model
biases not directly shown in the test dataset. Our code is available at
https://github.com/chong-z/nlp-second-order-attack.
| 2,021 |
Computation and Language
|
Factual Probing Is [MASK]: Learning vs. Learning to Recall
|
Petroni et al. (2019) demonstrated that it is possible to retrieve world
facts from a pre-trained language model by expressing them as cloze-style
prompts and interpret the model's prediction accuracy as a lower bound on the
amount of factual information it encodes. Subsequent work has attempted to
tighten the estimate by searching for better prompts, using a disjoint set of
facts as training data. In this work, we make two complementary contributions
to better understand these factual probing techniques. First, we propose
OptiPrompt, a novel and efficient method which directly optimizes in continuous
embedding space. We find this simple method is able to predict an additional
6.4% of facts in the LAMA benchmark. Second, we raise a more important
question: Can we really interpret these probing results as a lower bound? Is it
possible that these prompt-search methods learn from the training data too? We
find, somewhat surprisingly, that the training data used by these methods
contains certain regularities of the underlying fact distribution, and all the
existing prompt methods, including ours, are able to exploit them for better
fact prediction. We conduct a set of control experiments to disentangle
"learning" from "learning to recall", providing a more detailed picture of what
different prompts can reveal about pre-trained language models.
| 2,021 |
Computation and Language
|
Learning to Remove: Towards Isotropic Pre-trained BERT Embedding
|
Pre-trained language models such as BERT have become a more common choice of
natural language processing (NLP) tasks. Research in word representation shows
that isotropic embeddings can significantly improve performance on downstream
tasks. However, we measure and analyze the geometry of pre-trained BERT
embedding and find that it is far from isotropic. We find that the word vectors
are not centered around the origin, and the average cosine similarity between
two random words is much higher than zero, which indicates that the word
vectors are distributed in a narrow cone and deteriorate the representation
capacity of word embedding. We propose a simple, and yet effective method to
fix this problem: remove several dominant directions of BERT embedding with a
set of learnable weights. We train the weights on word similarity tasks and
show that processed embedding is more isotropic. Our method is evaluated on
three standardized tasks: word similarity, word analogy, and semantic textual
similarity. In all tasks, the word embedding processed by our method
consistently outperforms the original embedding (with average improvement of
13% on word analogy and 16% on semantic textual similarity) and two baseline
methods. Our method is also proven to be more robust to changes of
hyperparameter.
| 2,021 |
Computation and Language
|
Building a Swedish Open-Domain Conversational Language Model
|
We present on-going work of evaluating the, to our knowledge, first large
generative language model trained to converse in Swedish, using data from the
online discussion forum Flashback. We conduct a human evaluation pilot study
that indicates the model is often able to respond to conversations in both a
human-like and informative manner, on a diverse set of topics. While data from
online forums can be useful to build conversational systems, we reflect on the
negative consequences that incautious application might have, and the need for
taking active measures to safeguard against them.
| 2,021 |
Computation and Language
|
Better Feature Integration for Named Entity Recognition
|
It has been shown that named entity recognition (NER) could benefit from
incorporating the long-distance structured information captured by dependency
trees. We believe this is because both types of features - the contextual
information captured by the linear sequences and the structured information
captured by the dependency trees may complement each other. However, existing
approaches largely focused on stacking the LSTM and graph neural networks such
as graph convolutional networks (GCNs) for building improved NER models, where
the exact interaction mechanism between the two types of features is not very
clear, and the performance gain does not appear to be significant. In this
work, we propose a simple and robust solution to incorporate both types of
features with our Synergized-LSTM (Syn-LSTM), which clearly captures how the
two types of features interact. We conduct extensive experiments on several
standard datasets across four languages. The results demonstrate that the
proposed model achieves better performance than previous approaches while
requiring fewer parameters. Our further analysis demonstrates that our model
can capture longer dependencies compared with strong baselines.
| 2,021 |
Computation and Language
|
Stay Together: A System for Single and Split-antecedent Anaphora
Resolution
|
The state-of-the-art on basic, single-antecedent anaphora has greatly
improved in recent years. Researchers have therefore started to pay more
attention to more complex cases of anaphora such as split-antecedent anaphora,
as in Time-Warner is considering a legal challenge to Telecommunications Inc's
plan to buy half of Showtime Networks Inc-a move that could lead to all-out war
between the two powerful companies. Split-antecedent anaphora is rarer and more
complex to resolve than single-antecedent anaphora; as a result, it is not
annotated in many datasets designed to test coreference, and previous work on
resolving this type of anaphora was carried out in unrealistic conditions that
assume gold mentions and/or gold split-antecedent anaphors are available. These
systems also focus on split-antecedent anaphors only. In this work, we
introduce a system that resolves both single and split-antecedent anaphors, and
evaluate it in a more realistic setting that uses predicted mentions. We also
start addressing the question of how to evaluate single and split-antecedent
anaphors together using standard coreference evaluation metrics.
| 2,021 |
Computation and Language
|
Combining exogenous and endogenous signals with a semi-supervised
co-attention network for early detection of COVID-19 fake tweets
|
Fake tweets are observed to be ever-increasing, demanding immediate
countermeasures to combat their spread. During COVID-19, tweets with
misinformation should be flagged and neutralized in their early stages to
mitigate the damages. Most of the existing methods for early detection of fake
news assume to have enough propagation information for large labeled tweets --
which may not be an ideal setting for cases like COVID-19 where both aspects
are largely absent. In this work, we present ENDEMIC, a novel early detection
model which leverages exogenous and endogenous signals related to tweets, while
learning on limited labeled data. We first develop a novel dataset, called CTF
for early COVID-19 Twitter fake news, with additional behavioral test sets to
validate early detection. We build a heterogeneous graph with
follower-followee, user-tweet, and tweet-retweet connections and train a graph
embedding model to aggregate propagation information. Graph embeddings and
contextual features constitute endogenous, while time-relative web-scraped
information constitutes exogenous signals. ENDEMIC is trained in a
semi-supervised fashion, overcoming the challenge of limited labeled data. We
propose a co-attention mechanism to fuse signal representations optimally.
Experimental results on ECTF, PolitiFact, and GossipCop show that ENDEMIC is
highly reliable in detecting early fake tweets, outperforming nine
state-of-the-art methods significantly.
| 2,021 |
Computation and Language
|
Machine Translation Decoding beyond Beam Search
|
Beam search is the go-to method for decoding auto-regressive machine
translation models. While it yields consistent improvements in terms of BLEU,
it is only concerned with finding outputs with high model likelihood, and is
thus agnostic to whatever end metric or score practitioners care about. Our aim
is to establish whether beam search can be replaced by a more powerful
metric-driven search technique. To this end, we explore numerous decoding
algorithms, including some which rely on a value function parameterised by a
neural network, and report results on a variety of metrics. Notably, we
introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its
competitiveness. We provide a blueprint for how to use MCTS fruitfully in
language applications, which opens promising future directions. We find that
which algorithm is best heavily depends on the characteristics of the goal
metric; we believe that our extensive experiments and analysis will inform
further research in this area.
| 2,021 |
Computation and Language
|
The Great Misalignment Problem in Human Evaluation of NLP Methods
|
We outline the Great Misalignment Problem in natural language processing
research, this means simply that the problem definition is not in line with the
method proposed and the human evaluation is not in line with the definition nor
the method. We study this misalignment problem by surveying 10 randomly sampled
papers published in ACL 2020 that report results with human evaluation. Our
results show that only one paper was fully in line in terms of problem
definition, method and evaluation. Only two papers presented a human evaluation
that was in line with what was modeled in the method. These results highlight
that the Great Misalignment Problem is a major one and it affects the validity
and reproducibility of results obtained by a human evaluation.
| 2,021 |
Computation and Language
|
Comparing the Benefit of Synthetic Training Data for Various Automatic
Speech Recognition Architectures
|
Recent publications on automatic-speech-recognition (ASR) have a strong focus
on attention encoder-decoder (AED) architectures which tend to suffer from
over-fitting in low resource scenarios. One solution to tackle this issue is to
generate synthetic data with a trained text-to-speech system (TTS) if
additional text is available. This was successfully applied in many
publications with AED systems, but only very limited in the context of other
ASR architectures. We investigate the effect of varying pre-processing, the
speaker embedding and input encoding of the TTS system w.r.t. the effectiveness
of the synthesized data for AED-ASR training. Additionally, we also consider
internal language model subtraction for the first time, resulting in up to 38%
relative improvement. We compare the AED results to a state-of-the-art hybrid
ASR system, a monophone based system using
connectionist-temporal-classification (CTC) and a monotonic transducer based
system. We show that for the later systems the addition of synthetic data has
no relevant effect, but they still outperform the AED systems on
LibriSpeech-100h. We achieve a final word-error-rate of 3.3%/10.0% with a
hybrid system on the clean/noisy test-sets, surpassing any previous
state-of-the-art systems on Librispeech-100h that do not include unlabeled
audio data.
| 2,021 |
Computation and Language
|
Multilingual Language Models Predict Human Reading Behavior
|
We analyze if large language models are able to predict patterns of human
reading behavior. We compare the performance of language-specific and
multilingual pretrained transformer models to predict reading time measures
reflecting natural human sentence processing on Dutch, English, German, and
Russian texts. This results in accurate models of human reading behavior, which
indicates that transformer models implicitly encode relative importance in
language in a way that is comparable to human processing mechanisms. We find
that BERT and XLM models successfully predict a range of eye tracking features.
In a series of experiments, we analyze the cross-domain and cross-language
abilities of these models and show how they reflect human sentence processing.
| 2,021 |
Computation and Language
|
Developing Annotated Resources for Internal Displacement Monitoring
|
This paper describes in details the design and development of a novel
annotation framework and of annotated resources for Internal Displacement, as
the outcome of a collaboration with the Internal Displacement Monitoring
Centre, aimed at improving the accuracy of their monitoring platform IDETECT.
The schema includes multi-faceted description of the events, including cause,
quantity of people displaced, location and date. Higher-order facets aimed at
improving the information extraction, such as document relevance and type, are
proposed. We also report a case study of machine learning application to the
document classification tasks. Finally, we discuss the importance of
standardized schema in dataset benchmark development and its impact on the
development of reliable disaster monitoring infrastructure.
| 2,021 |
Computation and Language
|
CNN Encoding of Acoustic Parameters for Prominence Detection
|
Expressive reading, considered the defining attribute of oral reading
fluency, comprises the prosodic realization of phrasing and prominence. In the
context of evaluating oral reading, it helps to establish the speaker's
comprehension of the text. We consider a labeled dataset of children's reading
recordings for the speaker-independent detection of prominent words using
acoustic-prosodic and lexico-syntactic features. A previous well-tuned random
forest ensemble predictor is replaced by an RNN sequence classifier to exploit
potential context dependency across the longer utterance. Further, deep
learning is applied to obtain word-level features from low-level acoustic
contours of fundamental frequency, intensity and spectral shape in an
end-to-end fashion. Performance comparisons are presented across the different
feature types and across different feature learning architectures for prominent
word prediction to draw insights wherever possible.
| 2,022 |
Computation and Language
|
Continual Learning for Text Classification with Information
Disentanglement Based Regularization
|
Continual learning has become increasingly important as it enables NLP models
to constantly learn and gain knowledge over time. Previous continual learning
methods are mainly designed to preserve knowledge from previous tasks, without
much emphasis on how to well generalize models to new tasks. In this work, we
propose an information disentanglement based regularization method for
continual learning on text classification. Our proposed method first
disentangles text hidden spaces into representations that are generic to all
tasks and representations specific to each individual task, and further
regularizes these representations differently to better constrain the knowledge
required to generalize. We also introduce two simple auxiliary tasks: next
sentence prediction and task-id prediction, for learning better generic and
specific representation spaces. Experiments conducted on large-scale benchmarks
demonstrate the effectiveness of our method in continual text classification
tasks with various sequences and lengths over state-of-the-art baselines. We
have publicly released our code at https://github.com/GT-SALT/IDBR.
| 2,021 |
Computation and Language
|
Updater-Extractor Architecture for Inductive World State Representations
|
Developing NLP models traditionally involves two stages - training and
application. Retention of information acquired after training (at application
time) is architecturally limited by the size of the model's context window (in
the case of transformers), or by the practical difficulties associated with
long sequences (in the case of RNNs). In this paper, we propose a novel
transformer-based Updater-Extractor architecture and a training procedure that
can work with sequences of arbitrary length and refine its knowledge about the
world based on linguistic inputs. We explicitly train the model to incorporate
incoming information into its world state representation, obtaining strong
inductive generalization and the ability to handle extremely long-range
dependencies. We prove a lemma that provides a theoretical basis for our
approach. The result also provides insight into success and failure modes of
models trained with variants of Truncated Back-Propagation Through Time (such
as Transformer XL). Empirically, we investigate the model performance on three
different tasks, demonstrating its promise. This preprint is still a work in
progress. At present, we focused on easily interpretable tasks, leaving the
application of the proposed ideas to practical NLP applications for the future.
| 2,021 |
Computation and Language
|
Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases
and Symptoms of COVID-19 in Tweets
|
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social
Media Mining for Health Applications (SMM4H) shared tasks. Our system is based
on fine-tuning Distill- BERT on each task, as well as first fine-tuning the
model on the other task. We explore how much fine-tuning is necessary for
accurately classifying tweets as containing self-reported COVID-19 symptoms
(Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal
reporting, or a literature/news mention of the virus (Task 6).
| 2,021 |
Computation and Language
|
BART based semantic correction for Mandarin automatic speech recognition
system
|
Although automatic speech recognition (ASR) systems achieved significantly
improvements in recent years, spoken language recognition error occurs which
can be easily spotted by human beings. Various language modeling techniques
have been developed on post recognition tasks like semantic correction. In this
paper, we propose a Transformer based semantic correction method with
pretrained BART initialization, Experiments on 10000 hours Mandarin speech
dataset show that character error rate (CER) can be effectively reduced by
21.7% relatively compared to our baseline ASR system. Expert evaluation
demonstrates that actual improvement of our model surpasses what CER indicates.
| 2,021 |
Computation and Language
|
Self-Training with Weak Supervision
|
State-of-the-art deep neural networks require large-scale labeled training
data that is often expensive to obtain or not available for many tasks. Weak
supervision in the form of domain-specific rules has been shown to be useful in
such settings to automatically generate weakly labeled training data. However,
learning with weak rules is challenging due to their inherent heuristic and
noisy nature. An additional challenge is rule coverage and overlap, where prior
work on weak supervision only considers instances that are covered by weak
rules, thus leaving valuable unlabeled data behind.
In this work, we develop a weak supervision framework (ASTRA) that leverages
all the available data for a given task. To this end, we leverage task-specific
unlabeled data through self-training with a model (student) that considers
contextualized representations and predicts pseudo-labels for instances that
may not be covered by weak rules. We further develop a rule attention network
(teacher) that learns how to aggregate student pseudo-labels with weak rule
labels, conditioned on their fidelity and the underlying context of an
instance. Finally, we construct a semi-supervised learning objective for
end-to-end training with unlabeled data, domain-specific rules, and a small
amount of labeled data. Extensive experiments on six benchmark datasets for
text classification demonstrate the effectiveness of our approach with
significant improvements over state-of-the-art baselines.
| 2,021 |
Computation and Language
|
Investigating Methods to Improve Language Model Integration for
Attention-based Encoder-Decoder ASR Models
|
Attention-based encoder-decoder (AED) models learn an implicit internal
language model (ILM) from the training transcriptions. The integration with an
external LM trained on much more unpaired text usually leads to better
performance. A Bayesian interpretation as in the hybrid autoregressive
transducer (HAT) suggests dividing by the prior of the discriminative acoustic
model, which corresponds to this implicit LM, similarly as in the hybrid hidden
Markov model approach. The implicit LM cannot be calculated efficiently in
general and it is yet unclear what are the best methods to estimate it. In this
work, we compare different approaches from the literature and propose several
novel methods to estimate the ILM directly from the AED model. Our proposed
methods outperform all previous approaches. We also investigate other methods
to suppress the ILM mainly by decreasing the capacity of the AED model,
limiting the label context, and also by training the AED model together with a
pre-existing LM.
| 2,021 |
Computation and Language
|
WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding
Universal Value" Documents with Soft Labels
|
The UNESCO World Heritage List (WHL) includes the exceptionally valuable
cultural and natural heritage to be preserved for mankind. Evaluating and
justifying the Outstanding Universal Value (OUV) is essential for each site
inscribed in the WHL, and yet a complex task, even for experts, since the
selection criteria of OUV are not mutually exclusive. Furthermore, manual
annotation of heritage values and attributes from multi-source textual data,
which is currently dominant in heritage studies, is knowledge-demanding and
time-consuming, impeding systematic analysis of such authoritative documents in
terms of their implications on heritage management. This study applies
state-of-the-art NLP models to build a classifier on a new dataset containing
Statements of OUV, seeking an explainable and scalable automation tool to
facilitate the nomination, evaluation, research, and monitoring processes of
World Heritage sites. Label smoothing is innovatively adapted to improve the
model performance by adding prior inter-class relationship knowledge to
generate soft labels. The study shows that the best models fine-tuned from BERT
and ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation
on the model prediction shows that the models are sufficiently generalizable.
The study is promising to be further developed and applied in heritage research
and practice.
| 2,021 |
Computation and Language
|
Survey on reinforcement learning for language processing
|
In recent years some researchers have explored the use of reinforcement
learning (RL) algorithms as key components in the solution of various natural
language processing tasks. For instance, some of these algorithms leveraging
deep neural learning have found their way into conversational systems. This
paper reviews the state of the art of RL methods for their possible use for
different problems of natural language processing, focusing primarily on
conversational systems, mainly due to their growing relevance. We provide
detailed descriptions of the problems as well as discussions of why RL is
well-suited to solve them. Also, we analyze the advantages and limitations of
these methods. Finally, we elaborate on promising research directions in
natural language processing that might benefit from reinforcement learning.
| 2,022 |
Computation and Language
|
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers
|
Leveraging deep learning models for Anomaly Detection (AD) has seen
widespread use in recent years due to superior performances over traditional
methods. Recent deep methods for anomalies in images learn better features of
normality in an end-to-end self-supervised setting. These methods train a model
to discriminate between different transformations applied to visual data and
then use the output to compute an anomaly score. We use this approach for AD in
text, by introducing a novel pretext task on text sequences. We learn our DATE
model end-to-end, enforcing two independent and complementary self-supervision
signals, one at the token-level and one at the sequence-level. Under this new
task formulation, we show strong quantitative and qualitative results on the
20Newsgroups and AG News datasets. In the semi-supervised setting, we
outperform state-of-the-art results by +13.5% and +6.9%, respectively (AUROC).
In the unsupervised configuration, DATE surpasses all other methods even when
10% of its training data is contaminated with outliers (compared with 0% for
the others).
| 2,021 |
Computation and Language
|
Samanantar: The Largest Publicly Available Parallel Corpora Collection
for 11 Indic Languages
|
We present Samanantar, the largest publicly available parallel corpora
collection for Indic languages. The collection contains a total of 49.7 million
sentence pairs between English and 11 Indic languages (from two language
families). Specifically, we compile 12.4 million sentence pairs from existing,
publicly-available parallel corpora, and additionally mine 37.4 million
sentence pairs from the web, resulting in a 4x increase. We mine the parallel
sentences from the web by combining many corpora, tools, and methods: (a)
web-crawled monolingual corpora, (b) document OCR for extracting sentences from
scanned documents, (c) multilingual representation models for aligning
sentences, and (d) approximate nearest neighbor search for searching in a large
collection of sentences. Human evaluation of samples from the newly mined
corpora validate the high quality of the parallel sentences across 11
languages. Further, we extract 83.4 million sentence pairs between all 55 Indic
language pairs from the English-centric parallel corpus using English as the
pivot language. We trained multilingual NMT models spanning all these languages
on Samanantar, which outperform existing models and baselines on publicly
available benchmarks, such as FLORES, establishing the utility of Samanantar.
Our data and models are available publicly at
https://ai4bharat.iitm.ac.in/samanantar and we hope they will help advance
research in NMT and multilingual NLP for Indic languages.
| 2,023 |
Computation and Language
|
Semantic Frame Forecast
|
This paper introduces semantic frame forecast, a task that predicts the
semantic frames that will occur in the next 10, 100, or even 1,000 sentences in
a running story. Prior work focused on predicting the immediate future of a
story, such as one to a few sentences ahead. However, when novelists write long
stories, generating a few sentences is not enough to help them gain high-level
insight to develop the follow-up story. In this paper, we formulate a long
story as a sequence of "story blocks," where each block contains a fixed number
of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the
follow-up story arc beyond the scope of a few sentences. We represent a story
block using the term frequencies (TF) of semantic frames in it, normalized by
each frame's inverse document frequency (IDF). We conduct semantic frame
forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific
abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The
results show that automated models can forecast the follow-up story blocks
better than the random, prior, and replay baselines, indicating the task's
feasibility. We also learn that the models using the frame representation as
features outperform all the existing approaches when the block size is over 150
sentences. The human evaluation also shows that the proposed frame
representation, when visualized as word clouds, is comprehensible,
representative, and specific to humans. Our code is available at
https://github.com/appleternity/FrameForecasting.
| 2,021 |
Computation and Language
|
Backtranslation Feedback Improves User Confidence in MT, Not Quality
|
Translating text into a language unknown to the text's author, dubbed
outbound translation, is a modern need for which the user experience has
significant room for improvement, beyond the basic machine translation
facility. We demonstrate this by showing three ways in which user confidence in
the outbound translation, as well as its overall final quality, can be
affected: backward translation, quality estimation (with alignment) and source
paraphrasing. In this paper, we describe an experiment on outbound translation
from English to Czech and Estonian. We examine the effects of each proposed
feedback module and further focus on how the quality of machine translation
systems influence these findings and the user perception of success. We show
that backward translation feedback has a mixed effect on the whole process: it
increases user confidence in the produced translation, but not the objective
quality.
| 2,021 |
Computation and Language
|
On the Inductive Bias of Masked Language Modeling: From Statistical to
Syntactic Dependencies
|
We study how masking and predicting tokens in an unsupervised fashion can
give rise to linguistic structures and downstream performance gains. Recent
theories have suggested that pretrained language models acquire useful
inductive biases through masks that implicitly act as cloze reductions for
downstream tasks. While appealing, we show that the success of the random
masking strategy used in practice cannot be explained by such cloze-like masks
alone. We construct cloze-like masks using task-specific lexicons for three
different classification datasets and show that the majority of pretrained
performance gains come from generic masks that are not associated with the
lexicon. To explain the empirical success of these generic masks, we
demonstrate a correspondence between the Masked Language Model (MLM) objective
and existing methods for learning statistical dependencies in graphical models.
Using this, we derive a method for extracting these learned statistical
dependencies in MLMs and show that these dependencies encode useful inductive
biases in the form of syntactic structures. In an unsupervised parsing
evaluation, simply forming a minimum spanning tree on the implied statistical
dependence structure outperforms a classic method for unsupervised parsing
(58.74 vs. 55.91 UUAS).
| 2,021 |
Computation and Language
|
Joint Universal Syntactic and Semantic Parsing
|
While numerous attempts have been made to jointly parse syntax and semantics,
high performance in one domain typically comes at the price of performance in
the other. This trade-off contradicts the large body of research focusing on
the rich interactions at the syntax-semantics interface. We explore multiple
model architectures which allow us to exploit the rich syntactic and semantic
annotations contained in the Universal Decompositional Semantics (UDS) dataset,
jointly parsing Universal Dependencies and UDS to obtain state-of-the-art
results in both formalisms. We analyze the behaviour of a joint model of syntax
and semantics, finding patterns supported by linguistic theory at the
syntax-semantics interface. We then investigate to what degree joint modeling
generalizes to a multilingual setting, where we find similar trends across 8
languages.
| 2,021 |
Computation and Language
|
Macro-Average: Rare Types Are Important Too
|
While traditional corpus-level evaluation metrics for machine translation
(MT) correlate well with fluency, they struggle to reflect adequacy.
Model-based MT metrics trained on segment-level human judgments have emerged as
an attractive replacement due to strong correlation results. These models,
however, require potentially expensive re-training for new domains and
languages. Furthermore, their decisions are inherently non-transparent and
appear to reflect unwelcome biases. We explore the simple type-based classifier
metric, MacroF1, and study its applicability to MT evaluation. We find that
MacroF1 is competitive on direct assessment, and outperforms others in
indicating downstream cross-lingual information retrieval task performance.
Further, we show that MacroF1 can be used to effectively compare supervised and
unsupervised neural machine translation, and reveal significant qualitative
differences in the methods' outputs.
| 2,021 |
Computation and Language
|
A Replication Study of Dense Passage Retriever
|
Text retrieval using learned dense representations has recently emerged as a
promising alternative to "traditional" text retrieval using sparse bag-of-words
representations. One recent work that has garnered much attention is the dense
passage retriever (DPR) technique proposed by Karpukhin et al. (2020) for
end-to-end open-domain question answering. We present a replication study of
this work, starting with model checkpoints provided by the authors, but
otherwise from an independent implementation in our group's Pyserini IR toolkit
and PyGaggle neural text ranking library. Although our experimental results
largely verify the claims of the original paper, we arrived at two important
additional findings that contribute to a better understanding of DPR: First, it
appears that the original authors under-report the effectiveness of the BM25
baseline and hence also dense--sparse hybrid retrieval results. Second, by
incorporating evidence from the retriever and an improved answer span scoring
technique, we are able to improve end-to-end question answering effectiveness
using exactly the same models as in the original work.
| 2,021 |
Computation and Language
|
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