Titles
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Extremely low-resource machine translation for closely related languages
|
An effective method to improve extremely low-resource neural machine
translation is multilingual training, which can be improved by leveraging
monolingual data to create synthetic bilingual corpora using the
back-translation method. This work focuses on closely related languages from
the Uralic language family: from Estonian and Finnish geographical regions. We
find that multilingual learning and synthetic corpora increase the translation
quality in every language pair for which we have data. We show that transfer
learning and fine-tuning are very effective for doing low-resource machine
translation and achieve the best results. We collected new parallel data for
V\~oro, North and South Saami and present first results of neural machine
translation for these languages.
| 2,021 |
Computation and Language
|
TranSmart: A Practical Interactive Machine Translation System
|
Automatic machine translation is super efficient to produce translations yet
their quality is not guaranteed. This technique report introduces TranSmart, a
practical human-machine interactive translation system that is able to trade
off translation quality and efficiency. Compared to existing publicly available
interactive translation systems, TranSmart supports three key features,
word-level autocompletion, sentence-level autocompletion and translation
memory. By word-level and sentence-level autocompletion, TranSmart allows users
to interactively translate words in their own manners rather than the strict
manner from left to right. In addition, TranSmart has the potential to avoid
similar translation mistakes by using translated sentences in history as its
memory. This report presents major functions of TranSmart, algorithms for
achieving these functions, how to use the TranSmart APIs, and evaluation
results of some key functions. TranSmart is publicly available at its homepage
(https://transmart.qq.com).
| 2,021 |
Computation and Language
|
Maria: A Visual Experience Powered Conversational Agent
|
Arguably, the visual perception of conversational agents to the physical
world is a key way for them to exhibit the human-like intelligence.
Image-grounded conversation is thus proposed to address this challenge.
Existing works focus on exploring the multimodal dialog models that ground the
conversation on a given image. In this paper, we take a step further to study
image-grounded conversation under a fully open-ended setting where no paired
dialog and image are assumed available. Specifically, we present Maria, a
neural conversation agent powered by the visual world experiences which are
retrieved from a large-scale image index. Maria consists of three flexible
components, i.e., text-to-image retriever, visual concept detector and
visual-knowledge-grounded response generator. The retriever aims to retrieve a
correlated image to the dialog from an image index, while the visual concept
detector extracts rich visual knowledge from the image. Then, the response
generator is grounded on the extracted visual knowledge and dialog context to
generate the target response. Extensive experiments demonstrate Maria
outperforms previous state-of-the-art methods on automatic metrics and human
evaluation, and can generate informative responses that have some visual
commonsense of the physical world.
| 2,021 |
Computation and Language
|
Self-Supervised Multimodal Opinion Summarization
|
Recently, opinion summarization, which is the generation of a summary from
multiple reviews, has been conducted in a self-supervised manner by considering
a sampled review as a pseudo summary. However, non-text data such as image and
metadata related to reviews have been considered less often. To use the
abundant information contained in non-text data, we propose a self-supervised
multimodal opinion summarization framework called MultimodalSum. Our framework
obtains a representation of each modality using a separate encoder for each
modality, and the text decoder generates a summary. To resolve the inherent
heterogeneity of multimodal data, we propose a multimodal training pipeline. We
first pretrain the text encoder--decoder based solely on text modality data.
Subsequently, we pretrain the non-text modality encoders by considering the
pretrained text decoder as a pivot for the homogeneous representation of
multimodal data. Finally, to fuse multimodal representations, we train the
entire framework in an end-to-end manner. We demonstrate the superiority of
MultimodalSum by conducting experiments on Yelp and Amazon datasets.
| 2,021 |
Computation and Language
|
Neural Entity Recognition with Gazetteer based Fusion
|
Incorporating external knowledge into Named Entity Recognition (NER) systems
has been widely studied in the generic domain. In this paper, we focus on
clinical domain where only limited data is accessible and interpretability is
important. Recent advancement in technology and the acceleration of clinical
trials has resulted in the discovery of new drugs, procedures as well as
medical conditions. These factors motivate towards building robust zero-shot
NER systems which can quickly adapt to new medical terminology. We propose an
auxiliary gazetteer model and fuse it with an NER system, which results in
better robustness and interpretability across different clinical datasets. Our
gazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains
on the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on
novel entity mentions never presented during training. Moreover, our fusion
model is able to quickly adapt to new mentions in gazetteers without
re-training and the gains from the proposed fusion model are transferable to
related datasets.
| 2,021 |
Computation and Language
|
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
|
Finding codes given natural language query isb eneficial to the productivity
of software developers. Future progress towards better semantic matching
between query and code requires richer supervised training resources. To remedy
this, we introduce the CoSQA dataset.It includes 20,604 labels for pairs of
natural language queries and codes, each annotated by at least 3 human
annotators. We further introduce a contrastive learning method dubbed CoCLR to
enhance query-code matching, which works as a data augmenter to bring more
artificially generated training instances. We show that evaluated on CodeXGLUE
with the same CodeBERT model, training on CoSQA improves the accuracy of code
question answering by 5.1%, and incorporating CoCLR brings a further
improvement of 10.5%.
| 2,021 |
Computation and Language
|
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical
Core-Fringe Approach
|
We propose to measure fine-grained domain relevance - the degree that a term
is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning)
domain. Such measurement is crucial for many downstream tasks in natural
language processing. To handle long-tail terms, we build a core-anchored
semantic graph, which uses core terms with rich description information to
bridge the vast remaining fringe terms semantically. To support a fine-grained
domain without relying on a matching corpus for supervision, we develop
hierarchical core-fringe learning, which learns core and fringe terms jointly
in a semi-supervised manner contextualized in the hierarchy of the domain. To
reduce expensive human efforts, we employ automatic annotation and hierarchical
positive-unlabeled learning. Our approach applies to big or small domains,
covers head or tail terms, and requires little human effort. Extensive
experiments demonstrate that our methods outperform strong baselines and even
surpass professional human performance.
| 2,021 |
Computation and Language
|
RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical
Resource for English)
|
Most words are ambiguous--i.e., they convey distinct meanings in different
contexts--and even the meanings of unambiguous words are context-dependent.
Both phenomena present a challenge for NLP. Recently, the advent of
contextualized word embeddings has led to success on tasks involving lexical
ambiguity, such as Word Sense Disambiguation. However, there are few tasks that
directly evaluate how well these contextualized embeddings accommodate the more
continuous, dynamic nature of word meaning--particularly in a way that matches
human intuitions. We introduce RAW-C, a dataset of graded, human relatedness
judgments for 112 ambiguous words in context (with 672 sentence pairs total),
as well as human estimates of sense dominance. The average inter-annotator
agreement (assessed using a leave-one-annotator-out method) was 0.79. We then
show that a measure of cosine distance, computed using contextualized
embeddings from BERT and ELMo, correlates with human judgments, but that cosine
distance also systematically underestimates how similar humans find uses of the
same sense of a word to be, and systematically overestimates how similar humans
find uses of different-sense homonyms. Finally, we propose a synthesis between
psycholinguistic theories of the mental lexicon and computational models of
lexical semantics.
| 2,021 |
Computation and Language
|
Synthetic Data Generation for Grammatical Error Correction with Tagged
Corruption Models
|
Synthetic data generation is widely known to boost the accuracy of neural
grammatical error correction (GEC) systems, but existing methods often lack
diversity or are too simplistic to generate the broad range of grammatical
errors made by human writers. In this work, we use error type tags from
automatic annotation tools such as ERRANT to guide synthetic data generation.
We compare several models that can produce an ungrammatical sentence given a
clean sentence and an error type tag. We use these models to build a new, large
synthetic pre-training data set with error tag frequency distributions matching
a given development set. Our synthetic data set yields large and consistent
gains, improving the state-of-the-art on the BEA-19 and CoNLL-14 test sets. We
also show that our approach is particularly effective in adapting a GEC system,
trained on mixed native and non-native English, to a native English test set,
even surpassing real training data consisting of high-quality sentence pairs.
| 2,021 |
Computation and Language
|
Generative Adversarial Imitation Learning for Empathy-based AI
|
Generative adversarial imitation learning (GAIL) is a model-free algorithm
that has been shown to provide strong results in imitating complex behaviors in
high-dimensional environments. In this paper, we utilize the GAIL model for
text generation to develop empathy-based context-aware conversational AI. Our
model uses an expert trajectory of empathetic prompt-response dialogues which
can accurately exhibit the correct empathetic emotion when generating a
response. The Generator of the GAIL model uses the GPT-2 sequential pre-trained
language model trained on 117 million parameters from 40 GB of internet data.
We propose a novel application of an approach used in transfer learning to fine
tune the GPT-2 model in order to generate concise, user-specific empathetic
responses validated against the Discriminator. Our novel GAIL model utilizes a
sentiment analysis history-based reinforcement learning approach to
empathetically respond to human interactions in a personalized manner. We find
that our model's response scores on various human-generated prompts collected
from the Facebook Empathetic Dialogues dataset outperform baseline
counterparts. Moreover, our model improves upon various history-based
conversational AI models developed recently, as our model's performance over a
sustained conversation of 3 or more interactions outperform similar
conversational AI models.
| 2,021 |
Computation and Language
|
Online Learning Meets Machine Translation Evaluation: Finding the Best
Systems with the Least Human Effort
|
In Machine Translation, assessing the quality of a large amount of automatic
translations can be challenging. Automatic metrics are not reliable when it
comes to high performing systems. In addition, resorting to human evaluators
can be expensive, especially when evaluating multiple systems. To overcome the
latter challenge, we propose a novel application of online learning that, given
an ensemble of Machine Translation systems, dynamically converges to the best
systems, by taking advantage of the human feedback available. Our experiments
on WMT'19 datasets show that our online approach quickly converges to the top-3
ranked systems for the language pairs considered, despite the lack of human
feedback for many translations.
| 2,021 |
Computation and Language
|
Relational Gating for "What If" Reasoning
|
This paper addresses the challenge of learning to do procedural reasoning
over text to answer "What if..." questions. We propose a novel relational
gating network that learns to filter the key entities and relationships and
learns contextual and cross representations of both procedure and question for
finding the answer. Our relational gating network contains an entity gating
module, relation gating module, and contextual interaction module. These
modules help in solving the "What if..." reasoning problem. We show that
modeling pairwise relationships helps to capture higher-order relations and
find the line of reasoning for causes and effects in the procedural
descriptions. Our proposed approach achieves the state-of-the-art results on
the WIQA dataset.
| 2,021 |
Computation and Language
|
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced
Collective Inference
|
Compared to the general news domain, information extraction (IE) from
biomedical text requires much broader domain knowledge. However, many previous
IE methods do not utilize any external knowledge during inference. Due to the
exponential growth of biomedical publications, models that do not go beyond
their fixed set of parameters will likely fall behind. Inspired by how humans
look up relevant information to comprehend a scientific text, we present a
novel framework that utilizes external knowledge for joint entity and relation
extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input
text, KECI first constructs an initial span graph representing its initial
understanding of the text. It then uses an entity linker to form a knowledge
graph containing relevant background knowledge for the the entity mentions in
the text. To make the final predictions, KECI fuses the initial span graph and
the knowledge graph into a more refined graph using an attention mechanism.
KECI takes a collective approach to link mention spans to entities by
integrating global relational information into local representations using
graph convolutional networks. Our experimental results show that the framework
is highly effective, achieving new state-of-the-art results in two different
benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse
drug event extraction). For example, KECI achieves absolute improvements of
4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity
and relation extraction tasks.
| 2,021 |
Computation and Language
|
Verb Sense Clustering using Contextualized Word Representations for
Semantic Frame Induction
|
Contextualized word representations have proven useful for various natural
language processing tasks. However, it remains unclear to what extent these
representations can cover hand-coded semantic information such as semantic
frames, which specify the semantic role of the arguments associated with a
predicate. In this paper, we focus on verbs that evoke different frames
depending on the context, and we investigate how well contextualized word
representations can recognize the difference of frames that the same verb
evokes. We also explore which types of representation are suitable for semantic
frame induction. In our experiments, we compare seven different contextualized
word representations for two English frame-semantic resources, FrameNet and
PropBank. We demonstrate that several contextualized word representations,
especially BERT and its variants, are considerably informative for semantic
frame induction. Furthermore, we examine the extent to which the contextualized
representation of a verb can estimate the number of frames that the verb can
evoke.
| 2,021 |
Computation and Language
|
Semantic Frame Induction using Masked Word Embeddings and Two-Step
Clustering
|
Recent studies on semantic frame induction show that relatively high
performance has been achieved by using clustering-based methods with
contextualized word embeddings. However, there are two potential drawbacks to
these methods: one is that they focus too much on the superficial information
of the frame-evoking verb and the other is that they tend to divide the
instances of the same verb into too many different frame clusters. To overcome
these drawbacks, we propose a semantic frame induction method using masked word
embeddings and two-step clustering. Through experiments on the English FrameNet
data, we demonstrate that using the masked word embeddings is effective for
avoiding too much reliance on the surface information of frame-evoking verbs
and that two-step clustering can improve the number of resulting frame clusters
for the instances of the same verb.
| 2,021 |
Computation and Language
|
Leveraging Linguistic Coordination in Reranking N-Best Candidates For
End-to-End Response Selection Using BERT
|
Retrieval-based dialogue systems select the best response from many
candidates. Although many state-of-the-art models have shown promising
performance in dialogue response selection tasks, there is still quite a gap
between R@1 and R@10 performance. To address this, we propose to leverage
linguistic coordination (a phenomenon that individuals tend to develop similar
linguistic behaviors in conversation) to rerank the N-best candidates produced
by BERT, a state-of-the-art pre-trained language model. Our results show an
improvement in R@1 compared to BERT baselines, demonstrating the utility of
repairing machine-generated outputs by leveraging a linguistic theory.
| 2,021 |
Computation and Language
|
Diagnosing Transformers in Task-Oriented Semantic Parsing
|
Modern task-oriented semantic parsing approaches typically use seq2seq
transformers to map textual utterances to semantic frames comprised of intents
and slots. While these models are empirically strong, their specific strengths
and weaknesses have largely remained unexplored. In this work, we study BART
and XLM-R, two state-of-the-art parsers, across both monolingual and
multilingual settings. Our experiments yield several key results:
transformer-based parsers struggle not only with disambiguating intents/slots,
but surprisingly also with producing syntactically-valid frames. Though
pre-training imbues transformers with syntactic inductive biases, we find the
ambiguity of copying utterance spans into frames often leads to tree
invalidity, indicating span extraction is a major bottleneck for current
parsers. However, as a silver lining, we show transformer-based parsers give
sufficient indicators for whether a frame is likely to be correct or incorrect,
making them easier to deploy in production settings.
| 2,021 |
Computation and Language
|
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment
Prediction and Explanation
|
An automated system that could assist a judge in predicting the outcome of a
case would help expedite the judicial process. For such a system to be
practically useful, predictions by the system should be explainable. To promote
research in developing such a system, we introduce ILDC (Indian Legal Documents
Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated
with original court decisions. A portion of the corpus (a separate test set) is
annotated with gold standard explanations by legal experts. Based on ILDC, we
propose the task of Court Judgment Prediction and Explanation (CJPE). The task
requires an automated system to predict an explainable outcome of a case. We
experiment with a battery of baseline models for case predictions and propose a
hierarchical occlusion based model for explainability. Our best prediction
model has an accuracy of 78% versus 94% for human legal experts, pointing
towards the complexity of the prediction task. The analysis of explanations by
the proposed algorithm reveals a significant difference in the point of view of
the algorithm and legal experts for explaining the judgments, pointing towards
scope for future research.
| 2,021 |
Computation and Language
|
Hierarchical Transformer Encoders for Vietnamese Spelling Correction
|
In this paper, we propose a Hierarchical Transformer model for Vietnamese
spelling correction problem. The model consists of multiple Transformer
encoders and utilizes both character-level and word-level to detect errors and
make corrections. In addition, to facilitate future work in Vietnamese spelling
correction tasks, we propose a realistic dataset collected from real-life texts
for the problem. We compare our method with other methods and publicly
available systems. The proposed method outperforms all of the contemporary
methods in terms of recall, precision, and f1-score. A demo version is publicly
available.
| 2,021 |
Computation and Language
|
Alleviating the Knowledge-Language Inconsistency: A Study for Deep
Commonsense Knowledge
|
Knowledge facts are typically represented by relational triples, while we
observe that some commonsense facts are represented by the triples whose forms
are inconsistent with the expression of language. This inconsistency puts
forward a challenge for pre-trained language models to deal with these
commonsense knowledge facts. In this paper, we term such knowledge as deep
commonsense knowledge and conduct extensive exploratory experiments on it. We
show that deep commonsense knowledge occupies a significant part of commonsense
knowledge while conventional methods fail to capture it effectively. We further
propose a novel method to mine the deep commonsense knowledge distributed in
sentences, alleviating the reliance of conventional methods on the triple
representation form of knowledge. Experiments demonstrate that the proposal
significantly improves the performance in mining deep commonsense knowledge.
| 2,021 |
Computation and Language
|
Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data
Augmentation via MiniMax
|
In Natural Language Processing (NLP), finding data augmentation techniques
that can produce high-quality human-interpretable examples has always been
challenging. Recently, leveraging kNN such that augmented examples are
retrieved from large repositories of unlabelled sentences has made a step
toward interpretable augmentation. Inspired by this paradigm, we introduce
Minimax-kNN, a sample efficient data augmentation strategy tailored for
Knowledge Distillation (KD). We exploit a semi-supervised approach based on KD
to train a model on augmented data. In contrast to existing kNN augmentation
techniques that blindly incorporate all samples, our method dynamically selects
a subset of augmented samples that maximizes KL-divergence between the teacher
and student models. This step aims to extract the most efficient samples to
ensure our augmented data covers regions in the input space with maximum loss
value. We evaluated our technique on several text classification tasks and
demonstrated that Minimax-kNN consistently outperforms strong baselines. Our
results show that Minimax-kNN requires fewer augmented examples and less
computation to achieve superior performance over the state-of-the-art kNN-based
augmentation techniques.
| 2,021 |
Computation and Language
|
ByT5: Towards a token-free future with pre-trained byte-to-byte models
|
Most widely-used pre-trained language models operate on sequences of tokens
corresponding to word or subword units. By comparison, token-free models that
operate directly on raw text (bytes or characters) have many benefits: they can
process text in any language out of the box, they are more robust to noise, and
they minimize technical debt by removing complex and error-prone text
preprocessing pipelines. Since byte or character sequences are longer than
token sequences, past work on token-free models has often introduced new model
architectures designed to amortize the cost of operating directly on raw text.
In this paper, we show that a standard Transformer architecture can be used
with minimal modifications to process byte sequences. We characterize the
trade-offs in terms of parameter count, training FLOPs, and inference speed,
and show that byte-level models are competitive with their token-level
counterparts. We also demonstrate that byte-level models are significantly more
robust to noise and perform better on tasks that are sensitive to spelling and
pronunciation. As part of our contribution, we release a new set of pre-trained
byte-level Transformer models based on the T5 architecture, as well as all code
and data used in our experiments.
| 2,022 |
Computation and Language
|
THINK: A Novel Conversation Model for Generating Grammatically Correct
and Coherent Responses
|
Many existing conversation models that are based on the encoder-decoder
framework have focused on ways to make the encoder more complicated to enrich
the context vectors so as to increase the diversity and informativeness of
generated responses. However, these approaches face two problems. First, the
decoder is too simple to effectively utilize the previously generated
information and tends to generate duplicated and self-contradicting responses.
Second, the complex encoder tends to generate diverse but incoherent responses
because the complex context vectors may deviate from the original semantics of
context. In this work, we proposed a conversation model named "THINK" (Teamwork
generation Hover around Impressive Noticeable Keywords) to make the decoder
more complicated and avoid generating duplicated and self-contradicting
responses. The model simplifies the context vectors and increases the coherence
of generated responses in a reasonable way. For this model, we propose Teamwork
generation framework and Semantics Extractor. Compared with other baselines,
both automatic and human evaluation showed the advantages of our model.
| 2,021 |
Computation and Language
|
Noised Consistency Training for Text Summarization
|
Neural abstractive summarization methods often require large quantities of
labeled training data. However, labeling large amounts of summarization data is
often prohibitive due to time, financial, and expertise constraints, which has
limited the usefulness of summarization systems to practical applications. In
this paper, we argue that this limitation can be overcome by a semi-supervised
approach: consistency training which is to leverage large amounts of unlabeled
data to improve the performance of supervised learning over a small corpus. The
consistency regularization semi-supervised learning can regularize model
predictions to be invariant to small noise applied to input articles. By adding
noised unlabeled corpus to help regularize consistency training, this framework
obtains comparative performance without using the full dataset. In particular,
we have verified that leveraging large amounts of unlabeled data decently
improves the performance of supervised learning over an insufficient labeled
dataset.
| 2,022 |
Computation and Language
|
Cross-Lingual Abstractive Summarization with Limited Parallel Resources
|
Parallel cross-lingual summarization data is scarce, requiring models to
better use the limited available cross-lingual resources. Existing methods to
do so often adopt sequence-to-sequence networks with multi-task frameworks.
Such approaches apply multiple decoders, each of which is utilized for a
specific task. However, these independent decoders share no parameters, hence
fail to capture the relationships between the discrete phrases of summaries in
different languages, breaking the connections in order to transfer the
knowledge of the high-resource languages to low-resource languages. To bridge
these connections, we propose a novel Multi-Task framework for Cross-Lingual
Abstractive Summarization (MCLAS) in a low-resource setting. Employing one
unified decoder to generate the sequential concatenation of monolingual and
cross-lingual summaries, MCLAS makes the monolingual summarization task a
prerequisite of the cross-lingual summarization (CLS) task. In this way, the
shared decoder learns interactions involving alignments and summary patterns
across languages, which encourages attaining knowledge transfer. Experiments on
two CLS datasets demonstrate that our model significantly outperforms three
baseline models in both low-resource and full-dataset scenarios. Moreover,
in-depth analysis on the generated summaries and attention heads verifies that
interactions are learned well using MCLAS, which benefits the CLS task under
limited parallel resources.
| 2,021 |
Computation and Language
|
Data Augmentation for Text Generation Without Any Augmented Data
|
Data augmentation is an effective way to improve the performance of many
neural text generation models. However, current data augmentation methods need
to define or choose proper data mapping functions that map the original samples
into the augmented samples. In this work, we derive an objective to formulate
the problem of data augmentation on text generation tasks without any use of
augmented data constructed by specific mapping functions. Our proposed
objective can be efficiently optimized and applied to popular loss functions on
text generation tasks with a convergence rate guarantee. Experiments on five
datasets of two text generation tasks show that our approach can approximate or
even surpass popular data augmentation methods.
| 2,021 |
Computation and Language
|
Domain-Adaptive Pretraining Methods for Dialogue Understanding
|
Language models like BERT and SpanBERT pretrained on open-domain data have
obtained impressive gains on various NLP tasks. In this paper, we probe the
effectiveness of domain-adaptive pretraining objectives on downstream tasks. In
particular, three objectives, including a novel objective focusing on modeling
predicate-argument relations, are evaluated on two challenging dialogue
understanding tasks. Experimental results demonstrate that domain-adaptive
pretraining with proper objectives can significantly improve the performance of
a strong baseline on these tasks, achieving the new state-of-the-art
performances.
| 2,021 |
Computation and Language
|
Natural Language Processing 4 All (NLP4All): A New Online Platform for
Teaching and Learning NLP Concepts
|
Natural Language Processing offers new insights into language data across
almost all disciplines and domains, and allows us to corroborate and/or
challenge existing knowledge. The primary hurdles to widening participation in
and use of these new research tools are, first, a lack of coding skills in
students across K-16, and in the population at large, and second, a lack of
knowledge of how NLP-methods can be used to answer questions of disciplinary
interest outside of linguistics and/or computer science. To broaden
participation in NLP and improve NLP-literacy, we introduced a new tool
web-based tool called Natural Language Processing 4 All (NLP4All). The intended
purpose of NLP4All is to help teachers facilitate learning with and about NLP,
by providing easy-to-use interfaces to NLP-methods, data, and analyses, making
it possible for non- and novice-programmers to learn NLP concepts
interactively.
| 2,021 |
Computation and Language
|
OTTers: One-turn Topic Transitions for Open-Domain Dialogue
|
Mixed initiative in open-domain dialogue requires a system to pro-actively
introduce new topics. The one-turn topic transition task explores how a system
connects two topics in a cooperative and coherent manner. The goal of the task
is to generate a "bridging" utterance connecting the new topic to the topic of
the previous conversation turn. We are especially interested in commonsense
explanations of how a new topic relates to what has been mentioned before. We
first collect a new dataset of human one-turn topic transitions, which we call
OTTers. We then explore different strategies used by humans when asked to
complete such a task, and notice that the use of a bridging utterance to
connect the two topics is the approach used the most. We finally show how
existing state-of-the-art text generation models can be adapted to this task
and examine the performance of these baselines on different splits of the
OTTers data.
| 2,021 |
Computation and Language
|
An Explanatory Query-Based Framework for Exploring Academic Expertise
|
The success of research institutions heavily relies upon identifying the
right researchers "for the job": researchers may need to identify appropriate
collaborators, often from across disciplines; students may need to identify
suitable supervisors for projects of their interest; administrators may need to
match funding opportunities with relevant researchers, and so on. Usually,
finding potential collaborators in institutions is a time-consuming manual
search task prone to bias. In this paper, we propose a novel query-based
framework for searching, scoring, and exploring research expertise
automatically, based upon processing abstracts of academic publications. Given
user queries in natural language, our framework finds researchers with relevant
expertise, making use of domain-specific knowledge bases and word embeddings.
It also generates explanations for its recommendations. We evaluate our
framework with an institutional repository of papers from a leading university,
using, as baselines, artificial neural networks and transformer-based models
for a multilabel classification task to identify authors of publication
abstracts. We also assess the cross-domain effectiveness of our framework with
a (separate) research funding repository for the same institution. We show that
our simple method is effective in identifying matches, while satisfying
desirable properties and being efficient.
| 2,021 |
Computation and Language
|
How to Split: the Effect of Word Segmentation on Gender Bias in Speech
Translation
|
Having recognized gender bias as a major issue affecting current translation
technologies, researchers have primarily attempted to mitigate it by working on
the data front. However, whether algorithmic aspects concur to exacerbate
unwanted outputs remains so far under-investigated. In this work, we bring the
analysis on gender bias in automatic translation onto a seemingly neutral yet
critical component: word segmentation. Can segmenting methods influence the
ability to translate gender? Do certain segmentation approaches penalize the
representation of feminine linguistic markings? We address these questions by
comparing 5 existing segmentation strategies on the target side of speech
translation systems. Our results on two language pairs (English-Italian/French)
show that state-of-the-art sub-word splitting (BPE) comes at the cost of higher
gender bias. In light of this finding, we propose a combined approach that
preserves BPE overall translation quality, while leveraging the higher ability
of character-based segmentation to properly translate gender.
| 2,021 |
Computation and Language
|
Early Exiting with Ensemble Internal Classifiers
|
As a simple technique to accelerate inference of large-scale pre-trained
models, early exiting has gained much attention in the NLP community. It allows
samples to exit early at internal classifiers without passing through the
entire model. Most existing work usually trains the internal classifiers
independently and employs an exiting strategy to decide whether or not to exit
based on the confidence of the current internal classifier. However, none of
these works takes full advantage of the fact that the internal classifiers are
trained to solve the same task therefore can be used to construct an ensemble.
In this paper, we show that a novel objective function for the training of the
ensemble internal classifiers can be naturally induced from the perspective of
ensemble learning and information theory. The proposed training objective
consists of two terms: one for accuracy and the other for the diversity of the
internal classifiers. In contrast, the objective used in prior work is exactly
the accuracy term of our training objective therefore only optimizes the
accuracy but not diversity. Further, we propose a simple voting-based strategy
that considers predictions of all the past internal classifiers to infer the
correct label and decide whether to exit. Experimental results on various NLP
tasks show that our proposed objective function and voting-based strategy can
achieve better accuracy-speed trade-offs.
| 2,021 |
Computation and Language
|
Language Models Use Monotonicity to Assess NPI Licensing
|
We investigate the semantic knowledge of language models (LMs), focusing on
(1) whether these LMs create categories of linguistic environments based on
their semantic monotonicity properties, and (2) whether these categories play a
similar role in LMs as in human language understanding, using negative polarity
item licensing as a case study. We introduce a series of experiments consisting
of probing with diagnostic classifiers (DCs), linguistic acceptability tasks,
as well as a novel DC ranking method that tightly connects the probing results
to the inner workings of the LM. By applying our experimental pipeline to LMs
trained on various filtered corpora, we are able to gain stronger insights into
the semantic generalizations that are acquired by these models.
| 2,021 |
Computation and Language
|
Lightweight Cross-Lingual Sentence Representation Learning
|
Large-scale models for learning fixed-dimensional cross-lingual sentence
representations like LASER (Artetxe and Schwenk, 2019b) lead to significant
improvement in performance on downstream tasks. However, further increases and
modifications based on such large-scale models are usually impractical due to
memory limitations. In this work, we introduce a lightweight dual-transformer
architecture with just 2 layers for generating memory-efficient cross-lingual
sentence representations. We explore different training tasks and observe that
current cross-lingual training tasks leave a lot to be desired for this shallow
architecture. To ameliorate this, we propose a novel cross-lingual language
model, which combines the existing single-word masked language model with the
newly proposed cross-lingual token-level reconstruction task. We further
augment the training task by the introduction of two computationally-lite
sentence-level contrastive learning tasks to enhance the alignment of
cross-lingual sentence representation space, which compensates for the learning
bottleneck of the lightweight transformer for generative tasks. Our comparisons
with competing models on cross-lingual sentence retrieval and multilingual
document classification confirm the effectiveness of the newly proposed
training tasks for a shallow model.
| 2,022 |
Computation and Language
|
Learning Approximate and Exact Numeral Systems via Reinforcement
Learning
|
Recent work (Xu et al., 2020) has suggested that numeral systems in different
languages are shaped by a functional need for efficient communication in an
information-theoretic sense. Here we take a learning-theoretic approach and
show how efficient communication emerges via reinforcement learning. In our
framework, two artificial agents play a Lewis signaling game where the goal is
to convey a numeral concept. The agents gradually learn to communicate using
reinforcement learning and the resulting numeral systems are shown to be
efficient in the information-theoretic framework of Regier et al. (2015);
Gibson et al. (2017). They are also shown to be similar to human numeral
systems of same type. Our results thus provide a mechanistic explanation via
reinforcement learning of the recent results in Xu et al. (2020) and can
potentially be generalized to other semantic domains.
| 2,021 |
Computation and Language
|
Learning Relation Alignment for Calibrated Cross-modal Retrieval
|
Despite the achievements of large-scale multimodal pre-training approaches,
cross-modal retrieval, e.g., image-text retrieval, remains a challenging task.
To bridge the semantic gap between the two modalities, previous studies mainly
focus on word-region alignment at the object level, lacking the matching
between the linguistic relation among the words and the visual relation among
the regions. The neglect of such relation consistency impairs the
contextualized representation of image-text pairs and hinders the model
performance and the interpretability. In this paper, we first propose a novel
metric, Intra-modal Self-attention Distance (ISD), to quantify the relation
consistency by measuring the semantic distance between linguistic and visual
relations. In response, we present Inter-modal Alignment on Intra-modal
Self-attentions (IAIS), a regularized training method to optimize the ISD and
calibrate intra-modal self-attentions from the two modalities mutually via
inter-modal alignment. The IAIS regularizer boosts the performance of
prevailing models on Flickr30k and MS COCO datasets by a considerable margin,
which demonstrates the superiority of our approach.
| 2,021 |
Computation and Language
|
Accelerating BERT Inference for Sequence Labeling via Early-Exit
|
Both performance and efficiency are crucial factors for sequence labeling
tasks in many real-world scenarios. Although the pre-trained models (PTMs) have
significantly improved the performance of various sequence labeling tasks,
their computational cost is expensive. To alleviate this problem, we extend the
recent successful early-exit mechanism to accelerate the inference of PTMs for
sequence labeling tasks. However, existing early-exit mechanisms are
specifically designed for sequence-level tasks, rather than sequence labeling.
In this paper, we first propose a simple extension of sentence-level early-exit
for sequence labeling tasks. To further reduce the computational cost, we also
propose a token-level early-exit mechanism that allows partial tokens to exit
early at different layers. Considering the local dependency inherent in
sequence labeling, we employed a window-based criterion to decide for a token
whether or not to exit. The token-level early-exit brings the gap between
training and inference, so we introduce an extra self-sampling fine-tuning
stage to alleviate it. The extensive experiments on three popular sequence
labeling tasks show that our approach can save up to 66%-75% inference cost
with minimal performance degradation. Compared with competitive compressed
models such as DistilBERT, our approach can achieve better performance under
the same speed-up ratios of 2X, 3X, and 4X.
| 2,021 |
Computation and Language
|
Knowledge Inheritance for Pre-trained Language Models
|
Recent explorations of large-scale pre-trained language models (PLMs) have
revealed the power of PLMs with huge amounts of parameters, setting off a wave
of training ever-larger PLMs. However, it requires tremendous computational
resources to train a large-scale PLM, which may be practically unaffordable. In
addition, existing large-scale PLMs are mainly trained from scratch
individually, ignoring that many well-trained PLMs are available. To this end,
we explore the question how could existing PLMs benefit training large-scale
PLMs in future. Specifically, we introduce a pre-training framework named
"knowledge inheritance" (KI) and explore how could knowledge distillation serve
as auxiliary supervision during pre-training to efficiently learn larger PLMs.
Experimental results demonstrate the superiority of KI in training efficiency.
We also conduct empirical analyses to explore the effects of teacher PLMs'
pre-training settings, including model architecture, pre-training data, etc.
Finally, we show that KI could be applied to domain adaptation and knowledge
transfer.
| 2,022 |
Computation and Language
|
Changing the World by Changing the Data
|
NLP community is currently investing a lot more research and resources into
development of deep learning models than training data. While we have made a
lot of progress, it is now clear that our models learn all kinds of spurious
patterns, social biases, and annotation artifacts. Algorithmic solutions have
so far had limited success. An alternative that is being actively discussed is
more careful design of datasets so as to deliver specific signals. This
position paper maps out the arguments for and against data curation, and argues
that fundamentally the point is moot: curation already is and will be
happening, and it is changing the world. The question is only how much thought
we want to invest into that process.
| 2,021 |
Computation and Language
|
Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for
Multiple Toxic Span Extraction from Online Comments
|
Social network platforms are generally used to share positive, constructive,
and insightful content. However, in recent times, people often get exposed to
objectionable content like threat, identity attacks, hate speech, insults,
obscene texts, offensive remarks or bullying. Existing work on toxic speech
detection focuses on binary classification or on differentiating toxic speech
among a small set of categories. This paper describes the system proposed by
team Cisco for SemEval-2021 Task 5: Toxic Spans Detection, the first shared
task focusing on detecting the spans in the text that attribute to its
toxicity, in English language. We approach this problem primarily in two ways:
a sequence tagging approach and a dependency parsing approach. In our sequence
tagging approach we tag each token in a sentence under a particular tagging
scheme. Our best performing architecture in this approach also proved to be our
best performing architecture overall with an F1 score of 0.6922, thereby
placing us 7th on the final evaluation phase leaderboard. We also explore a
dependency parsing approach where we extract spans from the input sentence
under the supervision of target span boundaries and rank our spans using a
biaffine model. Finally, we also provide a detailed analysis of our results and
model performance in our paper.
| 2,021 |
Computation and Language
|
SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular
Data in Scientific Documents (SEM-TAB-FACTS)
|
Understanding tables is an important and relevant task that involves
understanding table structure as well as being able to compare and contrast
information within cells. In this paper, we address this challenge by
presenting a new dataset and tasks that addresses this goal in a shared task in
SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in
Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981
manually-generated tables and an auto-generated dataset of 1980 tables
providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS
featured two sub-tasks. In sub-task A, the goal was to determine if a statement
is supported, refuted or unknown in relation to a table. In sub-task B, the
focus was on identifying the specific cells of a table that provide evidence
for the statement. 69 teams signed up to participate in the task with 19
successful submissions to subtask A and 12 successful submissions to subtask B.
We present our results and main findings from the competition.
| 2,021 |
Computation and Language
|
What if This Modified That? Syntactic Interventions via Counterfactual
Embeddings
|
Neural language models exhibit impressive performance on a variety of tasks,
but their internal reasoning may be difficult to understand. Prior art aims to
uncover meaningful properties within model representations via probes, but it
is unclear how faithfully such probes portray information that the models
actually use. To overcome such limitations, we propose a technique, inspired by
causal analysis, for generating counterfactual embeddings within models. In
experiments testing our technique, we produce evidence that suggests some
BERT-based models use a tree-distance-like representation of syntax in
downstream prediction tasks.
| 2,021 |
Computation and Language
|
Feature extraction and evaluation for BioMedical Question Answering
|
In this paper, we present our work on the BioASQ pipeline. The goal is to
answer four types of questions: summary, yes/no, factoids, and list. Our goal
is to empirically evaluate different modules involved: the feature extractor
and the sentence selection block. We used our pipeline to test the
effectiveness of each module for all kinds of question types and perform error
analysis. We defined metrics that are useful for future research related to the
BioASQ pipeline critical to improve the performance of the training pipeline.
| 2,021 |
Computation and Language
|
Controllable Abstractive Dialogue Summarization with Sketch Supervision
|
In this paper, we aim to improve abstractive dialogue summarization quality
and, at the same time, enable granularity control. Our model has two primary
components and stages: 1) a two-stage generation strategy that generates a
preliminary summary sketch serving as the basis for the final summary. This
summary sketch provides a weakly supervised signal in the form of
pseudo-labeled interrogative pronoun categories and key phrases extracted using
a constituency parser. 2) A simple strategy to control the granularity of the
final summary, in that our model can automatically determine or control the
number of generated summary sentences for a given dialogue by predicting and
highlighting different text spans from the source text. Our model achieves
state-of-the-art performance on the largest dialogue summarization corpus
SAMSum, with as high as 50.79 in ROUGE-L score. In addition, we conduct a case
study and show competitive human evaluation results and controllability to
human-annotated summaries.
| 2,021 |
Computation and Language
|
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
|
Identifying and understanding quality phrases from context is a fundamental
task in text mining. The most challenging part of this task arguably lies in
uncommon, emerging, and domain-specific phrases. The infrequent nature of these
phrases significantly hurts the performance of phrase mining methods that rely
on sufficient phrase occurrences in the input corpus. Context-aware tagging
models, though not restricted by frequency, heavily rely on domain experts for
either massive sentence-level gold labels or handcrafted gazetteers. In this
work, we propose UCPhrase, a novel unsupervised context-aware quality phrase
tagger. Specifically, we induce high-quality phrase spans as silver labels from
consistently co-occurring word sequences within each document. Compared with
typical context-agnostic distant supervision based on existing knowledge bases
(KBs), our silver labels root deeply in the input domain and context, thus
having unique advantages in preserving contextual completeness and capturing
emerging, out-of-KB phrases. Training a conventional neural tagger based on
silver labels usually faces the risk of overfitting phrase surface names.
Alternatively, we observe that the contextualized attention maps generated from
a transformer-based neural language model effectively reveal the connections
between words in a surface-agnostic way. Therefore, we pair such attention maps
with the silver labels to train a lightweight span prediction model, which can
be applied to new input to recognize (unseen) quality phrases regardless of
their surface names or frequency. Thorough experiments on various tasks and
datasets, including corpus-level phrase ranking, document-level keyphrase
extraction, and sentence-level phrase tagging, demonstrate the superiority of
our design over state-of-the-art pre-trained, unsupervised, and distantly
supervised methods.
| 2,021 |
Computation and Language
|
Bhasacitra: Visualising the dialect geography of South Asia
|
We present Bhasacitra, a dialect mapping system for South Asia built on a
database of linguistic studies of languages of the region annotated for topic
and location data. We analyse language coverage and look towards applications
to typology by visualising example datasets. The application is not only meant
to be useful for feature mapping, but also serves as a new kind of interactive
bibliography for linguists of South Asian languages.
| 2,023 |
Computation and Language
|
Towards More Equitable Question Answering Systems: How Much More Data Do
You Need?
|
Question answering (QA) in English has been widely explored, but multilingual
datasets are relatively new, with several methods attempting to bridge the gap
between high- and low-resourced languages using data augmentation through
translation and cross-lingual transfer. In this project, we take a step back
and study which approaches allow us to take the most advantage of existing
resources in order to produce QA systems in many languages. Specifically, we
perform extensive analysis to measure the efficacy of few-shot approaches
augmented with automatic translations and permutations of
context-question-answer pairs. In addition, we make suggestions for future
dataset development efforts that make better use of a fixed annotation budget,
with a goal of increasing the language coverage of QA datasets and systems.
Code and data for reproducing our experiments are available here:
https://github.com/NavidRajabi/EMQA.
| 2,021 |
Computation and Language
|
Annotation Inconsistency and Entity Bias in MultiWOZ
|
MultiWOZ is one of the most popular multi-domain task-oriented dialog
datasets, containing 10K+ annotated dialogs covering eight domains. It has been
widely accepted as a benchmark for various dialog tasks, e.g., dialog state
tracking (DST), natural language generation (NLG), and end-to-end (E2E) dialog
modeling. In this work, we identify an overlooked issue with dialog state
annotation inconsistencies in the dataset, where a slot type is tagged
inconsistently across similar dialogs leading to confusion for DST modeling. We
propose an automated correction for this issue, which is present in a whopping
70% of the dialogs. Additionally, we notice that there is significant entity
bias in the dataset (e.g., "cambridge" appears in 50% of the destination cities
in the train domain). The entity bias can potentially lead to named entity
memorization in generative models, which may go unnoticed as the test set
suffers from a similar entity bias as well. We release a new test set with all
entities replaced with unseen entities. Finally, we benchmark joint goal
accuracy (JGA) of the state-of-the-art DST baselines on these modified versions
of the data. Our experiments show that the annotation inconsistency corrections
lead to 7-10% improvement in JGA. On the other hand, we observe a 29% drop in
JGA when models are evaluated on the new test set with unseen entities.
| 2,022 |
Computation and Language
|
NeuralLog: Natural Language Inference with Joint Neural and Logical
Reasoning
|
Deep learning (DL) based language models achieve high performance on various
benchmarks for Natural Language Inference (NLI). And at this time, symbolic
approaches to NLI are receiving less attention. Both approaches (symbolic and
DL) have their advantages and weaknesses. However, currently, no method
combines them in a system to solve the task of NLI. To merge symbolic and deep
learning methods, we propose an inference framework called NeuralLog, which
utilizes both a monotonicity-based logical inference engine and a neural
network language model for phrase alignment. Our framework models the NLI task
as a classic search problem and uses the beam search algorithm to search for
optimal inference paths. Experiments show that our joint logic and neural
inference system improves accuracy on the NLI task and can achieve state-of-art
accuracy on the SICK and MED datasets.
| 2,021 |
Computation and Language
|
Multi-Label Few-Shot Learning for Aspect Category Detection
|
Aspect category detection (ACD) in sentiment analysis aims to identify the
aspect categories mentioned in a sentence. In this paper, we formulate ACD in
the few-shot learning scenario. However, existing few-shot learning approaches
mainly focus on single-label predictions. These methods can not work well for
the ACD task since a sentence may contain multiple aspect categories.
Therefore, we propose a multi-label few-shot learning method based on the
prototypical network. To alleviate the noise, we design two effective attention
mechanisms. The support-set attention aims to extract better prototypes by
removing irrelevant aspects. The query-set attention computes multiple
prototype-specific representations for each query instance, which are then used
to compute accurate distances with the corresponding prototypes. To achieve
multi-label inference, we further learn a dynamic threshold per instance by a
policy network. Extensive experimental results on three datasets demonstrate
that the proposed method significantly outperforms strong baselines.
| 2,021 |
Computation and Language
|
Quotation Recommendation and Interpretation Based on Transformation from
Queries to Quotations
|
To help individuals express themselves better, quotation recommendation is
receiving growing attention. Nevertheless, most prior efforts focus on modeling
quotations and queries separately and ignore the relationship between the
quotations and the queries. In this work, we introduce a transformation matrix
that directly maps the query representations to quotation representations. To
better learn the mapping relationship, we employ a mapping loss that minimizes
the distance of two semantic spaces (one for quotation and another for
mapped-query). Furthermore, we explore using the words in history queries to
interpret the figurative language of quotations, where quotation-aware
attention is applied on top of history queries to highlight the indicator
words. Experiments on two datasets in English and Chinese show that our model
outperforms previous state-of-the-art models.
| 2,021 |
Computation and Language
|
Maintaining Common Ground in Dynamic Environments
|
Common grounding is the process of creating and maintaining mutual
understandings, which is a critical aspect of sophisticated human
communication. While various task settings have been proposed in existing
literature, they mostly focus on creating common ground under static context
and ignore the aspect of maintaining them overtime under dynamic context. In
this work, we propose a novel task setting to study the ability of both
creating and maintaining common ground in dynamic environments. Based on our
minimal task formulation, we collected a large-scale dataset of 5,617 dialogues
to enable fine-grained evaluation and analysis of various dialogue systems.
Through our dataset analyses, we highlight novel challenges introduced in our
setting, such as the usage of complex spatio-temporal expressions to create and
maintain common ground. Finally, we conduct extensive experiments to assess the
capabilities of our baseline dialogue system and discuss future prospects of
our research.
| 2,021 |
Computation and Language
|
Grammatical Error Correction as GAN-like Sequence Labeling
|
In Grammatical Error Correction (GEC), sequence labeling models enjoy fast
inference compared to sequence-to-sequence models; however, inference in
sequence labeling GEC models is an iterative process, as sentences are passed
to the model for multiple rounds of correction, which exposes the model to
sentences with progressively fewer errors at each round. Traditional GEC models
learn from sentences with fixed error rates. Coupling this with the iterative
correction process causes a mismatch between training and inference that
affects final performance. In order to address this mismatch, we propose a
GAN-like sequence labeling model, which consists of a grammatical error
detector as a discriminator and a grammatical error labeler with Gumbel-Softmax
sampling as a generator. By sampling from real error distributions, our errors
are more genuine compared to traditional synthesized GEC errors, thus
alleviating the aforementioned mismatch and allowing for better training. Our
results on several evaluation benchmarks demonstrate that our proposed approach
is effective and improves the previous state-of-the-art baseline.
| 2,021 |
Computation and Language
|
Exploiting Position Bias for Robust Aspect Sentiment Classification
|
Aspect sentiment classification (ASC) aims at determining sentiments
expressed towards different aspects in a sentence. While state-of-the-art ASC
models have achieved remarkable performance, they are recently shown to suffer
from the issue of robustness. Particularly in two common scenarios: when
domains of test and training data are different (out-of-domain scenario) or
test data is adversarially perturbed (adversarial scenario), ASC models may
attend to irrelevant words and neglect opinion expressions that truly describe
diverse aspects. To tackle the challenge, in this paper, we hypothesize that
position bias (i.e., the words closer to a concerning aspect would carry a
higher degree of importance) is crucial for building more robust ASC models by
reducing the probability of mis-attending. Accordingly, we propose two
mechanisms for capturing position bias, namely position-biased weight and
position-biased dropout, which can be flexibly injected into existing models to
enhance representations for classification. Experiments conducted on
out-of-domain and adversarial datasets demonstrate that our proposed approaches
largely improve the robustness and effectiveness of current models.
| 2,021 |
Computation and Language
|
Predictive Representation Learning for Language Modeling
|
To effectively perform the task of next-word prediction, long short-term
memory networks (LSTMs) must keep track of many types of information. Some
information is directly related to the next word's identity, but some is more
secondary (e.g. discourse-level features or features of downstream words).
Correlates of secondary information appear in LSTM representations even though
they are not part of an \emph{explicitly} supervised prediction task. In
contrast, in reinforcement learning (RL), techniques that explicitly supervise
representations to predict secondary information have been shown to be
beneficial. Inspired by that success, we propose Predictive Representation
Learning (PRL), which explicitly constrains LSTMs to encode specific
predictions, like those that might need to be learned implicitly. We show that
PRL 1) significantly improves two strong language modeling methods, 2)
converges more quickly, and 3) performs better when data is limited. Our work
shows that explicitly encoding a simple predictive task facilitates the search
for a more effective language model.
| 2,021 |
Computation and Language
|
CoDesc: A Large Code-Description Parallel Dataset
|
Translation between natural language and source code can help software
development by enabling developers to comprehend, ideate, search, and write
computer programs in natural language. Despite growing interest from the
industry and the research community, this task is often difficult due to the
lack of large standard datasets suitable for training deep neural models,
standard noise removal methods, and evaluation benchmarks. This leaves
researchers to collect new small-scale datasets, resulting in inconsistencies
across published works. In this study, we present CoDesc -- a large parallel
dataset composed of 4.2 million Java methods and natural language descriptions.
With extensive analysis, we identify and remove prevailing noise patterns from
the dataset. We demonstrate the proficiency of CoDesc in two complementary
tasks for code-description pairs: code summarization and code search. We show
that the dataset helps improve code search by up to 22\% and achieves the new
state-of-the-art in code summarization. Furthermore, we show CoDesc's
effectiveness in pre-training--fine-tuning setup, opening possibilities in
building pretrained language models for Java. To facilitate future research, we
release the dataset, a data processing tool, and a benchmark at
\url{https://github.com/csebuetnlp/CoDesc}.
| 2,021 |
Computation and Language
|
Demoting the Lead Bias in News Summarization via Alternating Adversarial
Learning
|
In news articles the lead bias is a common phenomenon that usually dominates
the learning signals for neural extractive summarizers, severely limiting their
performance on data with different or even no bias. In this paper, we introduce
a novel technique to demote lead bias and make the summarizer focus more on the
content semantics. Experiments on two news corpora with different degrees of
lead bias show that our method can effectively demote the model's learned lead
bias and improve its generality on out-of-distribution data, with little to no
performance loss on in-distribution data.
| 2,021 |
Computation and Language
|
CommitBERT: Commit Message Generation Using Pre-Trained Programming
Language Model
|
Commit message is a document that summarizes source code changes in natural
language. A good commit message clearly shows the source code changes, so this
enhances collaboration between developers. Therefore, our work is to develop a
model that automatically writes the commit message.
To this end, we release 345K datasets consisting of code modification and
commit messages in six programming languages (Python, PHP, Go, Java,
JavaScript, and Ruby). Similar to the neural machine translation (NMT) model,
using our dataset, we feed the code modification to the encoder input and the
commit message to the decoder input and measure the result of the generated
commit message with BLEU-4.
Also, we propose the following two training methods to improve the result of
generating the commit message: (1) A method of preprocessing the input to feed
the code modification to the encoder input. (2) A method that uses an initial
weight suitable for the code domain to reduce the gap in contextual
representation between programming language (PL) and natural language (NL).
Training code, dataset, and pre-trained weights are available at
https://github.com/graykode/commit-autosuggestions
| 2,021 |
Computation and Language
|
Korean-English Machine Translation with Multiple Tokenization Strategy
|
This work was conducted to find out how tokenization methods affect the
training results of machine translation models. In this work, alphabet
tokenization, morpheme tokenization, and BPE tokenization were applied to
Korean as the source language and English as the target language respectively,
and the comparison experiment was conducted by repeating 50,000 epochs of each
9 models using the Transformer neural network. As a result of measuring the
BLEU scores of the experimental models, the model that applied BPE tokenization
to Korean and morpheme tokenization to English recorded 35.73, showing the best
performance.
| 2,021 |
Computation and Language
|
Grammar Accuracy Evaluation (GAE): Quantifiable Quantitative Evaluation
of Machine Translation Models
|
Natural Language Generation (NLG) refers to the operation of expressing the
calculation results of a system in human language. Since the quality of
generated sentences from an NLG model cannot be fully represented using only
quantitative evaluation, they are evaluated using qualitative evaluation by
humans in which the meaning or grammar of a sentence is scored according to a
subjective criterion. Nevertheless, the existing evaluation methods have a
problem as a large score deviation occurs depending on the criteria of
evaluators. In this paper, we propose Grammar Accuracy Evaluation (GAE) that
can provide the specific evaluating criteria. As a result of analyzing the
quality of machine translation by BLEU and GAE, it was confirmed that the BLEU
score does not represent the absolute performance of machine translation models
and GAE compensates for the shortcomings of BLEU with flexible evaluation of
alternative synonyms and changes in sentence structure.
| 2,022 |
Computation and Language
|
Modeling Discriminative Representations for Out-of-Domain Detection with
Supervised Contrastive Learning
|
Detecting Out-of-Domain (OOD) or unknown intents from user queries is
essential in a task-oriented dialog system. A key challenge of OOD detection is
to learn discriminative semantic features. Traditional cross-entropy loss only
focuses on whether a sample is correctly classified, and does not explicitly
distinguish the margins between categories. In this paper, we propose a
supervised contrastive learning objective to minimize intra-class variance by
pulling together in-domain intents belonging to the same class and maximize
inter-class variance by pushing apart samples from different classes. Besides,
we employ an adversarial augmentation mechanism to obtain pseudo diverse views
of a sample in the latent space. Experiments on two public datasets prove the
effectiveness of our method capturing discriminative representations for OOD
detection.
| 2,021 |
Computation and Language
|
A Simple Voting Mechanism for Online Sexist Content Identification
|
This paper presents the participation of the MiniTrue team in the EXIST 2021
Challenge on the sexism detection in social media task for English and Spanish.
Our approach combines the language models with a simple voting mechanism for
the sexist label prediction. For this, three BERT based models and a voting
function are used. Experimental results show that our final model with the
voting function has achieved the best results among our four models, which
means that our voting mechanism brings an extra benefit to our system.
Nevertheless, we also observe that our system is robust to data sources and
languages.
| 2,021 |
Computation and Language
|
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in
the Task-Oriented Dialogue System
|
Existing slot filling models can only recognize pre-defined in-domain slot
types from a limited slot set. In the practical application, a reliable
dialogue system should know what it does not know. In this paper, we introduce
a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system.
NSD aims to discover unknown or out-of-domain slot types to strengthen the
capability of a dialogue system based on in-domain training data. Besides, we
construct two public NSD datasets, propose several strong NSD baselines, and
establish a benchmark for future work. Finally, we conduct exhaustive
experiments and qualitative analysis to comprehend key challenges and provide
new guidance for future directions.
| 2,021 |
Computation and Language
|
Is Sluice Resolution really just Question Answering?
|
Sluice resolution is a problem where a system needs to output the
corresponding antecedents of wh-ellipses. The antecedents are elided contents
behind the wh-words but are implicitly referred to using contexts. Previous
work frames sluice resolution as question answering where this setting
outperforms all its preceding works by large margins. Ellipsis and questions
are referentially dependent expressions (anaphoras) and retrieving the
corresponding antecedents are like answering questions to output pieces of
clarifying information. However, the task is not fully solved. Therefore, we
want to further investigate what makes sluice resolution differ to question
answering and fill in the error gaps. We also present some results using recent
state-of-the-art question answering systems which improve the previous work
(86.01 to 90.39 F1).
| 2,021 |
Computation and Language
|
Constructing Flow Graphs from Procedural Cybersecurity Texts
|
Following procedural texts written in natural languages is challenging. We
must read the whole text to identify the relevant information or identify the
instruction flows to complete a task, which is prone to failures. If such texts
are structured, we can readily visualize instruction-flows, reason or infer a
particular step, or even build automated systems to help novice agents achieve
a goal. However, this structure recovery task is a challenge because of such
texts' diverse nature. This paper proposes to identify relevant information
from such texts and generate information flows between sentences. We built a
large annotated procedural text dataset (CTFW) in the cybersecurity domain
(3154 documents). This dataset contains valuable instructions regarding
software vulnerability analysis experiences. We performed extensive experiments
on CTFW with our LM-GNN model variants in multiple settings. To show the
generalizability of both this task and our method, we also experimented with
procedural texts from two other domains (Maintenance Manual and Cooking), which
are substantially different from cybersecurity. Our experiments show that Graph
Convolution Network with BERT sentence embeddings outperforms BERT in all three
domains
| 2,021 |
Computation and Language
|
Learning Domain-Specialised Representations for Cross-Lingual Biomedical
Entity Linking
|
Injecting external domain-specific knowledge (e.g., UMLS) into pretrained
language models (LMs) advances their capability to handle specialised in-domain
tasks such as biomedical entity linking (BEL). However, such abundant expert
knowledge is available only for a handful of languages (e.g., English). In this
work, by proposing a novel cross-lingual biomedical entity linking task
(XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically
diverse languages, we first investigate the ability of standard
knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual
LMs beyond the standard monolingual English BEL task. The scores indicate large
gaps to English performance. We then address the challenge of transferring
domain-specific knowledge in resource-rich languages to resource-poor ones. To
this end, we propose and evaluate a series of cross-lingual transfer methods
for the XL-BEL task, and demonstrate that general-domain bitext helps propagate
the available English knowledge to languages with little to no in-domain data.
Remarkably, we show that our proposed domain-specific transfer methods yield
consistent gains across all target languages, sometimes up to 20 Precision@1
points, without any in-domain knowledge in the target language, and without any
in-domain parallel data.
| 2,021 |
Computation and Language
|
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural
Architecture Search
|
While pre-trained language models (e.g., BERT) have achieved impressive
results on different natural language processing tasks, they have large numbers
of parameters and suffer from big computational and memory costs, which make
them difficult for real-world deployment. Therefore, model compression is
necessary to reduce the computation and memory cost of pre-trained models. In
this work, we aim to compress BERT and address the following two challenging
practical issues: (1) The compression algorithm should be able to output
multiple compressed models with different sizes and latencies, in order to
support devices with different memory and latency limitations; (2) The
algorithm should be downstream task agnostic, so that the compressed models are
generally applicable for different downstream tasks. We leverage techniques in
neural architecture search (NAS) and propose NAS-BERT, an efficient method for
BERT compression. NAS-BERT trains a big supernet on a search space containing a
variety of architectures and outputs multiple compressed models with adaptive
sizes and latency. Furthermore, the training of NAS-BERT is conducted on
standard self-supervised pre-training tasks (e.g., masked language model) and
does not depend on specific downstream tasks. Thus, the compressed models can
be used across various downstream tasks. The technical challenge of NAS-BERT is
that training a big supernet on the pre-training task is extremely costly. We
employ several techniques including block-wise search, search space pruning,
and performance approximation to improve search efficiency and accuracy.
Extensive experiments on GLUE and SQuAD benchmark datasets demonstrate that
NAS-BERT can find lightweight models with better accuracy than previous
approaches, and can be directly applied to different downstream tasks with
adaptive model sizes for different requirements of memory or latency.
| 2,021 |
Computation and Language
|
Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
|
Multi-modal dialog modeling is of growing interest. In this work, we propose
frameworks to resolve a specific case of multi-modal dialog generation that
better mimics multi-modal dialog generation in the real world, where each
dialog turn is associated with the visual context in which it takes place.
Specifically, we propose to model the mutual dependency between text-visual
features, where the model not only needs to learn the probability of generating
the next dialog utterance given preceding dialog utterances and visual
contexts, but also the probability of predicting the visual features in which a
dialog utterance takes place, leading the generated dialog utterance specific
to the visual context. We observe significant performance boosts over vanilla
models when the mutual dependency between text and visual features is modeled.
Code is available at https://github.com/ShannonAI/OpenViDial.
| 2,021 |
Computation and Language
|
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based
Simulation
|
We propose NeuralWOZ, a novel dialogue collection framework that uses
model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector
and Labeler. Collector generates dialogues from (1) user's goal instructions,
which are the user context and task constraints in natural language, and (2)
system's API call results, which is a list of possible query responses for user
requests from the given knowledge base. Labeler annotates the generated
dialogue by formulating the annotation as a multiple-choice problem, in which
the candidate labels are extracted from goal instructions and API call results.
We demonstrate the effectiveness of the proposed method in the zero-shot domain
transfer learning for dialogue state tracking. In the evaluation, the synthetic
dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with
improvements of 4.4% point joint goal accuracy on average across domains, and
improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1
dataset.
| 2,021 |
Computation and Language
|
Good for Misconceived Reasons: An Empirical Revisiting on the Need for
Visual Context in Multimodal Machine Translation
|
A neural multimodal machine translation (MMT) system is one that aims to
perform better translation by extending conventional text-only translation
models with multimodal information. Many recent studies report improvements
when equipping their models with the multimodal module, despite the controversy
of whether such improvements indeed come from the multimodal part. We revisit
the contribution of multimodal information in MMT by devising two interpretable
MMT models. To our surprise, although our models replicate similar gains as
recently developed multimodal-integrated systems achieved, our models learn to
ignore the multimodal information. Upon further investigation, we discover that
the improvements achieved by the multimodal models over text-only counterparts
are in fact results of the regularization effect. We report empirical findings
that highlight the importance of MMT models' interpretability, and discuss how
our findings will benefit future research.
| 2,021 |
Computation and Language
|
Pre-training Universal Language Representation
|
Despite the well-developed cut-edge representation learning for language,
most language representation models usually focus on specific levels of
linguistic units. This work introduces universal language representation
learning, i.e., embeddings of different levels of linguistic units or text with
quite diverse lengths in a uniform vector space. We propose the training
objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled
corpus by a simple but effective algorithm for pre-trained language models.
Then we empirically verify that well designed pre-training scheme may
effectively yield universal language representation, which will bring great
convenience when handling multiple layers of linguistic objects in a unified
way. Especially, our model achieves the highest accuracy on analogy tasks in
different language levels and significantly improves the performance on
downstream tasks in the GLUE benchmark and a question answering dataset.
| 2,021 |
Computation and Language
|
CLEVE: Contrastive Pre-training for Event Extraction
|
Event extraction (EE) has considerably benefited from pre-trained language
models (PLMs) by fine-tuning. However, existing pre-training methods have not
involved modeling event characteristics, resulting in the developed EE models
cannot take full advantage of large-scale unsupervised data. To this end, we
propose CLEVE, a contrastive pre-training framework for EE to better learn
event knowledge from large unsupervised data and their semantic structures
(e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to
learn event semantics and a graph encoder to learn event structures
respectively. Specifically, the text encoder learns event semantic
representations by self-supervised contrastive learning to represent the words
of the same events closer than those unrelated words; the graph encoder learns
event structure representations by graph contrastive pre-training on parsed
event-related semantic structures. The two complementary representations then
work together to improve both the conventional supervised EE and the
unsupervised "liberal" EE, which requires jointly extracting events and
discovering event schemata without any annotated data. Experiments on ACE 2005
and MAVEN datasets show that CLEVE achieves significant improvements,
especially in the challenging unsupervised setting. The source code and
pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.
| 2,021 |
Computation and Language
|
REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation
Metrics for Open-domain Dialog Generation
|
The lack of reliable automatic evaluation metrics is a major impediment to
the development of open-domain dialogue systems. Various reference-based
metrics have been proposed to calculate a score between a predicted response
and a small set of references. However, these metrics show unsatisfactory
correlations with human judgments. For a reference-based metric, its
reliability mainly depends on two factors: its ability to measure the
similarity between the predicted response and the reference response, as well
as the reliability of the given reference set. Yet, there are few discussions
on the latter. Our work attempts to fill this vacancy. We first clarify an
assumption on reference-based metrics that, if more high-quality references are
added into the reference set, the reliability of the metric will increase.
Next, we present REAM$\sharp$: an enhancement approach to Reference-based
EvAluation Metrics for open-domain dialogue systems. A prediction model is
designed to estimate the reliability of the given reference set. We show how
its predicted results can be helpful to augment the reference set, and thus
improve the reliability of the metric. Experiments validate both the
effectiveness of our prediction model and that the reliability of
reference-based metrics improves with the augmented reference sets.
| 2,022 |
Computation and Language
|
Structured Sentiment Analysis as Dependency Graph Parsing
|
Structured sentiment analysis attempts to extract full opinion tuples from a
text, but over time this task has been subdivided into smaller and smaller
sub-tasks, e,g,, target extraction or targeted polarity classification. We
argue that this division has become counterproductive and propose a new unified
framework to remedy the situation. We cast the structured sentiment problem as
dependency graph parsing, where the nodes are spans of sentiment holders,
targets and expressions, and the arcs are the relations between them. We
perform experiments on five datasets in four languages (English, Norwegian,
Basque, and Catalan) and show that this approach leads to strong improvements
over state-of-the-art baselines. Our analysis shows that refining the sentiment
graphs with syntactic dependency information further improves results.
| 2,021 |
Computation and Language
|
How Low is Too Low? A Computational Perspective on Extremely
Low-Resource Languages
|
Despite the recent advancements of attention-based deep learning
architectures across a majority of Natural Language Processing tasks, their
application remains limited in a low-resource setting because of a lack of
pre-trained models for such languages. In this study, we make the first attempt
to investigate the challenges of adapting these techniques for an extremely
low-resource language -- Sumerian cuneiform -- one of the world's oldest
written languages attested from at least the beginning of the 3rd millennium
BC. Specifically, we introduce the first cross-lingual information extraction
pipeline for Sumerian, which includes part-of-speech tagging, named entity
recognition, and machine translation. We further curate InterpretLR, an
interpretability toolkit for low-resource NLP, and use it alongside human
attributions to make sense of the models. We emphasize on human evaluations to
gauge all our techniques. Notably, most components of our pipeline can be
generalised to any other language to obtain an interpretable execution of the
techniques, especially in a low-resource setting. We publicly release all
software, model checkpoints, and a novel dataset with domain-specific
pre-processing to promote further research.
| 2,021 |
Computation and Language
|
Fast Nearest Neighbor Machine Translation
|
Though nearest neighbor Machine Translation ($k$NN-MT)
\citep{khandelwal2020nearest} has proved to introduce significant performance
boosts over standard neural MT systems, it is prohibitively slow since it uses
the entire reference corpus as the datastore for the nearest neighbor search.
This means each step for each beam in the beam search has to search over the
entire reference corpus. $k$NN-MT is thus two-orders slower than vanilla MT
models, making it hard to be applied to real-world applications, especially
online services. In this work, we propose Fast $k$NN-MT to address this issue.
Fast $k$NN-MT constructs a significantly smaller datastore for the nearest
neighbor search: for each word in a source sentence, Fast $k$NN-MT first
selects its nearest token-level neighbors, which is limited to tokens that are
the same as the query token. Then at each decoding step, in contrast to using
the entire corpus as the datastore, the search space is limited to target
tokens corresponding to the previously selected reference source tokens. This
strategy avoids search through the whole datastore for nearest neighbors and
drastically improves decoding efficiency. Without loss of performance, Fast
$k$NN-MT is two-orders faster than $k$NN-MT, and is only two times slower than
the standard NMT model. Fast $k$NN-MT enables the practical use of $k$NN-MT
systems in real-world MT applications. The code is available at
\url{https://github.com/ShannonAI/fast-knn-nmt}
| 2,022 |
Computation and Language
|
Defending Pre-trained Language Models from Adversarial Word
Substitutions Without Performance Sacrifice
|
Pre-trained contextualized language models (PrLMs) have led to strong
performance gains in downstream natural language understanding tasks. However,
PrLMs can still be easily fooled by adversarial word substitution, which is one
of the most challenging textual adversarial attack methods. Existing defence
approaches suffer from notable performance loss and complexities. Thus, this
paper presents a compact and performance-preserved framework, Anomaly Detection
with Frequency-Aware Randomization (ADFAR). In detail, we design an auxiliary
anomaly detection classifier and adopt a multi-task learning procedure, by
which PrLMs are able to distinguish adversarial input samples. Then, in order
to defend adversarial word substitution, a frequency-aware randomization
process is applied to those recognized adversarial input samples. Empirical
results show that ADFAR significantly outperforms those newly proposed defense
methods over various tasks with much higher inference speed. Remarkably, ADFAR
does not impair the overall performance of PrLMs. The code is available at
https://github.com/LilyNLP/ADFAR
| 2,021 |
Computation and Language
|
Diversifying Dialog Generation via Adaptive Label Smoothing
|
Neural dialogue generation models trained with the one-hot target
distribution suffer from the over-confidence issue, which leads to poor
generation diversity as widely reported in the literature. Although existing
approaches such as label smoothing can alleviate this issue, they fail to adapt
to diverse dialog contexts. In this paper, we propose an Adaptive Label
Smoothing (AdaLabel) approach that can adaptively estimate a target label
distribution at each time step for different contexts. The maximum probability
in the predicted distribution is used to modify the soft target distribution
produced by a novel light-weight bi-directional decoder module. The resulting
target distribution is aware of both previous and future contexts and is
adjusted to avoid over-training the dialogue model. Our model can be trained in
an end-to-end manner. Extensive experiments on two benchmark datasets show that
our approach outperforms various competitive baselines in producing diverse
responses.
| 2,021 |
Computation and Language
|
HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed
Language Representation
|
Understanding linguistics and morphology of resource-scarce code-mixed texts
remains a key challenge in text processing. Although word embedding comes in
handy to support downstream tasks for low-resource languages, there are plenty
of scopes in improving the quality of language representation particularly for
code-mixed languages. In this paper, we propose HIT, a robust representation
learning method for code-mixed texts. HIT is a hierarchical transformer-based
framework that captures the semantic relationship among words and
hierarchically learns the sentence-level semantics using a fused attention
mechanism. HIT incorporates two attention modules, a multi-headed
self-attention and an outer product attention module, and computes their
weighted sum to obtain the attention weights. Our evaluation of HIT on one
European (Spanish) and five Indic (Hindi, Bengali, Tamil, Telugu, and
Malayalam) languages across four NLP tasks on eleven datasets suggests
significant performance improvement against various state-of-the-art systems.
We further show the adaptability of learned representation across tasks in a
transfer learning setup (with and without fine-tuning).
| 2,021 |
Computation and Language
|
LEAP: Learnable Pruning for Transformer-based Models
|
Pruning is an effective method to reduce the memory footprint and
computational cost associated with large natural language processing models.
However, current pruning algorithms either only focus on one pruning category,
e.g., structured pruning and unstructured, or need extensive hyperparameter
tuning in order to get reasonable accuracy performance. To address these
challenges, we propose LEArnable Pruning (LEAP), an effective method to
gradually prune the model based on thresholds learned by gradient descent.
Different than previous learnable pruning methods, which utilize $L_0$ or $L_1$
penalty to indirectly affect the final pruning ratio, LEAP introduces a novel
regularization function, that directly interacts with the preset target pruning
ratio. Moreover, in order to reduce hyperparameter tuning, a novel adaptive
regularization coefficient is deployed to control the regularization penalty
adaptively. With the new regularization term and its associated adaptive
regularization coefficient, LEAP is able to be applied for different pruning
granularity, including unstructured pruning, structured pruning, and hybrid
pruning, with minimal hyperparameter tuning. We apply LEAP for BERT models on
QQP/MNLI/SQuAD for different pruning settings. Our result shows that for all
datasets, pruning granularity, and pruning ratios, LEAP achieves on-par or
better results as compared to previous heavily hand-tuned methods.
| 2,022 |
Computation and Language
|
Attention Flows are Shapley Value Explanations
|
Shapley Values, a solution to the credit assignment problem in cooperative
game theory, are a popular type of explanation in machine learning, having been
used to explain the importance of features, embeddings, and even neurons. In
NLP, however, leave-one-out and attention-based explanations still predominate.
Can we draw a connection between these different methods? We formally prove
that -- save for the degenerate case -- attention weights and leave-one-out
values cannot be Shapley Values. $\textit{Attention flow}$ is a post-processed
variant of attention weights obtained by running the max-flow algorithm on the
attention graph. Perhaps surprisingly, we prove that attention flows are indeed
Shapley Values, at least at the layerwise level. Given the many desirable
theoretical qualities of Shapley Values -- which has driven their adoption
among the ML community -- we argue that NLP practitioners should, when
possible, adopt attention flow explanations alongside more traditional ones.
| 2,021 |
Computation and Language
|
On the Interplay Between Fine-tuning and Composition in Transformers
|
Pre-trained transformer language models have shown remarkable performance on
a variety of NLP tasks. However, recent research has suggested that
phrase-level representations in these models reflect heavy influences of
lexical content, but lack evidence of sophisticated, compositional phrase
information. Here we investigate the impact of fine-tuning on the capacity of
contextualized embeddings to capture phrase meaning information beyond lexical
content. Specifically, we fine-tune models on an adversarial paraphrase
classification task with high lexical overlap, and on a sentiment
classification task. After fine-tuning, we analyze phrasal representations in
controlled settings following prior work. We find that fine-tuning largely
fails to benefit compositionality in these representations, though training on
sentiment yields a small, localized benefit for certain models. In follow-up
analyses, we identify confounding cues in the paraphrase dataset that may
explain the lack of composition benefits from that task, and we discuss
potential factors underlying the localized benefits from sentiment training.
| 2,021 |
Computation and Language
|
Zero-shot Fact Verification by Claim Generation
|
Neural models for automated fact verification have achieved promising results
thanks to the availability of large, human-annotated datasets. However, for
each new domain that requires fact verification, creating a dataset by manually
writing claims and linking them to their supporting evidence is expensive. We
develop QACG, a framework for training a robust fact verification model by
using automatically generated claims that can be supported, refuted, or
unverifiable from evidence from Wikipedia. QACG generates question-answer pairs
from the evidence and then converts them into different types of claims.
Experiments on the FEVER dataset show that our QACG framework significantly
reduces the demand for human-annotated training data. In a zero-shot scenario,
QACG improves a RoBERTa model's F1 from 50% to 77%, equivalent in performance
to 2K+ manually-curated examples. Our QACG code is publicly available.
| 2,021 |
Computation and Language
|
Fully Hyperbolic Neural Networks
|
Hyperbolic neural networks have shown great potential for modeling complex
data. However, existing hyperbolic networks are not completely hyperbolic, as
they encode features in a hyperbolic space yet formalize most of their
operations in the tangent space (a Euclidean subspace) at the origin of the
hyperbolic space. This hybrid method greatly limits the modeling ability of
networks. In this paper, we propose a fully hyperbolic framework to build
hyperbolic networks based on the Lorentz model by adapting the Lorentz
transformations (including boost and rotation) to formalize essential
operations of neural networks. Moreover, we also prove that linear
transformation in tangent spaces used by existing hyperbolic networks is a
relaxation of the Lorentz rotation and does not include the boost, implicitly
limiting the capabilities of existing hyperbolic networks. The experimental
results on four NLP tasks show that our method has better performance for
building both shallow and deep networks. Our code will be released to
facilitate follow-up research.
| 2,022 |
Computation and Language
|
G-Transformer for Document-level Machine Translation
|
Document-level MT models are still far from satisfactory. Existing work
extend translation unit from single sentence to multiple sentences. However,
study shows that when we further enlarge the translation unit to a whole
document, supervised training of Transformer can fail. In this paper, we find
such failure is not caused by overfitting, but by sticking around local minima
during training. Our analysis shows that the increased complexity of
target-to-source attention is a reason for the failure. As a solution, we
propose G-Transformer, introducing locality assumption as an inductive bias
into Transformer, reducing the hypothesis space of the attention from target to
source. Experiments show that G-Transformer converges faster and more stably
than Transformer, achieving new state-of-the-art BLEU scores for both
non-pretraining and pre-training settings on three benchmark datasets.
| 2,021 |
Computation and Language
|
Emotional Voice Conversion: Theory, Databases and ESD
|
In this paper, we first provide a review of the state-of-the-art emotional
voice conversion research, and the existing emotional speech databases. We then
motivate the development of a novel emotional speech database (ESD) that
addresses the increasing research need. With this paper, the ESD database is
now made available to the research community. The ESD database consists of 350
parallel utterances spoken by 10 native English and 10 native Chinese speakers
and covers 5 emotion categories (neutral, happy, angry, sad and surprise). More
than 29 hours of speech data were recorded in a controlled acoustic
environment. The database is suitable for multi-speaker and cross-lingual
emotional voice conversion studies. As case studies, we implement several
state-of-the-art emotional voice conversion systems on the ESD database. This
paper provides a reference study on ESD in conjunction with its release.
| 2,022 |
Computation and Language
|
LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts
and Images using CLIP features
|
We describe our approach for SemEval-2021 task 6 on detection of persuasion
techniques in multimodal content (memes). Our system combines pretrained
multimodal models (CLIP) and chained classifiers. Also, we propose to enrich
the data by a data augmentation technique. Our submission achieves a rank of
8/16 in terms of F1-micro and 9/16 with F1-macro on the test set.
| 2,021 |
Computation and Language
|
Sketch and Refine: Towards Faithful and Informative Table-to-Text
Generation
|
Table-to-text generation refers to generating a descriptive text from a
key-value table. Traditional autoregressive methods, though can generate text
with high fluency, suffer from low coverage and poor faithfulness problems. To
mitigate these problems, we propose a novel Skeleton-based two-stage method
that combines both Autoregressive and Non-Autoregressive generations (SANA).
Our approach includes: (1) skeleton generation with an autoregressive pointer
network to select key tokens from the source table; (2) edit-based
non-autoregressive generation model to produce texts via iterative insertion
and deletion operations. By integrating hard constraints from the skeleton, the
non-autoregressive model improves the generation's coverage over the source
table and thus enhances its faithfulness. We conduct automatic and human
evaluations on both WikiPerson and WikiBio datasets. Experimental results
demonstrate that our method outperforms the previous state-of-the-art methods
in both automatic and human evaluation, especially on coverage and
faithfulness. In particular, we achieve PARENT-T recall of 99.47 in WikiPerson,
improving over the existing best results by more than 10 points.
| 2,021 |
Computation and Language
|
Towards One Model to Rule All: Multilingual Strategy for Dialectal
Code-Switching Arabic ASR
|
With the advent of globalization, there is an increasing demand for
multilingual automatic speech recognition (ASR), handling language and
dialectal variation of spoken content. Recent studies show its efficacy over
monolingual systems. In this study, we design a large multilingual end-to-end
ASR using self-attention based conformer architecture. We trained the system
using Arabic (Ar), English (En) and French (Fr) languages. We evaluate the
system performance handling: (i) monolingual (Ar, En and Fr); (ii)
multi-dialectal (Modern Standard Arabic, along with dialectal variation such as
Egyptian and Moroccan); (iii) code-switching -- cross-lingual (Ar-En/Fr) and
dialectal (MSA-Egyptian dialect) test cases, and compare with current
state-of-the-art systems. Furthermore, we investigate the influence of
different embedding/character representations including character vs
word-piece; shared vs distinct input symbol per language. Our findings
demonstrate the strength of such a model by outperforming state-of-the-art
monolingual dialectal Arabic and code-switching Arabic ASR.
| 2,021 |
Computation and Language
|
A Semantic-based Method for Unsupervised Commonsense Question Answering
|
Unsupervised commonsense question answering is appealing since it does not
rely on any labeled task data. Among existing work, a popular solution is to
use pre-trained language models to score candidate choices directly conditioned
on the question or context. However, such scores from language models can be
easily affected by irrelevant factors, such as word frequencies, sentence
structures, etc. These distracting factors may not only mislead the model to
choose a wrong answer but also make it oversensitive to lexical perturbations
in candidate answers.
In this paper, we present a novel SEmantic-based Question Answering method
(SEQA) for unsupervised commonsense question answering. Instead of directly
scoring each answer choice, our method first generates a set of plausible
answers with generative models (e.g., GPT-2), and then uses these plausible
answers to select the correct choice by considering the semantic similarity
between each plausible answer and each choice. We devise a simple, yet sound
formalism for this idea and verify its effectiveness and robustness with
extensive experiments. We evaluate the proposed method on four benchmark
datasets, and our method achieves the best results in unsupervised settings.
Moreover, when attacked by TextFooler with synonym replacement, SEQA
demonstrates much less performance drops than baselines, thereby indicating
stronger robustness.
| 2,021 |
Computation and Language
|
On Compositional Generalization of Neural Machine Translation
|
Modern neural machine translation (NMT) models have achieved competitive
performance in standard benchmarks such as WMT. However, there still exist
significant issues such as robustness, domain generalization, etc. In this
paper, we study NMT models from the perspective of compositional generalization
by building a benchmark dataset, CoGnition, consisting of 216k clean and
consistent sentence pairs. We quantitatively analyze effects of various factors
using compound translation error rate, then demonstrate that the NMT model
fails badly on compositional generalization, although it performs remarkably
well under traditional metrics.
| 2,021 |
Computation and Language
|
Transfer Learning for Sequence Generation: from Single-source to
Multi-source
|
Multi-source sequence generation (MSG) is an important kind of sequence
generation tasks that takes multiple sources, including automatic post-editing,
multi-source translation, multi-document summarization, etc. As MSG tasks
suffer from the data scarcity problem and recent pretrained models have been
proven to be effective for low-resource downstream tasks, transferring
pretrained sequence-to-sequence models to MSG tasks is essential. Although
directly finetuning pretrained models on MSG tasks and concatenating multiple
sources into a single long sequence is regarded as a simple method to transfer
pretrained models to MSG tasks, we conjecture that the direct finetuning method
leads to catastrophic forgetting and solely relying on pretrained
self-attention layers to capture cross-source information is not sufficient.
Therefore, we propose a two-stage finetuning method to alleviate the
pretrain-finetune discrepancy and introduce a novel MSG model with a fine
encoder to learn better representations in MSG tasks. Experiments show that our
approach achieves new state-of-the-art results on the WMT17 APE task and
multi-source translation task using the WMT14 test set. When adapted to
document-level translation, our framework outperforms strong baselines
significantly.
| 2,021 |
Computation and Language
|
Exploration and Exploitation: Two Ways to Improve Chinese Spelling
Correction Models
|
A sequence-to-sequence learning with neural networks has empirically proven
to be an effective framework for Chinese Spelling Correction (CSC), which takes
a sentence with some spelling errors as input and outputs the corrected one.
However, CSC models may fail to correct spelling errors covered by the
confusion sets, and also will encounter unseen ones. We propose a method, which
continually identifies the weak spots of a model to generate more valuable
training instances, and apply a task-specific pre-training strategy to enhance
the model. The generated adversarial examples are gradually added to the
training set. Experimental results show that such an adversarial training
method combined with the pretraining strategy can improve both the
generalization and robustness of multiple CSC models across three different
datasets, achieving stateof-the-art performance for CSC task.
| 2,021 |
Computation and Language
|
Supporting Cognitive and Emotional Empathic Writing of Students
|
We present an annotation approach to capturing emotional and cognitive
empathy in student-written peer reviews on business models in German. We
propose an annotation scheme that allows us to model emotional and cognitive
empathy scores based on three types of review components. Also, we conducted an
annotation study with three annotators based on 92 student essays to evaluate
our annotation scheme. The obtained inter-rater agreement of {\alpha}=0.79 for
the components and the multi-{\pi}=0.41 for the empathy scores indicate that
the proposed annotation scheme successfully guides annotators to a substantial
to moderate agreement. Moreover, we trained predictive models to detect the
annotated empathy structures and embedded them in an adaptive writing support
system for students to receive individual empathy feedback independent of an
instructor, time, and location. We evaluated our tool in a peer learning
exercise with 58 students and found promising results for perceived empathy
skill learning, perceived feedback accuracy, and intention to use. Finally, we
present our freely available corpus of 500 empathy-annotated, student-written
peer reviews on business models and our annotation guidelines to encourage
future research on the design and development of empathy support systems.
| 2,021 |
Computation and Language
|
Effective Batching for Recurrent Neural Network Grammars
|
As a language model that integrates traditional symbolic operations and
flexible neural representations, recurrent neural network grammars (RNNGs) have
attracted great attention from both scientific and engineering perspectives.
However, RNNGs are known to be harder to scale due to the difficulty of batched
training. In this paper, we propose effective batching for RNNGs, where every
operation is computed in parallel with tensors across multiple sentences. Our
PyTorch implementation effectively employs a GPU and achieves x6 speedup
compared to the existing C++ DyNet implementation with model-independent
auto-batching. Moreover, our batched RNNG also accelerates inference and
achieves x20-150 speedup for beam search depending on beam sizes. Finally, we
evaluate syntactic generalization performance of the scaled RNNG against the
LSTM baseline, based on the large training data of 100M tokens from English
Wikipedia and the broad-coverage targeted syntactic evaluation benchmark. Our
RNNG implementation is available at https://github.com/aistairc/rnng-pytorch/.
| 2,021 |
Computation and Language
|
Greedy-layer Pruning: Speeding up Transformer Models for Natural
Language Processing
|
Fine-tuning transformer models after unsupervised pre-training reaches a very
high performance on many different natural language processing tasks.
Unfortunately, transformers suffer from long inference times which greatly
increases costs in production. One possible solution is to use knowledge
distillation, which solves this problem by transferring information from large
teacher models to smaller student models. Knowledge distillation maintains high
performance and reaches high compression rates, nevertheless, the size of the
student model is fixed after pre-training and can not be changed individually
for a given downstream task and use-case to reach a desired performance/speedup
ratio. Another solution to reduce the size of models in a much more
fine-grained and computationally cheaper fashion is to prune layers after the
pre-training. The price to pay is that the performance of layer-wise pruning
algorithms is not on par with state-of-the-art knowledge distillation methods.
In this paper, Greedy-layer pruning is introduced to (1) outperform current
state-of-the-art for layer-wise pruning, (2) close the performance gap when
compared to knowledge distillation, while (3) providing a method to adapt the
model size dynamically to reach a desired performance/speedup tradeoff without
the need of additional pre-training phases. Our source code is available on
https://github.com/deepopinion/greedy-layer-pruning.
| 2,022 |
Computation and Language
|
Cascaded Head-colliding Attention
|
Transformers have advanced the field of natural language processing (NLP) on
a variety of important tasks. At the cornerstone of the Transformer
architecture is the multi-head attention (MHA) mechanism which models pairwise
interactions between the elements of the sequence. Despite its massive success,
the current framework ignores interactions among different heads, leading to
the problem that many of the heads are redundant in practice, which greatly
wastes the capacity of the model. To improve parameter efficiency, we
re-formulate the MHA as a latent variable model from a probabilistic
perspective. We present cascaded head-colliding attention (CODA) which
explicitly models the interactions between attention heads through a
hierarchical variational distribution. We conduct extensive experiments and
demonstrate that CODA outperforms the transformer baseline, by $0.6$ perplexity
on \texttt{Wikitext-103} in language modeling, and by $0.6$ BLEU on
\texttt{WMT14 EN-DE} in machine translation, due to its improvements on the
parameter efficiency.\footnote{Our implementation is publicly available at
\url{https://github.com/LZhengisme/CODA}.}
| 2,021 |
Computation and Language
|
Bangla Natural Language Processing: A Comprehensive Analysis of
Classical, Machine Learning, and Deep Learning Based Methods
|
The Bangla language is the seventh most spoken language, with 265 million
native and non-native speakers worldwide. However, English is the predominant
language for online resources and technical knowledge, journals, and
documentation. Consequently, many Bangla-speaking people, who have limited
command of English, face hurdles to utilize English resources. To bridge the
gap between limited support and increasing demand, researchers conducted many
experiments and developed valuable tools and techniques to create and process
Bangla language materials. Many efforts are also ongoing to make it easy to use
the Bangla language in the online and technical domains. There are some review
papers to understand the past, previous, and future Bangla Natural Language
Processing (BNLP) trends. The studies are mainly concentrated on the specific
domains of BNLP, such as sentiment analysis, speech recognition, optical
character recognition, and text summarization. There is an apparent scarcity of
resources that contain a comprehensive review of the recent BNLP tools and
methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP
research papers and categorize them into 11 categories, namely Information
Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of
Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake
Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing
and Recognition. We study articles published between 1999 to 2021, and 50% of
the papers were published after 2015. Furthermore, we discuss Classical,
Machine Learning and Deep Learning approaches with different datasets while
addressing the limitations and current and future trends of the BNLP.
| 2,022 |
Computation and Language
|
Verdi: Quality Estimation and Error Detection for Bilingual Corpora
|
Translation Quality Estimation is critical to reducing post-editing efforts
in machine translation and to cross-lingual corpus cleaning. As a research
problem, quality estimation (QE) aims to directly estimate the quality of
translation in a given pair of source and target sentences, and highlight the
words that need corrections, without referencing to golden translations. In
this paper, we propose Verdi, a novel framework for word-level and
sentence-level post-editing effort estimation for bilingual corpora. Verdi
adopts two word predictors to enable diverse features to be extracted from a
pair of sentences for subsequent quality estimation, including a
transformer-based neural machine translation (NMT) model and a pre-trained
cross-lingual language model (XLM). We exploit the symmetric nature of
bilingual corpora and apply model-level dual learning in the NMT predictor,
which handles a primal task and a dual task simultaneously with weight sharing,
leading to stronger context prediction ability than single-direction NMT
models. By taking advantage of the dual learning scheme, we further design a
novel feature to directly encode the translated target information without
relying on the source context. Extensive experiments conducted on WMT20 QE
tasks demonstrate that our method beats the winner of the competition and
outperforms other baseline methods by a great margin. We further use the
sentence-level scores provided by Verdi to clean a parallel corpus and observe
benefits on both model performance and training efficiency.
| 2,021 |
Computation and Language
|
SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
|
This paper introduces the SemEval-2021 shared task 4: Reading Comprehension
of Abstract Meaning (ReCAM). This shared task is designed to help evaluate the
ability of machines in representing and understanding abstract concepts. Given
a passage and the corresponding question, a participating system is expected to
choose the correct answer from five candidates of abstract concepts in a
cloze-style machine reading comprehension setup. Based on two typical
definitions of abstractness, i.e., the imperceptibility and nonspecificity, our
task provides three subtasks to evaluate the participating models.
Specifically, Subtask 1 aims to evaluate how well a system can model concepts
that cannot be directly perceived in the physical world. Subtask 2 focuses on
models' ability in comprehending nonspecific concepts located high in a
hypernym hierarchy given the context of a passage. Subtask 3 aims to provide
some insights into models' generalizability over the two types of abstractness.
During the SemEval-2021 official evaluation period, we received 23 submissions
to Subtask 1 and 28 to Subtask 2. The participating teams additionally made 29
submissions to Subtask 3. The leaderboard and competition website can be found
at https://competitions.codalab.org/competitions/26153. The data and baseline
code are available at
https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning.
| 2,021 |
Computation and Language
|
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