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Lexically-constrained Text Generation through Commonsense Knowledge
Extraction and Injection
|
Conditional text generation has been a challenging task that is yet to see
human-level performance from state-of-the-art models. In this work, we
specifically focus on the Commongen benchmark, wherein the aim is to generate a
plausible sentence for a given set of input concepts. Despite advances in other
tasks, large pre-trained language models that are fine-tuned on this dataset
often produce sentences that are syntactically correct but qualitatively
deviate from a human understanding of common sense. Furthermore, generated
sequences are unable to fulfill such lexical requirements as matching
part-of-speech and full concept coverage. In this paper, we explore how
commonsense knowledge graphs can enhance model performance, with respect to
commonsense reasoning and lexically-constrained decoding. We propose strategies
for enhancing the semantic correctness of the generated text, which we
accomplish through: extracting commonsense relations from Conceptnet, injecting
these relations into the Unified Language Model (UniLM) through attention
mechanisms, and enforcing the aforementioned lexical requirements through
output constraints. By performing several ablations, we find that commonsense
injection enables the generation of sentences that are more aligned with human
understanding, while remaining compliant with lexical requirements.
| 2,020 |
Computation and Language
|
A hybrid deep-learning approach for complex biochemical named entity
recognition
|
Named entity recognition (NER) of chemicals and drugs is a critical domain of
information extraction in biochemical research. NER provides support for text
mining in biochemical reactions, including entity relation extraction,
attribute extraction, and metabolic response relationship extraction. However,
the existence of complex naming characteristics in the biomedical field, such
as polysemy and special characters, make the NER task very challenging. Here,
we propose a hybrid deep learning approach to improve the recognition accuracy
of NER. Specifically, our approach applies the Bidirectional Encoder
Representations from Transformers (BERT) model to extract the underlying
features of the text, learns a representation of the context of the text
through Bi-directional Long Short-Term Memory (BILSTM), and incorporates the
multi-head attention (MHATT) mechanism to extract chapter-level features. In
this approach, the MHATT mechanism aims to improve the recognition accuracy of
abbreviations to efficiently deal with the problem of inconsistency in
full-text labels. Moreover, conditional random field (CRF) is used to label
sequence tags because this probabilistic method does not need strict
independence assumptions and can accommodate arbitrary context information. The
experimental evaluation on a publicly-available dataset shows that the proposed
hybrid approach achieves the best recognition performance; in particular, it
substantially improves performance in recognizing abbreviations, polysemes, and
low-frequency entities, compared with the state-of-the-art approaches. For
instance, compared with the recognition accuracies for low-frequency entities
produced by the BILSTM-CRF algorithm, those produced by the hybrid approach on
two entity datasets (MULTIPLE and IDENTIFIER) have been increased by 80% and
21.69%, respectively.
| 2,020 |
Computation and Language
|
Adaptive Bi-directional Attention: Exploring Multi-Granularity
Representations for Machine Reading Comprehension
|
Recently, the attention-enhanced multi-layer encoder, such as Transformer,
has been extensively studied in Machine Reading Comprehension (MRC). To predict
the answer, it is common practice to employ a predictor to draw information
only from the final encoder layer which generates the \textit{coarse-grained}
representations of the source sequences, i.e., passage and question. Previous
studies have shown that the representation of source sequence becomes more
\textit{coarse-grained} from \textit{fine-grained} as the encoding layer
increases. It is generally believed that with the growing number of layers in
deep neural networks, the encoding process will gather relevant information for
each location increasingly, resulting in more \textit{coarse-grained}
representations, which adds the likelihood of similarity to other locations
(referring to homogeneity). Such a phenomenon will mislead the model to make
wrong judgments so as to degrade the performance. To this end, we propose a
novel approach called Adaptive Bidirectional Attention, which adaptively
exploits the source representations of different levels to the predictor.
Experimental results on the benchmark dataset, SQuAD 2.0 demonstrate the
effectiveness of our approach, and the results are better than the previous
state-of-the-art model by 2.5$\%$ EM and 2.3$\%$ F1 scores.
| 2,021 |
Computation and Language
|
A Graph Reasoning Network for Multi-turn Response Selection via
Customized Pre-training
|
We investigate response selection for multi-turn conversation in
retrieval-based chatbots. Existing studies pay more attention to the matching
between utterances and responses by calculating the matching score based on
learned features, leading to insufficient model reasoning ability. In this
paper, we propose a graph-reasoning network (GRN) to address the problem. GRN
first conducts pre-training based on ALBERT using next utterance prediction and
utterance order prediction tasks specifically devised for response selection.
These two customized pre-training tasks can endow our model with the ability of
capturing semantical and chronological dependency between utterances. We then
fine-tune the model on an integrated network with sequence reasoning and graph
reasoning structures. The sequence reasoning module conducts inference based on
the highly summarized context vector of utterance-response pairs from the
global perspective. The graph reasoning module conducts the reasoning on the
utterance-level graph neural network from the local perspective. Experiments on
two conversational reasoning datasets show that our model can dramatically
outperform the strong baseline methods and can achieve performance which is
close to human-level.
| 2,021 |
Computation and Language
|
Towards Incorporating Entity-specific Knowledge Graph Information in
Predicting Drug-Drug Interactions
|
Off-the-shelf biomedical embeddings obtained from the recently released
various pre-trained language models (such as BERT, XLNET) have demonstrated
state-of-the-art results (in terms of accuracy) for the various natural
language understanding tasks (NLU) in the biomedical domain. Relation
Classification (RC) falls into one of the most critical tasks. In this paper,
we explore how to incorporate domain knowledge of the biomedical entities (such
as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for
predicting Drug-Drug Interaction from textual corpus. We propose a new method,
BERTKG-DDI, to combine drug embeddings obtained from its interaction with other
biomedical entities along with domain-specific BioBERT embedding-based RC
architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly
indicate that this strategy improves other baselines architectures by 4.1%
macro F1-score.
| 2,020 |
Computation and Language
|
Domain specific BERT representation for Named Entity Recognition of lab
protocol
|
Supervised models trained to predict properties from representations have
been achieving high accuracy on a variety of tasks. For instance, the BERT
family seems to work exceptionally well on the downstream task from NER tagging
to the range of other linguistic tasks. But the vocabulary used in the medical
field contains a lot of different tokens used only in the medical industry such
as the name of different diseases, devices, organisms, medicines, etc. that
makes it difficult for traditional BERT model to create contextualized
embedding. In this paper, we are going to illustrate the System for Named
Entity Tagging based on Bio-Bert. Experimental results show that our model
gives substantial improvements over the baseline and stood the fourth runner up
in terms of F1 score, and first runner up in terms of Recall with just 2.21 F1
score behind the best one.
| 2,020 |
Computation and Language
|
Narrative Incoherence Detection
|
We propose the task of narrative incoherence detection as a new arena for
inter-sentential semantic understanding: Given a multi-sentence narrative,
decide whether there exist any semantic discrepancies in the narrative flow.
Specifically, we focus on the missing sentence and discordant sentence
detection. Despite its simple setup, this task is challenging as the model
needs to understand and analyze a multi-sentence narrative, and predict
incoherence at the sentence level. As an initial step towards this task, we
implement several baselines either directly analyzing the raw text
(\textit{token-level}) or analyzing learned sentence representations
(\textit{sentence-level}). We observe that while token-level modeling has
better performance when the input contains fewer sentences, sentence-level
modeling performs better on longer narratives and possesses an advantage in
efficiency and flexibility. Pre-training on large-scale data and auxiliary
sentence prediction training objective further boost the detection performance
of the sentence-level model.
| 2,021 |
Computation and Language
|
MT-Teql: Evaluating and Augmenting Consistency of Text-to-SQL Models
with Metamorphic Testing
|
Text-to-SQL is a task to generate SQL queries from human utterances. However,
due to the variation of natural language, two semantically equivalent
utterances may appear differently in the lexical level. Likewise, user
preferences (e.g., the choice of normal forms) can lead to dramatic changes in
table structures when expressing conceptually identical schemas. Envisioning
the general difficulty for text-to-SQL models to preserve prediction
consistency against linguistic and schema variations, we propose MT-Teql, a
Metamorphic Testing-based framework for systematically evaluating and
augmenting the consistency of TExt-to-SQL models. Inspired by the principles of
software metamorphic testing, MT-Teql delivers a model-agnostic framework which
implements a comprehensive set of metamorphic relations (MRs) to conduct
semantics-preserving transformations toward utterances and schemas. Model
Inconsistency can be exposed when the original and transformed inputs induce
different SQL queries. In addition, we leverage the transformed inputs to
retrain models for further model robustness boost. Our experiments show that
our framework exposes thousands of prediction errors from SOTA models and
enriches existing datasets by order of magnitude, eliminating over 40%
inconsistency errors without compromising standard accuracy.
| 2,020 |
Computation and Language
|
Medical Entity Linking using Triplet Network
|
Entity linking (or Normalization) is an essential task in text mining that
maps the entity mentions in the medical text to standard entities in a given
Knowledge Base (KB). This task is of great importance in the medical domain. It
can also be used for merging different medical and clinical ontologies. In this
paper, we center around the problem of disease linking or normalization. This
task is executed in two phases: candidate generation and candidate scoring. In
this paper, we present an approach to rank the candidate Knowledge Base entries
based on their similarity with disease mention. We make use of the Triplet
Network for candidate ranking. While the existing methods have used carefully
generated sieves and external resources for candidate generation, we introduce
a robust and portable candidate generation scheme that does not make use of the
hand-crafted rules. Experimental results on the standard benchmark NCBI disease
dataset demonstrate that our system outperforms the prior methods by a
significant margin.
| 2,020 |
Computation and Language
|
An End-to-End Document-Level Neural Discourse Parser Exploiting
Multi-Granularity Representations
|
Document-level discourse parsing, in accordance with the Rhetorical Structure
Theory (RST), remains notoriously challenging. Challenges include the deep
structure of document-level discourse trees, the requirement of subtle semantic
judgments, and the lack of large-scale training corpora. To address such
challenges, we propose to exploit robust representations derived from multiple
levels of granularity across syntax and semantics, and in turn incorporate such
representations in an end-to-end encoder-decoder neural architecture for more
resourceful discourse processing. In particular, we first use a pre-trained
contextual language model that embodies high-order and long-range dependency to
enable finer-grain semantic, syntactic, and organizational representations. We
further encode such representations with boundary and hierarchical information
to obtain more refined modeling for document-level discourse processing.
Experimental results show that our parser achieves the state-of-the-art
performance, approaching human-level performance on the benchmarked RST
dataset.
| 2,020 |
Computation and Language
|
Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text
Summarization
|
Text summarization is one of the most critical Natural Language Processing
(NLP) tasks. More and more researches are conducted in this field every day.
Pre-trained transformer-based encoder-decoder models have begun to gain
popularity for these tasks. This paper proposes two methods to address this
task and introduces a novel dataset named pn-summary for Persian abstractive
text summarization. The models employed in this paper are mT5 and an
encoder-decoder version of the ParsBERT model (i.e., a monolingual BERT model
for Persian). These models are fine-tuned on the pn-summary dataset. The
current work is the first of its kind and, by achieving promising results, can
serve as a baseline for any future work.
| 2,021 |
Computation and Language
|
Self-attention Comparison Module for Boosting Performance on
Retrieval-based Open-Domain Dialog Systems
|
Since the pre-trained language models are widely used, retrieval-based
open-domain dialog systems, have attracted considerable attention from
researchers recently. Most of the previous works select a suitable response
only according to the matching degree between the query and each individual
candidate response. Although good performance has been achieved, these recent
works ignore the comparison among the candidate responses, which could provide
rich information for selecting the most appropriate response. Intuitively,
better decisions could be made when the models can get access to the comparison
information among all the candidate responses. In order to leverage the
comparison information among the candidate responses, in this paper, we propose
a novel and plug-in Self-attention Comparison Module for retrieval-based
open-domain dialog systems, called SCM. Extensive experiment results
demonstrate that our proposed self-attention comparison module effectively
boosts the performance of the existing retrieval-based open-domain dialog
systems. Besides, we have publicly released our source codes for future
research.
| 2,020 |
Computation and Language
|
Document-Level Relation Extraction with Reconstruction
|
In document-level relation extraction (DocRE), graph structure is generally
used to encode relation information in the input document to classify the
relation category between each entity pair, and has greatly advanced the DocRE
task over the past several years. However, the learned graph representation
universally models relation information between all entity pairs regardless of
whether there are relationships between these entity pairs. Thus, those entity
pairs without relationships disperse the attention of the encoder-classifier
DocRE for ones with relationships, which may further hind the improvement of
DocRE. To alleviate this issue, we propose a novel
encoder-classifier-reconstructor model for DocRE. The reconstructor manages to
reconstruct the ground-truth path dependencies from the graph representation,
to ensure that the proposed DocRE model pays more attention to encode entity
pairs with relationships in the training. Furthermore, the reconstructor is
regarded as a relationship indicator to assist relation classification in the
inference, which can further improve the performance of DocRE model.
Experimental results on a large-scale DocRE dataset show that the proposed
model can significantly improve the accuracy of relation extraction on a strong
heterogeneous graph-based baseline.
| 2,020 |
Computation and Language
|
TechTexC: Classification of Technical Texts using Convolution and
Bidirectional Long Short Term Memory Network
|
This paper illustrates the details description of technical text
classification system and its results that developed as a part of participation
in the shared task TechDofication 2020. The shared task consists of two
sub-tasks: (i) first task identify the coarse-grained technical domain of given
text in a specified language and (ii) the second task classify a text of
computer science domain into fine-grained sub-domains. A classification system
(called 'TechTexC') is developed to perform the classification task using three
techniques: convolution neural network (CNN), bidirectional long short term
memory (BiLSTM) network, and combined CNN with BiLSTM. Results show that CNN
with BiLSTM model outperforms the other techniques concerning task-1 of
sub-tasks (a, b, c and g) and task-2a. This combined model obtained f1 scores
of 82.63 (sub-task a), 81.95 (sub-task b), 82.39 (sub-task c), 84.37 (sub-task
g), and 67.44 (task-2a) on the development dataset. Moreover, in the case of
test set, the combined CNN with BiLSTM approach achieved that higher accuracy
for the subtasks 1a (70.76%), 1b (79.97%), 1c (65.45%), 1g (49.23%) and 2a
(70.14%).
| 2,020 |
Computation and Language
|
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant
Systems
|
Query rewriting (QR) systems are widely used to reduce the friction caused by
errors in a spoken language understanding pipeline. However, the underlying
supervised models require a large number of labeled pairs, and these pairs are
hard and costly to be collected. Therefore, We propose an augmentation
framework that learns patterns from existing training pairs and generates
rewrite candidates from rewrite labels inversely to compensate for insufficient
QR training data. The proposed framework casts the augmentation problem as a
sequence-to-sequence generation task and enforces the optimization process with
a policy gradient technique for controllable rewarding. This approach goes
beyond the traditional heuristics or rule-based augmentation methods and is not
constrained to generate predefined patterns of swapping/replacing words. Our
experimental results show its effectiveness compared with a fully trained QR
baseline and demonstrate its potential application in boosting the QR
performance on low-resource domains or locales.
| 2,020 |
Computation and Language
|
BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using
Chemical Structure Information
|
Traditional biomedical version of embeddings obtained from pre-trained
language models have recently shown state-of-the-art results for relation
extraction (RE) tasks in the medical domain. In this paper, we explore how to
incorporate domain knowledge, available in the form of molecular structure of
drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a
method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the
rich chemical structure of drugs along with off-the-shelf domain-specific
BioBERT embedding-based RE architecture. Experiments conducted on the
DDIExtraction 2013 corpus clearly indicate that this strategy improves other
strong baselines architectures by 3.4\% macro F1-score.
| 2,020 |
Computation and Language
|
A Distributional Approach to Controlled Text Generation
|
We propose a Distributional Approach for addressing Controlled Text
Generation from pre-trained Language Models (LMs). This approach permits to
specify, in a single formal framework, both "pointwise" and "distributional"
constraints over the target LM -- to our knowledge, the first model with such
generality -- while minimizing KL divergence from the initial LM distribution.
The optimal target distribution is then uniquely determined as an explicit EBM
(Energy-Based Model) representation. From that optimal representation we then
train a target controlled Autoregressive LM through an adaptive distributional
variant of Policy Gradient. We conduct a first set of experiments over
pointwise constraints showing the advantages of our approach over a set of
baselines, in terms of obtaining a controlled LM balancing constraint
satisfaction with divergence from the initial LM. We then perform experiments
over distributional constraints, a unique feature of our approach,
demonstrating its potential as a remedy to the problem of Bias in Language
Models. Through an ablation study, we show the effectiveness of our adaptive
technique for obtaining faster convergence. (Code available at
https://github.com/naver/gdc)
| 2,021 |
Computation and Language
|
Subword Sampling for Low Resource Word Alignment
|
Annotation projection is an important area in NLP that can greatly contribute
to creating language resources for low-resource languages. Word alignment plays
a key role in this setting. However, most of the existing word alignment
methods are designed for a high resource setting in machine translation where
millions of parallel sentences are available. This amount reduces to a few
thousands of sentences when dealing with low-resource languages failing the
existing established IBM models. In this paper, we propose subword
sampling-based alignment of text units. This method's hypothesis is that the
aggregation of different granularities of text for certain language pairs can
help word-level alignment. For certain languages for which gold-standard
alignments exist, we propose an iterative Bayesian optimization framework to
optimize selecting possible subwords from the space of possible subword
representations of the source and target sentences. We show that the subword
sampling method consistently outperforms word-level alignment on six language
pairs: English-German, English-French, English-Romanian, English-Persian,
English-Hindi, and English-Inuktitut. In addition, we show that the
hyperparameters learned for certain language pairs can be applied to other
languages at no supervision and consistently improve the alignment results. We
observe that using $5K$ parallel sentences together with our proposed subword
sampling approach, we obtain similar F1 scores to the use of $100K$'s of
parallel sentences in existing word-level fast-align/eflomal alignment methods.
| 2,021 |
Computation and Language
|
SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation
Count Prediction
|
Predicting the number of citations of scholarly documents is an upcoming task
in scholarly document processing. Besides the intrinsic merit of this
information, it also has a wider use as an imperfect proxy for quality which
has the advantage of being cheaply available for large volumes of scholarly
documents. Previous work has dealt with number of citations prediction with
relatively small training data sets, or larger datasets but with short,
incomplete input text. In this work we leverage the open access ACL Anthology
collection in combination with the Semantic Scholar bibliometric database to
create a large corpus of scholarly documents with associated citation
information and we propose a new citation prediction model called SChuBERT. In
our experiments we compare SChuBERT with several state-of-the-art citation
prediction models and show that it outperforms previous methods by a large
margin. We also show the merit of using more training data and longer input for
number of citations prediction.
| 2,020 |
Computation and Language
|
Acronym Identification and Disambiguation Shared Tasks for Scientific
Document Understanding
|
Acronyms are the short forms of longer phrases and they are frequently used
in writing, especially scholarly writing, to save space and facilitate the
communication of information. As such, every text understanding tool should be
capable of recognizing acronyms in text (i.e., acronym identification) and also
finding their correct meaning (i.e., acronym disambiguation). As most of the
prior works on these tasks are restricted to the biomedical domain and use
unsupervised methods or models trained on limited datasets, they fail to
perform well for scientific document understanding. To push forward research in
this direction, we have organized two shared task for acronym identification
and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU,
respectively. The two shared tasks have attracted 52 and 43 participants,
respectively. While the submitted systems make substantial improvements
compared to the existing baselines, there are still far from the human-level
performance. This paper reviews the two shared tasks and the prominent
participating systems for each of them.
| 2,021 |
Computation and Language
|
Semi-Supervised Disentangled Framework for Transferable Named Entity
Recognition
|
Named entity recognition (NER) for identifying proper nouns in unstructured
text is one of the most important and fundamental tasks in natural language
processing. However, despite the widespread use of NER models, they still
require a large-scale labeled data set, which incurs a heavy burden due to
manual annotation. Domain adaptation is one of the most promising solutions to
this problem, where rich labeled data from the relevant source domain are
utilized to strengthen the generalizability of a model based on the target
domain. However, the mainstream cross-domain NER models are still affected by
the following two challenges (1) Extracting domain-invariant information such
as syntactic information for cross-domain transfer. (2) Integrating
domain-specific information such as semantic information into the model to
improve the performance of NER. In this study, we present a semi-supervised
framework for transferable NER, which disentangles the domain-invariant latent
variables and domain-specific latent variables. In the proposed framework, the
domain-specific information is integrated with the domain-specific latent
variables by using a domain predictor. The domain-specific and domain-invariant
latent variables are disentangled using three mutual information regularization
terms, i.e., maximizing the mutual information between the domain-specific
latent variables and the original embedding, maximizing the mutual information
between the domain-invariant latent variables and the original embedding, and
minimizing the mutual information between the domain-specific and
domain-invariant latent variables. Extensive experiments demonstrated that our
model can obtain state-of-the-art performance with cross-domain and
cross-lingual NER benchmark data sets.
| 2,020 |
Computation and Language
|
Improved Biomedical Word Embeddings in the Transformer Era
|
Biomedical word embeddings are usually pre-trained on free text corpora with
neural methods that capture local and global distributional properties. They
are leveraged in downstream tasks using various neural architectures that are
designed to optimize task-specific objectives that might further tune such
embeddings. Since 2018, however, there is a marked shift from these static
embeddings to contextual embeddings motivated by language models (e.g., ELMo,
transformers such as BERT, and ULMFiT). These dynamic embeddings have the added
benefit of being able to distinguish homonyms and acronyms given their context.
However, static embeddings are still relevant in low resource settings (e.g.,
smart devices, IoT elements) and to study lexical semantics from a
computational linguistics perspective. In this paper, we jointly learn word and
concept embeddings by first using the skip-gram method and further fine-tuning
them with correlational information manifesting in co-occurring Medical Subject
Heading (MeSH) concepts in biomedical citations. This fine-tuning is
accomplished with the BERT transformer architecture in the two-sentence input
mode with a classification objective that captures MeSH pair co-occurrence. In
essence, we repurpose a transformer architecture (typically used to generate
dynamic embeddings) to improve static embeddings using concept correlations. We
conduct evaluations of these tuned static embeddings using multiple datasets
for word relatedness developed by previous efforts. Without selectively culling
concepts and terms (as was pursued by previous efforts), we believe we offer
the most exhaustive evaluation of static embeddings to date with clear
performance improvements across the board. We provide our code and embeddings
for public use for downstream applications and research endeavors:
https://github.com/bionlproc/BERT-CRel-Embeddings
| 2,021 |
Computation and Language
|
Recognizing Emotion Cause in Conversations
|
We address the problem of recognizing emotion cause in conversations, define
two novel sub-tasks of this problem, and provide a corresponding dialogue-level
dataset, along with strong Transformer-based baselines. The dataset is
available at https://github.com/declare-lab/RECCON.
Introduction: Recognizing the cause behind emotions in text is a fundamental
yet under-explored area of research in NLP. Advances in this area hold the
potential to improve interpretability and performance in affect-based models.
Identifying emotion causes at the utterance level in conversations is
particularly challenging due to the intermingling dynamics among the
interlocutors.
Method: We introduce the task of Recognizing Emotion Cause in CONversations
with an accompanying dataset named RECCON, containing over 1,000 dialogues and
10,000 utterance cause-effect pairs. Furthermore, we define different cause
types based on the source of the causes, and establish strong Transformer-based
baselines to address two different sub-tasks on this dataset: causal span
extraction and causal emotion entailment.
Result: Our Transformer-based baselines, which leverage contextual
pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art
emotion cause extraction approaches
Conclusion: We introduce a new task highly relevant for (explainable)
emotion-aware artificial intelligence: recognizing emotion cause in
conversations, provide a new highly challenging publicly available
dialogue-level dataset for this task, and give strong baseline results on this
dataset.
| 2,021 |
Computation and Language
|
Undivided Attention: Are Intermediate Layers Necessary for BERT?
|
In recent times, BERT-based models have been extremely successful in solving
a variety of natural language processing (NLP) tasks such as reading
comprehension, natural language inference, sentiment analysis, etc. All
BERT-based architectures have a self-attention block followed by a block of
intermediate layers as the basic building component. However, a strong
justification for the inclusion of these intermediate layers remains missing in
the literature. In this work we investigate the importance of intermediate
layers on the overall network performance of downstream tasks. We show that
reducing the number of intermediate layers and modifying the architecture for
BERT-BASE results in minimal loss in fine-tuning accuracy for downstream tasks
while decreasing the number of parameters and training time of the model.
Additionally, we use centered kernel alignment and probing linear classifiers
to gain insight into our architectural modifications and justify that removal
of intermediate layers has little impact on the fine-tuned accuracy.
| 2,023 |
Computation and Language
|
Adversarial Meta Sampling for Multilingual Low-Resource Speech
Recognition
|
Low-resource automatic speech recognition (ASR) is challenging, as the
low-resource target language data cannot well train an ASR model. To solve this
issue, meta-learning formulates ASR for each source language into many small
ASR tasks and meta-learns a model initialization on all tasks from different
source languages to access fast adaptation on unseen target languages. However,
for different source languages, the quantity and difficulty vary greatly
because of their different data scales and diverse phonological systems, which
leads to task-quantity and task-difficulty imbalance issues and thus a failure
of multilingual meta-learning ASR (MML-ASR). In this work, we solve this
problem by developing a novel adversarial meta sampling (AMS) approach to
improve MML-ASR. When sampling tasks in MML-ASR, AMS adaptively determines the
task sampling probability for each source language. Specifically, for each
source language, if the query loss is large, it means that its tasks are not
well sampled to train ASR model in terms of its quantity and difficulty and
thus should be sampled more frequently for extra learning. Inspired by this
fact, we feed the historical task query loss of all source language domain into
a network to learn a task sampling policy for adversarially increasing the
current query loss of MML-ASR. Thus, the learnt task sampling policy can master
the learning situation of each language and thus predicts good task sampling
probability for each language for more effective learning. Finally, experiment
results on two multilingual datasets show significant performance improvement
when applying our AMS on MML-ASR, and also demonstrate the applicability of AMS
to other low-resource speech tasks and transfer learning ASR approaches.
| 2,021 |
Computation and Language
|
Few-Shot Text Generation with Pattern-Exploiting Training
|
Providing pretrained language models with simple task descriptions in natural
language enables them to solve some tasks in a fully unsupervised fashion.
Moreover, when combined with regular learning from examples, this idea yields
impressive few-shot results for a wide range of text classification tasks. It
is also a promising direction to improve data efficiency in generative
settings, but there are several challenges to using a combination of task
descriptions and example-based learning for text generation. In particular, it
is crucial to find task descriptions that are easy to understand for the
pretrained model and to ensure that it actually makes good use of them;
furthermore, effective measures against overfitting have to be implemented. In
this paper, we show how these challenges can be tackled: We introduce GenPET, a
method for text generation that is based on pattern-exploiting training, a
recent approach for combining textual instructions with supervised learning
that only works for classification tasks. On several summarization and headline
generation datasets, GenPET gives consistent improvements over strong baselines
in few-shot settings.
| 2,021 |
Computation and Language
|
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with
Copy Mechanism for Task-Oriented Dialogue Systems
|
Task-oriented conversational modeling with unstructured knowledge access, as
track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to
build a system to generate response given dialogue history and knowledge
access. This challenge can be separated into three subtasks, (1)
knowledge-seeking turn detection, (2) knowledge selection, and (3)
knowledge-grounded response generation. We use pre-trained language models,
ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1
and 2, the coarse-grained information like domain and entity are used to
enhance knowledge usage. For subtask 3, we use a latent variable to encode
dialog history and selected knowledge better and generate responses combined
with copy mechanism. Meanwhile, some useful post-processing strategies are
performed on the model's final output to make further knowledge usage in the
generation task. As shown in released evaluation results, our proposed system
ranks second under objective metrics and ranks fourth under human metrics.
| 2,020 |
Computation and Language
|
A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring
of Answer Transcriptions in Video Job Interviews
|
We address the task of automatically scoring the competency of candidates
based on textual features, from the automatic speech recognition (ASR)
transcriptions in the asynchronous video job interview (AVI). The key challenge
is how to construct the dependency relation between questions and answers, and
conduct the semantic level interaction for each question-answer (QA) pair.
However, most of the recent studies in AVI focus on how to represent questions
and answers better, but ignore the dependency information and interaction
between them, which is critical for QA evaluation. In this work, we propose a
Hierarchical Reasoning Graph Neural Network (HRGNN) for the automatic
assessment of question-answer pairs. Specifically, we construct a
sentence-level relational graph neural network to capture the dependency
information of sentences in or between the question and the answer. Based on
these graphs, we employ a semantic-level reasoning graph attention network to
model the interaction states of the current QA session. Finally, we propose a
gated recurrent unit encoder to represent the temporal question-answer pairs
for the final prediction. Empirical results conducted on CHNAT (a real-world
dataset) validate that our proposed model significantly outperforms
text-matching based benchmark models. Ablation studies and experimental results
with 10 random seeds also show the effectiveness and stability of our models.
| 2,020 |
Computation and Language
|
g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning
for COVID-19 Fake News Detection
|
The COVID-19 pandemic has had a huge impact on various areas of human life.
Hence, the coronavirus pandemic and its consequences are being actively
discussed on social media. However, not all social media posts are truthful.
Many of them spread fake news that cause panic among readers, misinform people
and thus exacerbate the effect of the pandemic. In this paper, we present our
results at the Constraint@AAAI2021 Shared Task: COVID-19 Fake News Detection in
English. In particular, we propose our approach using the transformer-based
ensemble of COVID-Twitter-BERT (CT-BERT) models. We describe the models used,
the ways of text preprocessing and adding extra data. As a result, our best
model achieved the weighted F1-score of 98.69 on the test set (the first place
in the leaderboard) of this shared task that attracted 166 submitted teams in
total.
| 2,021 |
Computation and Language
|
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue
Generation
|
Human doctors with well-structured medical knowledge can diagnose a disease
merely via a few conversations with patients about symptoms. In contrast,
existing knowledge-grounded dialogue systems often require a large number of
dialogue instances to learn as they fail to capture the correlations between
different diseases and neglect the diagnostic experience shared among them. To
address this issue, we propose a more natural and practical paradigm, i.e.,
low-resource medical dialogue generation, which can transfer the diagnostic
experience from source diseases to target ones with a handful of data for
adaptation. It is capitalized on a commonsense knowledge graph to characterize
the prior disease-symptom relations. Besides, we develop a Graph-Evolving
Meta-Learning (GEML) framework that learns to evolve the commonsense graph for
reasoning disease-symptom correlations in a new disease, which effectively
alleviates the needs of a large number of dialogues. More importantly, by
dynamically evolving disease-symptom graphs, GEML also well addresses the
real-world challenges that the disease-symptom correlations of each disease may
vary or evolve along with more diagnostic cases. Extensive experiment results
on the CMDD dataset and our newly-collected Chunyu dataset testify the
superiority of our approach over state-of-the-art approaches. Besides, our GEML
can generate an enriched dialogue-sensitive knowledge graph in an online
manner, which could benefit other tasks grounded on knowledge graph.
| 2,020 |
Computation and Language
|
Pre-Training a Language Model Without Human Language
|
In this paper, we study how the intrinsic nature of pre-training data
contributes to the fine-tuned downstream performance. To this end, we pre-train
different transformer-based masked language models on several corpora with
certain features, and we fine-tune those language models on GLUE benchmarks. We
find that models pre-trained on unstructured data beat those trained directly
from scratch on downstream tasks. Our results also show that pre-training on
structured data does not always make the model acquire ability that can be
transferred to natural language downstream tasks. To our great astonishment, we
uncover that pre-training on certain non-human language data gives GLUE
performance close to performance pre-trained on another non-English language.
| 2,020 |
Computation and Language
|
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting
Incongruity-Based Features for Humor Recognition
|
Humor recognition has been widely studied as a text classification problem
using data-driven approaches. However, most existing work does not examine the
actual joke mechanism to understand humor. We break down any joke into two
distinct components: the set-up and the punchline, and further explore the
special relationship between them. Inspired by the incongruity theory of humor,
we model the set-up as the part developing semantic uncertainty, and the
punchline disrupting audience expectations. With increasingly powerful language
models, we were able to feed the set-up along with the punchline into the GPT-2
language model, and calculate the uncertainty and surprisal values of the
jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found
that these two features have better capabilities of telling jokes from
non-jokes, compared with existing baselines.
| 2,021 |
Computation and Language
|
Domain Adaptation of NMT models for English-Hindi Machine Translation
Task at AdapMT ICON 2020
|
Recent advancements in Neural Machine Translation (NMT) models have proved to
produce a state of the art results on machine translation for low resource
Indian languages. This paper describes the neural machine translation systems
for the English-Hindi language presented in AdapMT Shared Task ICON 2020. The
shared task aims to build a translation system for Indian languages in specific
domains like Artificial Intelligence (AI) and Chemistry using a small in-domain
parallel corpus. We evaluated the effectiveness of two popular NMT models i.e,
LSTM, and Transformer architectures for the English-Hindi machine translation
task based on BLEU scores. We train these models primarily using the out of
domain data and employ simple domain adaptation techniques based on the
characteristics of the in-domain dataset. The fine-tuning and mixed-domain data
approaches are used for domain adaptation. Our team was ranked first in the
chemistry and general domain En-Hi translation task and second in the AI domain
En-Hi translation task.
| 2,020 |
Computation and Language
|
Applying Wav2vec2.0 to Speech Recognition in Various Low-resource
Languages
|
There are several domains that own corresponding widely used feature
extractors, such as ResNet, BERT, and GPT-x. These models are usually
pre-trained on large amounts of unlabeled data by self-supervision and can be
effectively applied to downstream tasks. In the speech domain, wav2vec2.0
starts to show its powerful representation ability and feasibility of ultra-low
resource speech recognition on the Librispeech corpus, which belongs to the
audiobook domain. However, wav2vec2.0 has not been examined on real spoken
scenarios and languages other than English. To verify its universality over
languages, we apply pre-trained models to solve low-resource speech recognition
tasks in various spoken languages. We achieve more than 20% relative
improvements in six languages compared with previous work. Among these
languages, English achieves a gain of 52.4%. Moreover, using coarse-grained
modeling units, such as subword or character, achieves better results than
fine-grained modeling units, such as phone or letter.
| 2,021 |
Computation and Language
|
COVID-19 Emotion Monitoring as a Tool to Increase Preparedness for
Disease Outbreaks in Developing Regions
|
The COVID-19 pandemic brought many challenges, from hospital-occupation
management to lock-down mental-health repercussions such as anxiety or
depression. In this work, we present a solution for the later problem by
developing a Twitter emotion-monitor system based on a state-of-the-art
natural-language processing model. The system monitors six different emotions
on accounts in cities, as well as politicians and health-authorities Twitter
accounts. With an anonymous use of the emotion monitor, health authorities and
private health-insurance companies can develop strategies to tackle problems
such as suicide and clinical depression. The model chosen for such a task is a
Bidirectional-Encoder Representations from Transformers (BERT) pre-trained on a
Spanish corpus (BETO). The model performed well on a validation dataset. The
system is deployed online as part of a web application for simulation and data
analysis of COVID-19, in Colombia, available at
https://epidemiologia-matematica.org.
| 2,020 |
Computation and Language
|
Confronting Abusive Language Online: A Survey from the Ethical and Human
Rights Perspective
|
The pervasiveness of abusive content on the internet can lead to severe
psychological and physical harm. Significant effort in Natural Language
Processing (NLP) research has been devoted to addressing this problem through
abusive content detection and related sub-areas, such as the detection of hate
speech, toxicity, cyberbullying, etc. Although current technologies achieve
high classification performance in research studies, it has been observed that
the real-life application of this technology can cause unintended harms, such
as the silencing of under-represented groups. We review a large body of NLP
research on automatic abuse detection with a new focus on ethical challenges,
organized around eight established ethical principles: privacy, accountability,
safety and security, transparency and explainability, fairness and
non-discrimination, human control of technology, professional responsibility,
and promotion of human values. In many cases, these principles relate not only
to situational ethical codes, which may be context-dependent, but are in fact
connected to universal human rights, such as the right to privacy, freedom from
discrimination, and freedom of expression. We highlight the need to examine the
broad social impacts of this technology, and to bring ethical and human rights
considerations to every stage of the application life-cycle, from task
formulation and dataset design, to model training and evaluation, to
application deployment. Guided by these principles, we identify several
opportunities for rights-respecting, socio-technical solutions to detect and
confront online abuse, including `nudging', `quarantining', value sensitive
design, counter-narratives, style transfer, and AI-driven public education
applications.
| 2,021 |
Computation and Language
|
ActionBert: Leveraging User Actions for Semantic Understanding of User
Interfaces
|
As mobile devices are becoming ubiquitous, regularly interacting with a
variety of user interfaces (UIs) is a common aspect of daily life for many
people. To improve the accessibility of these devices and to enable their usage
in a variety of settings, building models that can assist users and accomplish
tasks through the UI is vitally important. However, there are several
challenges to achieve this. First, UI components of similar appearance can have
different functionalities, making understanding their function more important
than just analyzing their appearance. Second, domain-specific features like
Document Object Model (DOM) in web pages and View Hierarchy (VH) in mobile
applications provide important signals about the semantics of UI elements, but
these features are not in a natural language format. Third, owing to a large
diversity in UIs and absence of standard DOM or VH representations, building a
UI understanding model with high coverage requires large amounts of training
data.
Inspired by the success of pre-training based approaches in NLP for tackling
a variety of problems in a data-efficient way, we introduce a new pre-trained
UI representation model called ActionBert. Our methodology is designed to
leverage visual, linguistic and domain-specific features in user interaction
traces to pre-train generic feature representations of UIs and their
components. Our key intuition is that user actions, e.g., a sequence of clicks
on different UI components, reveals important information about their
functionality. We evaluate the proposed model on a wide variety of downstream
tasks, ranging from icon classification to UI component retrieval based on its
natural language description. Experiments show that the proposed ActionBert
model outperforms multi-modal baselines across all downstream tasks by up to
15.5%.
| 2,021 |
Computation and Language
|
Multi-Head Self-Attention with Role-Guided Masks
|
The state of the art in learning meaningful semantic representations of words
is the Transformer model and its attention mechanisms. Simply put, the
attention mechanisms learn to attend to specific parts of the input dispensing
recurrence and convolutions. While some of the learned attention heads have
been found to play linguistically interpretable roles, they can be redundant or
prone to errors. We propose a method to guide the attention heads towards roles
identified in prior work as important. We do this by defining role-specific
masks to constrain the heads to attend to specific parts of the input, such
that different heads are designed to play different roles. Experiments on text
classification and machine translation using 7 different datasets show that our
method outperforms competitive attention-based, CNN, and RNN baselines.
| 2,020 |
Computation and Language
|
Simple-QE: Better Automatic Quality Estimation for Text Simplification
|
Text simplification systems generate versions of texts that are easier to
understand for a broader audience. The quality of simplified texts is generally
estimated using metrics that compare to human references, which can be
difficult to obtain. We propose Simple-QE, a BERT-based quality estimation (QE)
model adapted from prior summarization QE work, and show that it correlates
well with human quality judgments. Simple-QE does not require human references,
which makes the model useful in a practical setting where users would need to
be informed about the quality of generated simplifications. We also show that
we can adapt this approach to accurately predict the complexity of
human-written texts.
| 2,020 |
Computation and Language
|
TicketTalk: Toward human-level performance with end-to-end,
transaction-based dialog systems
|
We present a data-driven, end-to-end approach to transaction-based dialog
systems that performs at near-human levels in terms of verbal response quality
and factual grounding accuracy. We show that two essential components of the
system produce these results: a sufficiently large and diverse, in-domain
labeled dataset, and a neural network-based, pre-trained model that generates
both verbal responses and API call predictions. In terms of data, we introduce
TicketTalk, a movie ticketing dialog dataset with 23,789 annotated
conversations. The movie ticketing conversations range from completely
open-ended and unrestricted to more structured, both in terms of their
knowledge base, discourse features, and number of turns. In qualitative human
evaluations, model-generated responses trained on just 10,000 TicketTalk
dialogs were rated to "make sense" 86.5 percent of the time, almost the same as
human responses in the same contexts. Our simple, API-focused annotation schema
results in a much easier labeling task making it faster and more cost
effective. It is also the key component for being able to predict API calls
accurately. We handle factual grounding by incorporating API calls in the
training data, allowing our model to learn which actions to take and when.
Trained on the same 10,000-dialog set, the model's API call predictions were
rated to be correct 93.9 percent of the time in our evaluations, surpassing the
ratings for the corresponding human labels. We show how API prediction and
response generation scores improve as the dataset size incrementally increases
from 5000 to 21,000 dialogs. Our analysis also clearly illustrates the benefits
of pre-training. We are publicly releasing the TicketTalk dataset with this
paper to facilitate future work on transaction-based dialogs.
| 2,020 |
Computation and Language
|
Future-Guided Incremental Transformer for Simultaneous Translation
|
Simultaneous translation (ST) starts translations synchronously while reading
source sentences, and is used in many online scenarios. The previous wait-k
policy is concise and achieved good results in ST. However, wait-k policy faces
two weaknesses: low training speed caused by the recalculation of hidden states
and lack of future source information to guide training. For the low training
speed, we propose an incremental Transformer with an average embedding layer
(AEL) to accelerate the speed of calculation of the hidden states during
training. For future-guided training, we propose a conventional Transformer as
the teacher of the incremental Transformer, and try to invisibly embed some
future information in the model through knowledge distillation. We conducted
experiments on Chinese-English and German-English simultaneous translation
tasks and compared with the wait-k policy to evaluate the proposed method. Our
method can effectively increase the training speed by about 28 times on average
at different k and implicitly embed some predictive abilities in the model,
achieving better translation quality than wait-k baseline.
| 2,020 |
Computation and Language
|
Code Switching Language Model Using Monolingual Training Data
|
Training a code-switching (CS) language model using only monolingual data is
still an ongoing research problem. In this paper, a CS language model is
trained using only monolingual training data. As recurrent neural network (RNN)
models are best suited for predicting sequential data. In this work, an RNN
language model is trained using alternate batches from only monolingual English
and Spanish data and the perplexity of the language model is computed. From the
results, it is concluded that using alternate batches of monolingual data in
training reduced the perplexity of a CS language model. The results were
consistently improved using mean square error (MSE) in the output embeddings of
RNN based language model. By combining both methods, perplexity is reduced from
299.63 to 80.38. The proposed methods were comparable to the language model
fine tune with code-switch training data.
| 2,020 |
Computation and Language
|
Automated Lay Language Summarization of Biomedical Scientific Reviews
|
Health literacy has emerged as a crucial factor in making appropriate health
decisions and ensuring treatment outcomes. However, medical jargon and the
complex structure of professional language in this domain make health
information especially hard to interpret. Thus, there is an urgent unmet need
for automated methods to enhance the accessibility of the biomedical literature
to the general population. This problem can be framed as a type of translation
problem between the language of healthcare professionals, and that of the
general public. In this paper, we introduce the novel task of automated
generation of lay language summaries of biomedical scientific reviews, and
construct a dataset to support the development and evaluation of automated
methods through which to enhance the accessibility of the biomedical
literature. We conduct analyses of the various challenges in solving this task,
including not only summarization of the key points but also explanation of
background knowledge and simplification of professional language. We experiment
with state-of-the-art summarization models as well as several data augmentation
techniques, and evaluate their performance using both automated metrics and
human assessment. Results indicate that automatically generated summaries
produced using contemporary neural architectures can achieve promising quality
and readability as compared with reference summaries developed for the lay
public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score
of 13.30). We also discuss the limitations of the current attempt, providing
insights and directions for future work.
| 2,022 |
Computation and Language
|
Learning Dense Representations of Phrases at Scale
|
Open-domain question answering can be reformulated as a phrase retrieval
problem, without the need for processing documents on-demand during inference
(Seo et al., 2019). However, current phrase retrieval models heavily depend on
sparse representations and still underperform retriever-reader approaches. In
this work, we show for the first time that we can learn dense representations
of phrases alone that achieve much stronger performance in open-domain QA. We
present an effective method to learn phrase representations from the
supervision of reading comprehension tasks, coupled with novel negative
sampling methods. We also propose a query-side fine-tuning strategy, which can
support transfer learning and reduce the discrepancy between training and
inference. On five popular open-domain QA datasets, our model DensePhrases
improves over previous phrase retrieval models by 15%-25% absolute accuracy and
matches the performance of state-of-the-art retriever-reader models. Our model
is easy to parallelize due to pure dense representations and processes more
than 10 questions per second on CPUs. Finally, we directly use our pre-indexed
dense phrase representations for two slot filling tasks, showing the promise of
utilizing DensePhrases as a dense knowledge base for downstream tasks.
| 2,021 |
Computation and Language
|
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic
Parsing
|
We present BRIDGE, a powerful sequential architecture for modeling
dependencies between natural language questions and relational databases in
cross-DB semantic parsing. BRIDGE represents the question and DB schema in a
tagged sequence where a subset of the fields are augmented with cell values
mentioned in the question. The hybrid sequence is encoded by BERT with minimal
subsequent layers and the text-DB contextualization is realized via the
fine-tuned deep attention in BERT. Combined with a pointer-generator decoder
with schema-consistency driven search space pruning, BRIDGE attained
state-of-the-art performance on popular cross-DB text-to-SQL benchmarks, Spider
(71.1\% dev, 67.5\% test with ensemble model) and WikiSQL (92.6\% dev, 91.9\%
test). Our analysis shows that BRIDGE effectively captures the desired
cross-modal dependencies and has the potential to generalize to more text-DB
related tasks. Our implementation is available at
\url{https://github.com/salesforce/TabularSemanticParsing}.
| 2,021 |
Computation and Language
|
Negation in Cognitive Reasoning
|
Negation is both an operation in formal logic and in natural language by
which a proposition is replaced by one stating the opposite, as by the addition
of "not" or another negation cue. Treating negation in an adequate way is
required for cognitive reasoning, which aims at modeling the human ability to
draw meaningful conclusions despite incomplete and inconsistent knowledge. One
task of cognitive reasoning is answering questions given by sentences in
natural language. There are tools based on discourse representation theory to
convert sentences automatically into a formal logic representation, and
additional knowledge can be added using the predicate names in the formula and
knowledge databases. However, the knowledge in logic databases in practice
always is incomplete. Hence, forward reasoning of automated reasoning systems
alone does not suffice to derive answers to questions because, instead of
complete proofs, often only partial positive knowledge can be derived, while
negative knowledge is used only during the reasoning process. In consequence,
we aim at eliminating syntactic negation, strictly speaking, the negated event
or property. In this paper, we describe an effective procedure to determine the
negated event or property in order to replace it by its inverse. This lays the
basis of cognitive reasoning, employing both logic and machine learning for
general question answering. We evaluate our procedure by several benchmarks and
demonstrate its practical usefulness in our cognitive reasoning system.
| 2,021 |
Computation and Language
|
EmotionGIF-IITP-AINLPML: Ensemble-based Automated Deep Neural System for
predicting category(ies) of a GIF response
|
In this paper, we describe the systems submitted by our IITP-AINLPML team in
the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the
category(ies) of a GIF response for a given unlabelled tweet. For the round 1
phase of the task, we propose an attention-based Bi-directional GRU network
trained on both the tweet (text) and their replies (text wherever available)
and the given category(ies) for its GIF response. In the round 2 phase, we
build several deep neural-based classifiers for the task and report the final
predictions through a majority voting based ensemble technique. Our proposed
models attain the best Mean Recall (MR) scores of 52.92% and 53.80% in round 1
and round 2, respectively.
| 2,020 |
Computation and Language
|
Automatic Scansion of Spanish Poetry without Syllabification
|
In recent years, several systems of automated metric analysis of Spanish
poetry have emerged. These systems rely on complex methods of syllabification
and stress assignment, which use PoS-tagging libraries, whose computational
cost is high. This cost increases with the calculation of metric ambiguities.
Furthermore, they do not consider determining issues in syllabic count such as
the phenomena of compensation between hemistichs of verses of more than eleven
syllables. However, it is possible to carry out an informative and accurate
metric analysis without using these costly methods. We propose an algorithm
that performs accurate scansion (number of syllables, stress pattern and type
of verse) without syllabification. It addresses metric ambiguities and takes
into account the hemistichs compensation. Our algorithm outperforms the current
state of the art by 2% in fixed-metre poetry, and 25% in mixed-metre poetry. It
also runs 21 and 25 times faster, respectively. Finally, a desktop application
is offered as a tool for researchers of Spanish poetry.
| 2,020 |
Computation and Language
|
A Multimodal Framework for the Detection of Hateful Memes
|
An increasingly common expression of online hate speech is multimodal in
nature and comes in the form of memes. Designing systems to automatically
detect hateful content is of paramount importance if we are to mitigate its
undesirable effects on the society at large. The detection of multimodal hate
speech is an intrinsically difficult and open problem: memes convey a message
using both images and text and, hence, require multimodal reasoning and joint
visual and language understanding. In this work, we seek to advance this line
of research and develop a multimodal framework for the detection of hateful
memes. We improve the performance of existing multimodal approaches beyond
simple fine-tuning and, among others, show the effectiveness of upsampling of
contrastive examples to encourage multimodality and ensemble learning based on
cross-validation to improve robustness. We furthermore analyze model
misclassifications and discuss a number of hypothesis-driven augmentations and
their effects on performance, presenting important implications for future
research in the field. Our best approach comprises an ensemble of UNITER-based
models and achieves an AUROC score of 80.53, placing us 4th on phase 2 of the
2020 Hateful Memes Challenge organized by Facebook.
| 2,021 |
Computation and Language
|
Speech Synthesis as Augmentation for Low-Resource ASR
|
Speech synthesis might hold the key to low-resource speech recognition. Data
augmentation techniques have become an essential part of modern speech
recognition training. Yet, they are simple, naive, and rarely reflect
real-world conditions. Meanwhile, speech synthesis techniques have been rapidly
getting closer to the goal of achieving human-like speech. In this paper, we
investigate the possibility of using synthesized speech as a form of data
augmentation to lower the resources necessary to build a speech recognizer. We
experiment with three different kinds of synthesizers: statistical parametric,
neural, and adversarial. Our findings are interesting and point to new research
directions for the future.
| 2,020 |
Computation and Language
|
Disentangling semantics in language through VAEs and a certain
architectural choice
|
We present an unsupervised method to obtain disentangled representations of
sentences that single out semantic content. Using modified Transformers as
building blocks, we train a Variational Autoencoder to translate the sentence
to a fixed number of hierarchically structured latent variables. We study the
influence of each latent variable in generation on the dependency structure of
sentences, and on the predicate structure it yields when passed through an Open
Information Extraction model. Our model could separate verbs, subjects, direct
objects, and prepositional objects into latent variables we identified. We show
that varying the corresponding latent variables results in varying these
elements in sentences, and that swapping them between couples of sentences
leads to the expected partial semantic swap.
| 2,020 |
Computation and Language
|
Multi-modal Identification of State-Sponsored Propaganda on Social Media
|
The prevalence of state-sponsored propaganda on the Internet has become a
cause for concern in the recent years. While much effort has been made to
identify state-sponsored Internet propaganda, the problem remains far from
being solved because the ambiguous definition of propaganda leads to unreliable
data labelling, and the huge amount of potential predictive features causes the
models to be inexplicable. This paper is the first attempt to build a balanced
dataset for this task. The dataset is comprised of propaganda by three
different organizations across two time periods. A multi-model framework for
detecting propaganda messages solely based on the visual and textual content is
proposed which achieves a promising performance on detecting propaganda by the
three organizations both for the same time period (training and testing on data
from the same time period) (F1=0.869) and for different time periods (training
on past, testing on future) (F1=0.697). To reduce the influence of false
positive predictions, we change the threshold to test the relationship between
the false positive and true positive rates and provide explanations for the
predictions made by our models with visualization tools to enhance the
interpretability of our framework. Our new dataset and general framework
provide a strong benchmark for the task of identifying state-sponsored Internet
propaganda and point out a potential path for future work on this task.
| 2,021 |
Computation and Language
|
ProofWriter: Generating Implications, Proofs, and Abductive Statements
over Natural Language
|
Transformers have been shown to emulate logical deduction over natural
language theories (logical rules expressed in natural language), reliably
assigning true/false labels to candidate implications. However, their ability
to generate implications of a theory has not yet been demonstrated, and methods
for reconstructing proofs of answers are imperfect. In this work we show that a
generative model, called ProofWriter, can reliably generate both implications
of a theory and the natural language proof(s) that support them. In particular,
iterating a 1-step implication generator results in proofs that are highly
reliable, and represent actual model decisions (rather than post-hoc
rationalizations). On the RuleTaker dataset, the accuracy of ProofWriter's
proofs exceed previous methods by +9% absolute, and in a way that generalizes
to proof depths unseen in training and on out-of-domain problems. We also show
that generative techniques can perform a type of abduction with high precision:
Given a theory and an unprovable conclusion, identify a missing fact that
allows the conclusion to be proved, along with a proof. These results
significantly improve the viability of neural methods for systematically
reasoning over natural language.
| 2,021 |
Computation and Language
|
SubICap: Towards Subword-informed Image Captioning
|
Existing Image Captioning (IC) systems model words as atomic units in
captions and are unable to exploit the structural information in the words.
This makes representation of rare words very difficult and out-of-vocabulary
words impossible. Moreover, to avoid computational complexity, existing IC
models operate over a modest sized vocabulary of frequent words, such that the
identity of rare words is lost. In this work we address this common limitation
of IC systems in dealing with rare words in the corpora. We decompose words
into smaller constituent units 'subwords' and represent captions as a sequence
of subwords instead of words. This helps represent all words in the corpora
using a significantly lower subword vocabulary, leading to better parameter
learning. Using subword language modeling, our captioning system improves
various metric scores, with a training vocabulary size approximately 90% less
than the baseline and various state-of-the-art word-level models. Our
quantitative and qualitative results and analysis signify the efficacy of our
proposed approach.
| 2,021 |
Computation and Language
|
Cross-lingual Universal Dependency Parsing Only from One Monolingual
Treebank
|
Syntactic parsing is a highly linguistic processing task whose parser
requires training on treebanks from the expensive human annotation. As it is
unlikely to obtain a treebank for every human language, in this work, we
propose an effective cross-lingual UD parsing framework for transferring parser
from only one source monolingual treebank to any other target languages without
treebank available. To reach satisfactory parsing accuracy among quite
different languages, we introduce two language modeling tasks into dependency
parsing as multi-tasking. Assuming only unlabeled data from target languages
plus the source treebank can be exploited together, we adopt a self-training
strategy for further performance improvement in terms of our multi-task
framework. Our proposed cross-lingual parsers are implemented for English,
Chinese, and 22 UD treebanks. The empirical study shows that our cross-lingual
parsers yield promising results for all target languages, for the first time,
approaching the parser performance which is trained in its own target treebank.
| 2,021 |
Computation and Language
|
Gender Bias in Multilingual Neural Machine Translation: The Architecture
Matters
|
Multilingual Neural Machine Translation architectures mainly differ in the
amount of sharing modules and parameters among languages. In this paper, and
from an algorithmic perspective, we explore if the chosen architecture, when
trained with the same data, influences the gender bias accuracy. Experiments in
four language pairs show that Language-Specific encoders-decoders exhibit less
bias than the Shared encoder-decoder architecture. Further interpretability
analysis of source embeddings and the attention shows that, in the
Language-Specific case, the embeddings encode more gender information, and its
attention is more diverted. Both behaviors help in mitigating gender bias.
| 2,020 |
Computation and Language
|
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence
Refinement
|
When answering a question, people often draw upon their rich world knowledge
in addition to the particular context. While recent works retrieve supporting
facts/evidence from commonsense knowledge bases to supply additional
information to each question, there is still ample opportunity to advance it on
the quality of the evidence. It is crucial since the quality of the evidence is
the key to answering commonsense questions, and even determines the upper bound
on the QA systems performance. In this paper, we propose a recursive erasure
memory network (REM-Net) to cope with the quality improvement of evidence. To
address this, REM-Net is equipped with a module to refine the evidence by
recursively erasing the low-quality evidence that does not explain the question
answering. Besides, instead of retrieving evidence from existing knowledge
bases, REM-Net leverages a pre-trained generative model to generate candidate
evidence customized for the question. We conduct experiments on two commonsense
question answering datasets, WIQA and CosmosQA. The results demonstrate the
performance of REM-Net and show that the refined evidence is explainable.
| 2,021 |
Computation and Language
|
On the Granularity of Explanations in Model Agnostic NLP
Interpretability
|
Current methods for Black-Box NLP interpretability, like LIME or SHAP, are
based on altering the text to interpret by removing words and modeling the
Black-Box response. In this paper, we outline limitations of this approach when
using complex BERT-based classifiers: The word-based sampling produces texts
that are out-of-distribution for the classifier and further gives rise to a
high-dimensional search space, which can't be sufficiently explored when time
or computation power is limited. Both of these challenges can be addressed by
using segments as elementary building blocks for NLP interpretability. As
illustration, we show that the simple choice of sentences greatly improves on
both of these challenges. As a consequence, the resulting explainer attains
much better fidelity on a benchmark classification task.
| 2,022 |
Computation and Language
|
QUACKIE: A NLP Classification Task With Ground Truth Explanations
|
NLP Interpretability aims to increase trust in model predictions. This makes
evaluating interpretability approaches a pressing issue. There are multiple
datasets for evaluating NLP Interpretability, but their dependence on human
provided ground truths raises questions about their unbiasedness. In this work,
we take a different approach and formulate a specific classification task by
diverting question-answering datasets. For this custom classification task, the
interpretability ground-truth arises directly from the definition of the
classification problem. We use this method to propose a benchmark and lay the
groundwork for future research in NLP interpretability by evaluating a wide
range of current state of the art methods.
| 2,020 |
Computation and Language
|
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act
Recognition and Sentiment Classification
|
In a dialog system, dialog act recognition and sentiment classification are
two correlative tasks to capture speakers intentions, where dialog act and
sentiment can indicate the explicit and the implicit intentions separately. The
dialog context information (contextual information) and the mutual interaction
information are two key factors that contribute to the two related tasks.
Unfortunately, none of the existing approaches consider the two important
sources of information simultaneously. In this paper, we propose a
Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two
tasks. The core module is a proposed co-interactive graph interaction layer
where a cross-utterances connection and a cross-tasks connection are
constructed and iteratively updated with each other, achieving to consider the
two types of information simultaneously. Experimental results on two public
datasets show that our model successfully captures the two sources of
information and achieve the state-of-the-art performance.
In addition, we find that the contributions from the contextual and mutual
interaction information do not fully overlap with contextualized word
representations (BERT, Roberta, XLNet).
| 2,020 |
Computation and Language
|
A Context Aware Approach for Generating Natural Language Attacks
|
We study an important task of attacking natural language processing models in
a black box setting. We propose an attack strategy that crafts semantically
similar adversarial examples on text classification and entailment tasks. Our
proposed attack finds candidate words by considering the information of both
the original word and its surrounding context. It jointly leverages masked
language modelling and next sentence prediction for context understanding. In
comparison to attacks proposed in prior literature, we are able to generate
high quality adversarial examples that do significantly better both in terms of
success rate and word perturbation percentage.
| 2,020 |
Computation and Language
|
To what extent do human explanations of model behavior align with actual
model behavior?
|
Given the increasingly prominent role NLP models (will) play in our lives, it
is important for human expectations of model behavior to align with actual
model behavior. Using Natural Language Inference (NLI) as a case study, we
investigate the extent to which human-generated explanations of models'
inference decisions align with how models actually make these decisions. More
specifically, we define three alignment metrics that quantify how well natural
language explanations align with model sensitivity to input words, as measured
by integrated gradients. Then, we evaluate eight different models (the base and
large versions of BERT, RoBERTa and ELECTRA, as well as anRNN and bag-of-words
model), and find that the BERT-base model has the highest alignment with
human-generated explanations, for all alignment metrics. Focusing in on
transformers, we find that the base versions tend to have higher alignment with
human-generated explanations than their larger counterparts, suggesting that
increasing the number of model parameters leads, in some cases, to worse
alignment with human explanations. Finally, we find that a model's alignment
with human explanations is not predicted by the model's accuracy, suggesting
that accuracy and alignment are complementary ways to evaluate models.
| 2,021 |
Computation and Language
|
I like fish, especially dolphins: Addressing Contradictions in Dialogue
Modeling
|
To quantify how well natural language understanding models can capture
consistency in a general conversation, we introduce the DialoguE COntradiction
DEtection task (DECODE) and a new conversational dataset containing both
human-human and human-bot contradictory dialogues. We then compare a structured
utterance-based approach of using pre-trained Transformer models for
contradiction detection with the typical unstructured approach. Results reveal
that: (i) our newly collected dataset is notably more effective at providing
supervision for the dialogue contradiction detection task than existing NLI
data including those aimed to cover the dialogue domain; (ii) the structured
utterance-based approach is more robust and transferable on both analysis and
out-of-distribution dialogues than its unstructured counterpart. We also show
that our best contradiction detection model correlates well with human
judgments and further provide evidence for its usage in both automatically
evaluating and improving the consistency of state-of-the-art generative
chatbots.
| 2,020 |
Computation and Language
|
ThamizhiUDp: A Dependency Parser for Tamil
|
This paper describes how we developed a neural-based dependency parser,
namely ThamizhiUDp, which provides a complete pipeline for the dependency
parsing of the Tamil language text using Universal Dependency formalism. We
have considered the phases of the dependency parsing pipeline and identified
tools and resources in each of these phases to improve the accuracy and to
tackle data scarcity. ThamizhiUDp uses Stanza for tokenisation and
lemmatisation, ThamizhiPOSt and ThamizhiMorph for generating Part of Speech
(POS) and Morphological annotations, and uuparser with multilingual training
for dependency parsing. ThamizhiPOSt is our POS tagger, which is based on the
Stanza, trained with Amrita POS-tagged corpus. It is the current
state-of-the-art in Tamil POS tagging with an F1 score of 93.27. Our
morphological analyzer, ThamizhiMorph is a rule-based system with a very good
coverage of Tamil. Our dependency parser ThamizhiUDp was trained using
multilingual data. It shows a Labelled Assigned Score (LAS) of 62.39, 4 points
higher than the current best achieved for Tamil dependency parsing. Therefore,
we show that breaking up the dependency parsing pipeline to accommodate
existing tools and resources is a viable approach for low-resource languages.
| 2,020 |
Computation and Language
|
Why Neural Machine Translation Prefers Empty Outputs
|
We investigate why neural machine translation (NMT) systems assign high
probability to empty translations. We find two explanations. First, label
smoothing makes correct-length translations less confident, making it easier
for the empty translation to finally outscore them. Second, NMT systems use the
same, high-frequency EoS word to end all target sentences, regardless of
length. This creates an implicit smoothing that increases zero-length
translations. Using different EoS types in target sentences of different
lengths exposes and eliminates this implicit smoothing.
| 2,020 |
Computation and Language
|
Towards a Universal Continuous Knowledge Base
|
In artificial intelligence (AI), knowledge is the information required by an
intelligent system to accomplish tasks. While traditional knowledge bases use
discrete, symbolic representations, detecting knowledge encoded in the
continuous representations learned from data has received increasing attention
recently. In this work, we propose a method for building a continuous knowledge
base (CKB) that can store knowledge imported from multiple, diverse neural
networks. The key idea of our approach is to define an interface for each
neural network and cast knowledge transferring as a function simulation
problem. Experiments on text classification show promising results: the CKB
imports knowledge from a single model and then exports the knowledge to a new
model, achieving comparable performance with the original model. More
interesting, we import the knowledge from multiple models to the knowledge
base, from which the fused knowledge is exported back to a single model,
achieving a higher accuracy than the original model. With the CKB, it is also
easy to achieve knowledge distillation and transfer learning. Our work opens
the door to building a universal continuous knowledge base to collect, store,
and organize all continuous knowledge encoded in various neural networks
trained for different AI tasks.
| 2,021 |
Computation and Language
|
Contextual Temperature for Language Modeling
|
Temperature scaling has been widely used as an effective approach to control
the smoothness of a distribution, which helps the model performance in various
tasks. Current practices to apply temperature scaling assume either a fixed, or
a manually-crafted dynamically changing schedule. However, our studies indicate
that the individual optimal trajectory for each class can change with the
context. To this end, we propose contextual temperature, a generalized approach
that learns an optimal temperature trajectory for each vocabulary over the
context. Experimental results confirm that the proposed method significantly
improves state-of-the-art language models, achieving a perplexity of 55.31 and
62.89 on the test set of Penn Treebank and WikiText-2, respectively. In-depth
analyses show that the behaviour of the learned temperature schedules varies
dramatically by vocabulary, and that the optimal schedules help in controlling
the uncertainties. These evidences further justify the need for the proposed
method and its advantages over fixed temperature schedules.
| 2,020 |
Computation and Language
|
LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification
|
Given a natural language statement, how to verify its veracity against a
large-scale textual knowledge source like Wikipedia? Most existing neural
models make predictions without giving clues about which part of a false claim
goes wrong. In this paper, we propose LOREN, an approach for interpretable fact
verification. We decompose the verification of the whole claim at phrase-level,
where the veracity of the phrases serves as explanations and can be aggregated
into the final verdict according to logical rules. The key insight of LOREN is
to represent claim phrase veracity as three-valued latent variables, which are
regularized by aggregation logical rules. The final claim verification is based
on all latent variables. Thus, LOREN enjoys the additional benefit of
interpretability -- it is easy to explain how it reaches certain results with
claim phrase veracity. Experiments on a public fact verification benchmark show
that LOREN is competitive against previous approaches while enjoying the merit
of faithful and accurate interpretability. The resources of LOREN are available
at: https://github.com/jiangjiechen/LOREN.
| 2,022 |
Computation and Language
|
Fine-grained Emotion and Intent Learning in Movie Dialogues
|
We propose a novel large-scale emotional dialogue dataset, consisting of 1M
dialogues retrieved from the OpenSubtitles corpus and annotated with 32
emotions and 9 empathetic response intents using a BERT-based fine-grained
dialogue emotion classifier. This work explains the complex pipeline used to
preprocess movie subtitles and select good movie dialogues to annotate. We also
describe the semi-supervised learning process followed to train a fine-grained
emotion classifier to annotate these dialogues. Despite the large set of
labels, our dialogue emotion classifier achieved an accuracy of $65\%$ and was
used to annotate 1M emotional movie dialogues from OpenSubtitles. This scale of
emotional dialogue classification has never been attempted before, both in
terms of dataset size and fine-grained emotion and intent categories.
Visualization techniques used to analyze the quality of the resultant dataset
suggest that it conforms to the patterns of human social interaction.
| 2,020 |
Computation and Language
|
Inserting Information Bottlenecks for Attribution in Transformers
|
Pretrained transformers achieve the state of the art across tasks in natural
language processing, motivating researchers to investigate their inner
mechanisms. One common direction is to understand what features are important
for prediction. In this paper, we apply information bottlenecks to analyze the
attribution of each feature for prediction on a black-box model. We use BERT as
the example and evaluate our approach both quantitatively and qualitatively. We
show the effectiveness of our method in terms of attribution and the ability to
provide insight into how information flows through layers. We demonstrate that
our technique outperforms two competitive methods in degradation tests on four
datasets. Code is available at https://github.com/bazingagin/IBA.
| 2,020 |
Computation and Language
|
Learning Light-Weight Translation Models from Deep Transformer
|
Recently, deep models have shown tremendous improvements in neural machine
translation (NMT). However, systems of this kind are computationally expensive
and memory intensive. In this paper, we take a natural step towards learning
strong but light-weight NMT systems. We proposed a novel group-permutation
based knowledge distillation approach to compressing the deep Transformer model
into a shallow model. The experimental results on several benchmarks validate
the effectiveness of our method. Our compressed model is 8X shallower than the
deep model, with almost no loss in BLEU. To further enhance the teacher model,
we present a Skipping Sub-Layer method to randomly omit sub-layers to introduce
perturbation into training, which achieves a BLEU score of 30.63 on
English-German newstest2014. The code is publicly available at
https://github.com/libeineu/GPKD.
| 2,020 |
Computation and Language
|
My Teacher Thinks The World Is Flat! Interpreting Automatic Essay
Scoring Mechanism
|
Significant progress has been made in deep-learning based Automatic Essay
Scoring (AES) systems in the past two decades. However, little research has
been put to understand and interpret the black-box nature of these
deep-learning based scoring models. Recent work shows that automated scoring
systems are prone to even common-sense adversarial samples. Their lack of
natural language understanding capability raises questions on the models being
actively used by millions of candidates for life-changing decisions. With
scoring being a highly multi-modal task, it becomes imperative for scoring
models to be validated and tested on all these modalities. We utilize recent
advances in interpretability to find the extent to which features such as
coherence, content and relevance are important for automated scoring mechanisms
and why they are susceptible to adversarial samples. We find that the systems
tested consider essays not as a piece of prose having the characteristics of
natural flow of speech and grammatical structure, but as `word-soups' where a
few words are much more important than the other words. Removing the context
surrounding those few important words causes the prose to lose the flow of
speech and grammar, however has little impact on the predicted score. We also
find that since the models are not semantically grounded with world-knowledge
and common sense, adding false facts such as ``the world is flat'' actually
increases the score instead of decreasing it.
| 2,020 |
Computation and Language
|
An Embarrassingly Simple Model for Dialogue Relation Extraction
|
Dialogue relation extraction (RE) is to predict the relation type of two
entities mentioned in a dialogue. In this paper, we propose a simple yet
effective model named SimpleRE for the RE task. SimpleRE captures the
interrelations among multiple relations in a dialogue through a novel input
format named BERT Relation Token Sequence (BRS). In BRS, multiple [CLS] tokens
are used to capture possible relations between different pairs of entities
mentioned in the dialogue. A Relation Refinement Gate (RRG) is then designed to
extract relation-specific semantic representation in an adaptive manner.
Experiments on the DialogRE dataset show that SimpleRE achieves the best
performance, with much shorter training time. Further, SimpleRE outperforms all
direct baselines on sentence-level RE without using external resources.
| 2,023 |
Computation and Language
|
SG-Net: Syntax Guided Transformer for Language Representation
|
Understanding human language is one of the key themes of artificial
intelligence. For language representation, the capacity of effectively modeling
the linguistic knowledge from the detail-riddled and lengthy texts and getting
rid of the noises is essential to improve its performance. Traditional
attentive models attend to all words without explicit constraint, which results
in inaccurate concentration on some dispensable words. In this work, we propose
using syntax to guide the text modeling by incorporating explicit syntactic
constraints into attention mechanisms for better linguistically motivated word
representations. In detail, for self-attention network (SAN) sponsored
Transformer-based encoder, we introduce syntactic dependency of interest (SDOI)
design into the SAN to form an SDOI-SAN with syntax-guided self-attention.
Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the
SAN from the original Transformer encoder through a dual contextual
architecture for better linguistics inspired representation. The proposed
SG-Net is applied to typical Transformer encoders. Extensive experiments on
popular benchmark tasks, including machine reading comprehension, natural
language inference, and neural machine translation show the effectiveness of
the proposed SG-Net design.
| 2,021 |
Computation and Language
|
Adaptive Convolution for Semantic Role Labeling
|
Semantic role labeling (SRL) aims at elaborating the meaning of a sentence by
forming a predicate-argument structure. Recent researches depicted that the
effective use of syntax can improve SRL performance. However, syntax is a
complicated linguistic clue and is hard to be effectively applied in a
downstream task like SRL. This work effectively encodes syntax using adaptive
convolution which endows strong flexibility to existing convolutional networks.
The existing CNNs may help in encoding a complicated structure like syntax for
SRL, but it still has shortcomings. Contrary to traditional convolutional
networks that use same filters for different inputs, adaptive convolution uses
adaptively generated filters conditioned on syntactically informed inputs. We
achieve this with the integration of a filter generation network which
generates the input specific filters. This helps the model to focus on
important syntactic features present inside the input, thus enlarging the gap
between syntax-aware and syntax-agnostic SRL systems. We further study a
hashing technique to compress the size of the filter generation network for SRL
in terms of trainable parameters. Experiments on CoNLL-2009 dataset confirm
that the proposed model substantially outperforms most previous SRL systems for
both English and Chinese languages
| 2,020 |
Computation and Language
|
MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language
Understanding Pretraining
|
One of the biggest challenges that prohibit the use of many current NLP
methods in clinical settings is the availability of public datasets. In this
work, we present MeDAL, a large medical text dataset curated for abbreviation
disambiguation, designed for natural language understanding pre-training in the
medical domain. We pre-trained several models of common architectures on this
dataset and empirically showed that such pre-training leads to improved
performance and convergence speed when fine-tuning on downstream medical tasks.
| 2,020 |
Computation and Language
|
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
|
Humans have been shown to give contrastive explanations, which explain why an
observed event happened rather than some other counterfactual event (the
contrast case). Despite the influential role that contrastivity plays in how
humans explain, this property is largely missing from current methods for
explaining NLP models. We present Minimal Contrastive Editing (MiCE), a method
for producing contrastive explanations of model predictions in the form of
edits to inputs that change model outputs to the contrast case. Our experiments
across three tasks--binary sentiment classification, topic classification, and
multiple-choice question answering--show that MiCE is able to produce edits
that are not only contrastive, but also minimal and fluent, consistent with
human contrastive edits. We demonstrate how MiCE edits can be used for two use
cases in NLP system development--debugging incorrect model outputs and
uncovering dataset artifacts--and thereby illustrate that producing contrastive
explanations is a promising research direction for model interpretability.
| 2,021 |
Computation and Language
|
SMART: A Situation Model for Algebra Story Problems via Attributed
Grammar
|
Solving algebra story problems remains a challenging task in artificial
intelligence, which requires a detailed understanding of real-world situations
and a strong mathematical reasoning capability. Previous neural solvers of math
word problems directly translate problem texts into equations, lacking an
explicit interpretation of the situations, and often fail to handle more
sophisticated situations. To address such limits of neural solvers, we
introduce the concept of a \emph{situation model}, which originates from
psychology studies to represent the mental states of humans in problem-solving,
and propose \emph{SMART}, which adopts attributed grammar as the representation
of situation models for algebra story problems. Specifically, we first train an
information extraction module to extract nodes, attributes, and relations from
problem texts and then generate a parse graph based on a pre-defined attributed
grammar. An iterative learning strategy is also proposed to improve the
performance of SMART further. To rigorously study this task, we carefully
curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show
that the proposed model outperforms all previous neural solvers by a large
margin while preserving much better interpretability. To test these models'
generalization capability, we also design an out-of-distribution (OOD)
evaluation, in which problems are more complex than those in the training set.
Our model exceeds state-of-the-art models by 17\% in the OOD evaluation,
demonstrating its superior generalization ability.
| 2,021 |
Computation and Language
|
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation
|
Knowledge distillation is considered as a training and compression strategy
in which two neural networks, namely a teacher and a student, are coupled
together during training. The teacher network is supposed to be a trustworthy
predictor and the student tries to mimic its predictions. Usually, a student
with a lighter architecture is selected so we can achieve compression and yet
deliver high-quality results. In such a setting, distillation only happens for
final predictions whereas the student could also benefit from teacher's
supervision for internal components.
Motivated by this, we studied the problem of distillation for intermediate
layers. Since there might not be a one-to-one alignment between student and
teacher layers, existing techniques skip some teacher layers and only distill
from a subset of them. This shortcoming directly impacts quality, so we instead
propose a combinatorial technique which relies on attention. Our model fuses
teacher-side information and takes each layer's significance into
consideration, then performs distillation between combined teacher layers and
those of the student. Using our technique, we distilled a 12-layer BERT (Devlin
et al. 2019) into 6-, 4-, and 2-layer counterparts and evaluated them on GLUE
tasks (Wang et al. 2018). Experimental results show that our combinatorial
approach is able to outperform other existing techniques.
| 2,020 |
Computation and Language
|
Automatic Curriculum Learning With Over-repetition Penalty for Dialogue
Policy Learning
|
Dialogue policy learning based on reinforcement learning is difficult to be
applied to real users to train dialogue agents from scratch because of the high
cost. User simulators, which choose random user goals for the dialogue agent to
train on, have been considered as an affordable substitute for real users.
However, this random sampling method ignores the law of human learning, making
the learned dialogue policy inefficient and unstable. We propose a novel
framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which
replaces the traditional random sampling method with a teacher policy model to
realize the dialogue policy for automatic curriculum learning. The teacher
model arranges a meaningful ordered curriculum and automatically adjusts it by
monitoring the learning progress of the dialogue agent and the over-repetition
penalty without any requirement of prior knowledge. The learning progress of
the dialogue agent reflects the relationship between the dialogue agent's
ability and the sampled goals' difficulty for sample efficiency. The
over-repetition penalty guarantees the sampled diversity. Experiments show that
the ACL-DQN significantly improves the effectiveness and stability of dialogue
tasks with a statistically significant margin. Furthermore, the framework can
be further improved by equipping with different curriculum schedules, which
demonstrates that the framework has strong generalizability.
| 2,020 |
Computation and Language
|
Pivot Through English: Reliably Answering Multilingual Questions without
Document Retrieval
|
Existing methods for open-retrieval question answering in lower resource
languages (LRLs) lag significantly behind English. They not only suffer from
the shortcomings of non-English document retrieval, but are reliant on
language-specific supervision for either the task or translation. We formulate
a task setup more realistic to available resources, that circumvents document
retrieval to reliably transfer knowledge from English to lower resource
languages. Assuming a strong English question answering model or database, we
compare and analyze methods that pivot through English: to map foreign queries
to English and then English answers back to target language answers. Within
this task setup we propose Reranked Multilingual Maximal Inner Product Search
(RM-MIPS), akin to semantic similarity retrieval over the English training set
with reranking, which outperforms the strongest baselines by 2.7% on XQuAD and
6.2% on MKQA. Analysis demonstrates the particular efficacy of this strategy
over state-of-the-art alternatives in challenging settings: low-resource
languages, with extensive distractor data and query distribution misalignment.
Circumventing retrieval, our analysis shows this approach offers rapid answer
generation to almost any language off-the-shelf, without the need for any
additional training data in the target language.
| 2,021 |
Computation and Language
|
Syntax-Enhanced Pre-trained Model
|
We study the problem of leveraging the syntactic structure of text to enhance
pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of
text either in the pre-training stage or in the fine-tuning stage, so that they
suffer from discrepancy between the two stages. Such a problem would lead to
the necessity of having human-annotated syntactic information, which limits the
application of existing methods to broader scenarios. To address this, we
present a model that utilizes the syntax of text in both pre-training and
fine-tuning stages. Our model is based on Transformer with a syntax-aware
attention layer that considers the dependency tree of the text. We further
introduce a new pre-training task of predicting the syntactic distance among
tokens in the dependency tree. We evaluate the model on three downstream tasks,
including relation classification, entity typing, and question answering.
Results show that our model achieves state-of-the-art performance on six public
benchmark datasets. We have two major findings. First, we demonstrate that
infusing automatically produced syntax of text improves pre-trained models.
Second, global syntactic distances among tokens bring larger performance gains
compared to local head relations between contiguous tokens.
| 2,021 |
Computation and Language
|
Neural Text Generation with Artificial Negative Examples
|
Neural text generation models conditioning on given input (e.g. machine
translation and image captioning) are usually trained by maximum likelihood
estimation of target text. However, the trained models suffer from various
types of errors at inference time. In this paper, we propose to suppress an
arbitrary type of errors by training the text generation model in a
reinforcement learning framework, where we use a trainable reward function that
is capable of discriminating between references and sentences containing the
targeted type of errors. We create such negative examples by artificially
injecting the targeted errors to the references. In experiments, we focus on
two error types, repeated and dropped tokens in model-generated text. The
experimental results show that our method can suppress the generation errors
and achieve significant improvements on two machine translation and two image
captioning tasks.
| 2,020 |
Computation and Language
|
On Generating Extended Summaries of Long Documents
|
Prior work in document summarization has mainly focused on generating short
summaries of a document. While this type of summary helps get a high-level view
of a given document, it is desirable in some cases to know more detailed
information about its salient points that can't fit in a short summary. This is
typically the case for longer documents such as a research paper, legal
document, or a book. In this paper, we present a new method for generating
extended summaries of long papers. Our method exploits hierarchical structure
of the documents and incorporates it into an extractive summarization model
through a multi-task learning approach. We then present our results on three
long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method
outperforms or matches the performance of strong baselines. Furthermore, we
perform a comprehensive analysis over the generated results, shedding insights
on future research for long-form summary generation task. Our analysis shows
that our multi-tasking approach can adjust extraction probability distribution
to the favor of summary-worthy sentences across diverse sections. Our datasets,
and codes are publicly available at
https://github.com/Georgetown-IR-Lab/ExtendedSumm
| 2,020 |
Computation and Language
|
Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved
Transformer for Multi-Hop Explanation Ranking
|
Explainable question answering for science questions is a challenging task
that requires multi-hop inference over a large set of fact sentences. To
counter the limitations of methods that view each query-document pair in
isolation, we propose the LSTM-Interleaved Transformer which incorporates
cross-document interactions for improved multi-hop ranking. The LIT
architecture can leverage prior ranking positions in the re-ranking setting.
Our model is competitive on the current leaderboard for the TextGraphs 2020
shared task, achieving a test-set MAP of 0.5607, and would have gained third
place had we submitted before the competition deadline. Our code implementation
is made available at
https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020
| 2,020 |
Computation and Language
|
Towards Fully Automated Manga Translation
|
We tackle the problem of machine translation of manga, Japanese comics. Manga
translation involves two important problems in machine translation:
context-aware and multimodal translation. Since text and images are mixed up in
an unstructured fashion in Manga, obtaining context from the image is essential
for manga translation. However, it is still an open problem how to extract
context from image and integrate into MT models. In addition, corpus and
benchmarks to train and evaluate such model is currently unavailable. In this
paper, we make the following four contributions that establishes the foundation
of manga translation research. First, we propose multimodal context-aware
translation framework. We are the first to incorporate context information
obtained from manga image. It enables us to translate texts in speech bubbles
that cannot be translated without using context information (e.g., texts in
other speech bubbles, gender of speakers, etc.). Second, for training the
model, we propose the approach to automatic corpus construction from pairs of
original manga and their translations, by which large parallel corpus can be
constructed without any manual labeling. Third, we created a new benchmark to
evaluate manga translation. Finally, on top of our proposed methods, we devised
a first comprehensive system for fully automated manga translation.
| 2,021 |
Computation and Language
|
Panarchy: ripples of a boundary concept
|
How do social-ecological systems change over time? In 2002 Holling and
colleagues proposed the concept of Panarchy, which presented social-ecological
systems as an interacting set of adaptive cycles, each of which is produced by
the dynamic tensions between novelty and efficiency at multiple scales.
Initially introduced as a conceptual framework and set of metaphors, panarchy
has gained the attention of scholars across many disciplines and its ideas
continue to inspire further conceptual developments. Almost twenty years after
this concept was introduced we review how it has been used, tested, extended
and revised. We do this by combining qualitative methods and machine learning.
Document analysis was used to code panarchy features that are commonly used in
the scientific literature (N = 42), a qualitative analysis that was
complemented with topic modeling of 2177 documents. We find that the adaptive
cycle is the feature of panarchy that has attracted the most attention.
Challenges remain in empirically grounding the metaphor, but recent theoretical
and empirical work offers some avenues for future research.
| 2,020 |
Computation and Language
|
BURT: BERT-inspired Universal Representation from Learning Meaningful
Segment
|
Although pre-trained contextualized language models such as BERT achieve
significant performance on various downstream tasks, current language
representation still only focuses on linguistic objective at a specific
granularity, which may not applicable when multiple levels of linguistic units
are involved at the same time. Thus this work introduces and explores the
universal representation learning, i.e., embeddings of different levels of
linguistic unit in a uniform vector space. We present a universal
representation model, BURT (BERT-inspired Universal Representation from
learning meaningful segmenT), to encode different levels of linguistic unit
into the same vector space. Specifically, we extract and mask meaningful
segments based on point-wise mutual information (PMI) to incorporate different
granular objectives into the pre-training stage. We conduct experiments on
datasets for English and Chinese including the GLUE and CLUE benchmarks, where
our model surpasses its baselines and alternatives on a wide range of
downstream tasks. We present our approach of constructing analogy datasets in
terms of words, phrases and sentences and experiment with multiple
representation models to examine geometric properties of the learned vector
space through a task-independent evaluation. Finally, we verify the
effectiveness of our unified pre-training strategy in two real-world text
matching scenarios. As a result, our model significantly outperforms existing
information retrieval (IR) methods and yields universal representations that
can be directly applied to retrieval-based question-answering and natural
language generation tasks.
| 2,021 |
Computation and Language
|
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced
Bengali Language
|
The exponential growths of social media and micro-blogging sites not only
provide platforms for empowering freedom of expressions and individual voices,
but also enables people to express anti-social behaviour like online
harassment, cyberbullying, and hate speech. Numerous works have been proposed
to utilize textual data for social and anti-social behaviour analysis, by
predicting the contexts mostly for highly-resourced languages like English.
However, some languages are under-resourced, e.g., South Asian languages like
Bengali, that lack computational resources for accurate natural language
processing (NLP). In this paper, we propose an explainable approach for hate
speech detection from the under-resourced Bengali language, which we called
DeepHateExplainer. Bengali texts are first comprehensively preprocessed, before
classifying them into political, personal, geopolitical, and religious hates
using a neural ensemble method of transformer-based neural architectures (i.e.,
monolingual Bangla BERT-base, multilingual BERT-cased/uncased, and
XLM-RoBERTa). Important(most and least) terms are then identified using
sensitivity analysis and layer-wise relevance propagation(LRP), before
providing human-interpretable explanations. Finally, we compute
comprehensiveness and sufficiency scores to measure the quality of explanations
w.r.t faithfulness. Evaluations against machine learning~(linear and tree-based
models) and neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word
embeddings) baselines yield F1-scores of 78%, 91%, 89%, and 84%, for political,
personal, geopolitical, and religious hates, respectively, outperforming both
ML and DNN baselines.
| 2,021 |
Computation and Language
|
Universal Sentence Representation Learning with Conditional Masked
Language Model
|
This paper presents a novel training method, Conditional Masked Language
Modeling (CMLM), to effectively learn sentence representations on large scale
unlabeled corpora. CMLM integrates sentence representation learning into MLM
training by conditioning on the encoded vectors of adjacent sentences. Our
English CMLM model achieves state-of-the-art performance on SentEval, even
outperforming models learned using supervised signals. As a fully unsupervised
learning method, CMLM can be conveniently extended to a broad range of
languages and domains. We find that a multilingual CMLM model co-trained with
bitext retrieval (BR) and natural language inference (NLI) tasks outperforms
the previous state-of-the-art multilingual models by a large margin, e.g. 10%
improvement upon baseline models on cross-lingual semantic search. We explore
the same language bias of the learned representations, and propose a simple,
post-training and model agnostic approach to remove the language identifying
information from the representation while still retaining sentence semantics.
| 2,021 |
Computation and Language
|
A Paragraph-level Multi-task Learning Model for Scientific
Fact-Verification
|
Even for domain experts, it is a non-trivial task to verify a scientific
claim by providing supporting or refuting evidence rationales. The situation
worsens as misinformation is proliferated on social media or news websites,
manually or programmatically, at every moment. As a result, an automatic
fact-verification tool becomes crucial for combating the spread of
misinformation. In this work, we propose a novel, paragraph-level, multi-task
learning model for the SciFact task by directly computing a sequence of
contextualized sentence embeddings from a BERT model and jointly training the
model on rationale selection and stance prediction.
| 2,021 |
Computation and Language
|
Robust Dialogue Utterance Rewriting as Sequence Tagging
|
The task of dialogue rewriting aims to reconstruct the latest dialogue
utterance by copying the missing content from the dialogue context. Until now,
the existing models for this task suffer from the robustness issue, i.e.,
performances drop dramatically when testing on a different domain. We address
this robustness issue by proposing a novel sequence-tagging-based model so that
the search space is significantly reduced, yet the core of this task is still
well covered. As a common issue of most tagging models for text generation, the
model's outputs may lack fluency. To alleviate this issue, we inject the loss
signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge
improvements of our model over the current state-of-the-art systems on domain
transfer.
| 2,021 |
Computation and Language
|
YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain
Reviews
|
Current TSA evaluation in a cross-domain setup is restricted to the small set
of review domains available in existing datasets. Such an evaluation is
limited, and may not reflect true performance on sites like Amazon or Yelp that
host diverse reviews from many domains. To address this gap, we present YASO -
a new TSA evaluation dataset of open-domain user reviews. YASO contains 2,215
English sentences from dozens of review domains, annotated with target terms
and their sentiment. Our analysis verifies the reliability of these
annotations, and explores the characteristics of the collected data. Benchmark
results using five contemporary TSA systems show there is ample room for
improvement on this challenging new dataset. YASO is available at
https://github.com/IBM/yaso-tsa.
| 2,021 |
Computation and Language
|
Understanding and Improving Lexical Choice in Non-Autoregressive
Translation
|
Knowledge distillation (KD) is essential for training non-autoregressive
translation (NAT) models by reducing the complexity of the raw data with an
autoregressive teacher model. In this study, we empirically show that as a side
effect of this training, the lexical choice errors on low-frequency words are
propagated to the NAT model from the teacher model. To alleviate this problem,
we propose to expose the raw data to NAT models to restore the useful
information of low-frequency words, which are missed in the distilled data. To
this end, we introduce an extra Kullback-Leibler divergence term derived by
comparing the lexical choice of NAT model and that embedded in the raw data.
Experimental results across language pairs and model architectures demonstrate
the effectiveness and universality of the proposed approach. Extensive analyses
confirm our claim that our approach improves performance by reducing the
lexical choice errors on low-frequency words. Encouragingly, our approach
pushes the SOTA NAT performance on the WMT14 English-German and WMT16
Romanian-English datasets up to 27.8 and 33.8 BLEU points, respectively. The
source code will be released.
| 2,021 |
Computation and Language
|
Is human scoring the best criteria for summary evaluation?
|
Normally, summary quality measures are compared with quality scores produced
by human annotators. A higher correlation with human scores is considered to be
a fair indicator of a better measure. We discuss observations that cast doubt
on this view. We attempt to show a possibility of an alternative indicator.
Given a family of measures, we explore a criterion of selecting the best
measure not relying on correlations with human scores. Our observations for the
BLANC family of measures suggest that the criterion is universal across very
different styles of summaries.
| 2,021 |
Computation and Language
|
UniK-QA: Unified Representations of Structured and Unstructured
Knowledge for Open-Domain Question Answering
|
We study open-domain question answering with structured, unstructured and
semi-structured knowledge sources, including text, tables, lists and knowledge
bases. Departing from prior work, we propose a unifying approach that
homogenizes all sources by reducing them to text and applies the
retriever-reader model which has so far been limited to text sources only. Our
approach greatly improves the results on knowledge-base QA tasks by 11 points,
compared to latest graph-based methods. More importantly, we demonstrate that
our unified knowledge (UniK-QA) model is a simple and yet effective way to
combine heterogeneous sources of knowledge, advancing the state-of-the-art
results on two popular question answering benchmarks, NaturalQuestions and
WebQuestions, by 3.5 and 2.6 points, respectively.
The code of UniK-QA is available at:
https://github.com/facebookresearch/UniK-QA.
| 2,022 |
Computation and Language
|
Multiple Structural Priors Guided Self Attention Network for Language
Understanding
|
Self attention networks (SANs) have been widely utilized in recent NLP
studies. Unlike CNNs or RNNs, standard SANs are usually position-independent,
and thus are incapable of capturing the structural priors between sequences of
words. Existing studies commonly apply one single mask strategy on SANs for
incorporating structural priors while failing at modeling more abundant
structural information of texts. In this paper, we aim at introducing multiple
types of structural priors into SAN models, proposing the Multiple Structural
Priors Guided Self Attention Network (MS-SAN) that transforms different
structural priors into different attention heads by using a novel multi-mask
based multi-head attention mechanism. In particular, we integrate two
categories of structural priors, including the sequential order and the
relative position of words. For the purpose of capturing the latent
hierarchical structure of the texts, we extract these information not only from
the word contexts but also from the dependency syntax trees. Experimental
results on two tasks show that MS-SAN achieves significant improvements against
other strong baselines.
| 2,021 |
Computation and Language
|
Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous
Rendering Machines
|
End-to-end neural networks have achieved promising performances in natural
language generation (NLG). However, they are treated as black boxes and lack
interpretability. To address this problem, we propose a novel framework,
heterogeneous rendering machines (HRM), that interprets how neural generators
render an input dialogue act (DA) into an utterance. HRM consists of a renderer
set and a mode switcher. The renderer set contains multiple decoders that vary
in both structure and functionality. For every generation step, the mode
switcher selects an appropriate decoder from the renderer set to generate an
item (a word or a phrase). To verify the effectiveness of our method, we have
conducted extensive experiments on 5 benchmark datasets. In terms of automatic
metrics (e.g., BLEU), our model is competitive with the current
state-of-the-art method. The qualitative analysis shows that our model can
interpret the rendering process of neural generators well. Human evaluation
also confirms the interpretability of our proposed approach.
| 2,021 |
Computation and Language
|
Can You be More Social? Injecting Politeness and Positivity into
Task-Oriented Conversational Agents
|
Goal-oriented conversational agents are becoming prevalent in our daily
lives. For these systems to engage users and achieve their goals, they need to
exhibit appropriate social behavior as well as provide informative replies that
guide users through tasks. The first component of the research in this paper
applies statistical modeling techniques to understand conversations between
users and human agents for customer service. Analyses show that social language
used by human agents is associated with greater users' responsiveness and task
completion. The second component of the research is the construction of a
conversational agent model capable of injecting social language into an agent's
responses while still preserving content. The model uses a sequence-to-sequence
deep learning architecture, extended with a social language understanding
element. Evaluation in terms of content preservation and social language level
using both human judgment and automatic linguistic measures shows that the
model can generate responses that enable agents to address users' issues in a
more socially appropriate way.
| 2,021 |
Computation and Language
|
A Theoretical Analysis of the Repetition Problem in Text Generation
|
Text generation tasks, including translation, summarization, language models,
and etc. see rapid growth during recent years. Despite the remarkable
achievements, the repetition problem has been observed in nearly all text
generation models undermining the generation performance extensively. To solve
the repetition problem, many methods have been proposed, but there is no
existing theoretical analysis to show why this problem happens and how it is
resolved. In this paper, we propose a new framework for theoretical analysis
for the repetition problem. We first define the Average Repetition Probability
(ARP) to characterize the repetition problem quantitatively. Then, we conduct
an extensive analysis of the Markov generation model and derive several upper
bounds of the average repetition probability with intuitive understanding. We
show that most of the existing methods are essentially minimizing the upper
bounds explicitly or implicitly. Grounded on our theory, we show that the
repetition problem is, unfortunately, caused by the traits of our language
itself. One major reason is attributed to the fact that there exist too many
words predicting the same word as the subsequent word with high probability.
Consequently, it is easy to go back to that word and form repetitions and we
dub it as the high inflow problem. Furthermore, we derive a concentration bound
of the average repetition probability for a general generation model. Finally,
based on the theoretical upper bounds, we propose a novel rebalanced encoding
approach to alleviate the high inflow problem. The experimental results show
that our theoretical framework is applicable in general generation models and
our proposed rebalanced encoding approach alleviates the repetition problem
significantly. The source code of this paper can be obtained from
https://github.com/fuzihaofzh/repetition-problem-nlg.
| 2,021 |
Computation and Language
|
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