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The TechQA Dataset | We introduce TechQA, a domain-adaptation question answering dataset for the
technical support domain. The TechQA corpus highlights two real-world issues
from the automated customer support domain. First, it contains actual questions
posed by users on a technical forum, rather than questions generated
specifically for a competition or a task. Second, it has a real-world size --
600 training, 310 dev, and 490 evaluation question/answer pairs -- thus
reflecting the cost of creating large labeled datasets with actual data.
Consequently, TechQA is meant to stimulate research in domain adaptation rather
than being a resource to build QA systems from scratch. The dataset was
obtained by crawling the IBM Developer and IBM DeveloperWorks forums for
questions with accepted answers that appear in a published IBM Technote---a
technical document that addresses a specific technical issue. We also release a
collection of the 801,998 publicly available Technotes as of April 4, 2019 as a
companion resource that might be used for pretraining, to learn representations
of the IT domain language.
| 2,019 | Computation and Language |
Resurrecting Submodularity for Neural Text Generation | Submodularity is desirable for a variety of objectives in content selection
where the current neural encoder-decoder framework is inadequate. However, it
has so far not been explored in the neural encoder-decoder system for text
generation. In this work, we define diminishing attentions with submodular
functions and in turn, prove the submodularity of the effective neural
coverage. The greedy algorithm approximating the solution to the submodular
maximization problem is not suited to attention score optimization in
auto-regressive generation. Therefore instead of following how submodular
function has been widely used, we propose a simplified yet principled solution.
The resulting attention module offers an architecturally simple and empirically
effective method to improve the coverage of neural text generation. We run
experiments on three directed text generation tasks with different levels of
recovering rate, across two modalities, three different neural model
architectures and two training strategy variations. The results and analyses
demonstrate that our method generalizes well across these settings, produces
texts of good quality and outperforms state-of-the-art baselines.
| 2,020 | Computation and Language |
Why Do Masked Neural Language Models Still Need Common Sense Knowledge? | Currently, contextualized word representations are learned by intricate
neural network models, such as masked neural language models (MNLMs). The new
representations significantly enhanced the performance in automated question
answering by reading paragraphs. However, identifying the detailed knowledge
trained in the MNLMs is difficult owing to numerous and intermingled
parameters. This paper provides empirical but insightful analyses on the
pretrained MNLMs with respect to common sense knowledge. First, we propose a
test that measures what types of common sense knowledge do pretrained MNLMs
understand. From the test, we observed that MNLMs partially understand various
types of common sense knowledge but do not accurately understand the semantic
meaning of relations. In addition, based on the difficulty of the
question-answering task problems, we observed that pretrained MLM-based models
are still vulnerable to problems that require common sense knowledge. We also
experimentally demonstrated that we can elevate existing MNLM-based models by
combining knowledge from an external common sense repository.
| 2,019 | Computation and Language |
Neural Graph Embedding Methods for Natural Language Processing | Knowledge graphs are structured representations of facts in a graph, where
nodes represent entities and edges represent relationships between them. Recent
research has resulted in the development of several large KGs. However, all of
them tend to be sparse with very few facts per entity. In the first part of the
thesis, we propose two solutions to alleviate this problem: (1) KG
Canonicalization, i.e., identifying and merging duplicate entities in a KG, (2)
Relation Extraction which involves automating the process of extracting
semantic relationships between entities from unstructured text. Traditional
Neural Networks like CNNs and RNNs are constrained to handle Euclidean data.
However, graphs in Natural Language Processing (NLP) are prominent. Recently,
Graph Convolutional Networks (GCNs) have been proposed to address this
shortcoming and have been successfully applied for several problems. In the
second part of the thesis, we utilize GCNs for Document Timestamping problem
and for learning word embeddings using dependency context of a word instead of
sequential context. In this third part of the thesis, we address two
limitations of existing GCN models, i.e., (1) The standard neighborhood
aggregation scheme puts no constraints on the number of nodes that can
influence the representation of a target node. This leads to a noisy
representation of hub-nodes which coves almost the entire graph in a few hops.
(2) Most of the existing GCN models are limited to handle undirected graphs.
However, a more general and pervasive class of graphs are relational graphs
where each edge has a label and direction associated with it. Existing
approaches to handle such graphs suffer from over-parameterization and are
restricted to learning representation of nodes only.
| 2,020 | Computation and Language |
Contrastive Multi-document Question Generation | Multi-document question generation focuses on generating a question that
covers the common aspect of multiple documents. Such a model is useful in
generating clarifying options. However, a naive model trained only using the
targeted ("positive") document set may generate too generic questions that
cover a larger scope than delineated by the document set. To address this
challenge, we introduce the contrastive learning strategy where given
"positive" and "negative" sets of documents, we generate a question that is
closely related to the "positive" set but is far away from the "negative" set.
This setting allows generated questions to be more specific and related to the
target document set. To generate such specific questions, we propose
Multi-Source Coordinated Question Generator (MSCQG), a novel framework that
includes a supervised learning (SL) stage and a reinforcement learning (RL)
stage. In the SL stage, a single-document question generator is trained. In the
RL stage, a coordinator model is trained to find optimal attention weights to
align multiple single-document generators, by optimizing a reward designed to
promote specificity of generated questions. We also develop an effective
auxiliary objective, named Set-induced Contrastive Regularization (SCR) that
improves the coordinator's contrastive learning during the RL stage. We show
that our model significantly outperforms several strong baselines, as measured
by automatic metrics and human evaluation. The source repository is publicly
available at \url{www.github.com/woonsangcho/contrast_qgen}.
| 2,021 | Computation and Language |
Should All Cross-Lingual Embeddings Speak English? | Most of recent work in cross-lingual word embeddings is severely
Anglocentric. The vast majority of lexicon induction evaluation dictionaries
are between English and another language, and the English embedding space is
selected by default as the hub when learning in a multilingual setting. With
this work, however, we challenge these practices. First, we show that the
choice of hub language can significantly impact downstream lexicon induction
performance. Second, we both expand the current evaluation dictionary
collection to include all language pairs using triangulation, and also create
new dictionaries for under-represented languages. Evaluating established
methods over all these language pairs sheds light into their suitability and
presents new challenges for the field. Finally, in our analysis we identify
general guidelines for strong cross-lingual embeddings baselines, based on more
than just Anglocentric experiments.
| 2,020 | Computation and Language |
A Comprehensive Comparison of Machine Learning Based Methods Used in
Bengali Question Classification | QA classification system maps questions asked by humans to an appropriate
answer category. A sound question classification (QC) system model is the
pre-requisite of a sound QA system. This work demonstrates phases of assembling
a QA type classification model. We present a comprehensive comparison
(performance and computational complexity) among some machine learning based
approaches used in QC for Bengali language.
| 2,019 | Computation and Language |
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation | Advances in language modeling architectures and the availability of large
text corpora have driven progress in automatic text generation. While this
results in models capable of generating coherent texts, it also prompts models
to internalize social biases present in the training corpus. This paper aims to
quantify and reduce a particular type of bias exhibited by language models:
bias in the sentiment of generated text. Given a conditioning context (e.g., a
writing prompt) and a language model, we analyze if (and how) the sentiment of
the generated text is affected by changes in values of sensitive attributes
(e.g., country names, occupations, genders) in the conditioning context using a
form of counterfactual evaluation. We quantify sentiment bias by adopting
individual and group fairness metrics from the fair machine learning
literature, and demonstrate that large-scale models trained on two different
corpora (news articles, and Wikipedia) exhibit considerable levels of bias. We
then propose embedding and sentiment prediction-derived regularization on the
language model's latent representations. The regularizations improve fairness
metrics while retaining comparable levels of perplexity and semantic
similarity.
| 2,020 | Computation and Language |
Interactive Refinement of Cross-Lingual Word Embeddings | Cross-lingual word embeddings transfer knowledge between languages: models
trained on high-resource languages can predict in low-resource languages. We
introduce CLIME, an interactive system to quickly refine cross-lingual word
embeddings for a given classification problem. First, CLIME ranks words by
their salience to the downstream task. Then, users mark similarity between
keywords and their nearest neighbors in the embedding space. Finally, CLIME
updates the embeddings using the annotations. We evaluate CLIME on identifying
health-related text in four low-resource languages: Ilocano, Sinhalese,
Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word
semantics and have higher test accuracy than the original embeddings. CLIME
often improves accuracy faster than an active learning baseline and can be
easily combined with active learning to improve results.
| 2,021 | Computation and Language |
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning | Pretrained transformer-based language models have achieved state of the art
across countless tasks in natural language processing. These models are highly
expressive, comprising at least a hundred million parameters and a dozen
layers. Recent evidence suggests that only a few of the final layers need to be
fine-tuned for high quality on downstream tasks. Naturally, a subsequent
research question is, "how many of the last layers do we need to fine-tune?" In
this paper, we precisely answer this question. We examine two recent pretrained
language models, BERT and RoBERTa, across standard tasks in textual entailment,
semantic similarity, sentiment analysis, and linguistic acceptability. We vary
the number of final layers that are fine-tuned, then study the resulting change
in task-specific effectiveness. We show that only a fourth of the final layers
need to be fine-tuned to achieve 90% of the original quality. Surprisingly, we
also find that fine-tuning all layers does not always help.
| 2,019 | Computation and Language |
Relation Adversarial Network for Low Resource Knowledge Graph Completion | Knowledge Graph Completion (KGC) has been proposed to improve Knowledge
Graphs by filling in missing connections via link prediction or relation
extraction. One of the main difficulties for KGC is a low resource problem.
Previous approaches assume sufficient training triples to learn versatile
vectors for entities and relations, or a satisfactory number of labeled
sentences to train a competent relation extraction model. However, low resource
relations are very common in KGs, and those newly added relations often do not
have many known samples for training. In this work, we aim at predicting new
facts under a challenging setting where only limited training instances are
available. We propose a general framework called Weighted Relation Adversarial
Network, which utilizes an adversarial procedure to help adapt
knowledge/features learned from high resource relations to different but
related low resource relations. Specifically, the framework takes advantage of
a relation discriminator to distinguish between samples from different
relations, and help learn relation-invariant features more transferable from
source relations to target relations. Experimental results show that the
proposed approach outperforms previous methods regarding low resource settings
for both link prediction and relation extraction.
| 2,023 | Computation and Language |
Domain Robustness in Neural Machine Translation | Translating text that diverges from the training domain is a key challenge
for machine translation. Domain robustness---the generalization of models to
unseen test domains---is low for both statistical (SMT) and neural machine
translation (NMT). In this paper, we study the performance of SMT and NMT
models on out-of-domain test sets. We find that in unknown domains, SMT and NMT
suffer from very different problems: SMT systems are mostly adequate but not
fluent, while NMT systems are mostly fluent, but not adequate. For NMT, we
identify such hallucinations (translations that are fluent but unrelated to the
source) as a key reason for low domain robustness. To mitigate this problem, we
empirically compare methods that are reported to improve adequacy or in-domain
robustness in terms of their effectiveness at improving domain robustness. In
experiments on German to English OPUS data, and German to Romansh (a
low-resource setting) we find that several methods improve domain robustness.
While those methods do lead to higher BLEU scores overall, they only slightly
increase the adequacy of translations compared to SMT.
| 2,020 | Computation and Language |
Pretrained Language Models for Document-Level Neural Machine Translation | Previous work on document-level NMT usually focuses on limited contexts
because of degraded performance on larger contexts. In this paper, we
investigate on using large contexts with three main contributions: (1)
Different from previous work which pertrained models on large-scale
sentence-level parallel corpora, we use pretrained language models,
specifically BERT, which are trained on monolingual documents; (2) We propose
context manipulation methods to control the influence of large contexts, which
lead to comparable results on systems using small and large contexts; (3) We
introduce a multi-task training for regularization to avoid models overfitting
our training corpora, which further improves our systems together with a deeper
encoder. Experiments are conducted on the widely used IWSLT data sets with
three language pairs, i.e., Chinese--English, French--English and
Spanish--English. Results show that our systems are significantly better than
three previously reported document-level systems.
| 2,019 | Computation and Language |
Not Enough Data? Deep Learning to the Rescue! | Based on recent advances in natural language modeling and those in text
generation capabilities, we propose a novel data augmentation method for text
classification tasks. We use a powerful pre-trained neural network model to
artificially synthesize new labeled data for supervised learning. We mainly
focus on cases with scarce labeled data. Our method, referred to as
language-model-based data augmentation (LAMBADA), involves fine-tuning a
state-of-the-art language generator to a specific task through an initial
training phase on the existing (usually small) labeled data. Using the
fine-tuned model and given a class label, new sentences for the class are
generated. Our process then filters these new sentences by using a classifier
trained on the original data. In a series of experiments, we show that LAMBADA
improves classifiers' performance on a variety of datasets. Moreover, LAMBADA
significantly improves upon the state-of-the-art techniques for data
augmentation, specifically those applicable to text classification tasks with
little data.
| 2,019 | Computation and Language |
iSarcasm: A Dataset of Intended Sarcasm | We consider the distinction between intended and perceived sarcasm in the
context of textual sarcasm detection. The former occurs when an utterance is
sarcastic from the perspective of its author, while the latter occurs when the
utterance is interpreted as sarcastic by the audience. We show the limitations
of previous labelling methods in capturing intended sarcasm and introduce the
iSarcasm dataset of tweets labeled for sarcasm directly by their authors.
Examining the state-of-the-art sarcasm detection models on our dataset showed
low performance compared to previously studied datasets, which indicates that
these datasets might be biased or obvious and sarcasm could be a phenomenon
under-studied computationally thus far. By providing the iSarcasm dataset, we
aim to encourage future NLP research to develop methods for detecting sarcasm
in text as intended by the authors of the text, not as labeled under
assumptions that we demonstrate to be sub-optimal.
| 2,020 | Computation and Language |
A General Framework for Adaptation of Neural Machine Translation to
Simultaneous Translation | Despite the success of neural machine translation (NMT), simultaneous neural
machine translation (SNMT), the task of translating in real time before a full
sentence has been observed, remains challenging due to the syntactic structure
difference and simultaneity requirements. In this paper, we propose a general
framework for adapting neural machine translation to translate simultaneously.
Our framework contains two parts: prefix translation that utilizes a
consecutive NMT model to translate source prefixes and a stopping criterion
that determines when to stop the prefix translation. Experiments on three
translation corpora and two language pairs show the efficacy of the proposed
framework on balancing the quality and latency in adapting NMT to perform
simultaneous translation.
| 2,020 | Computation and Language |
Europarl-ST: A Multilingual Corpus For Speech Translation Of
Parliamentary Debates | Current research into spoken language translation (SLT),or speech-to-text
translation, is often hampered by the lack of specific data resources for this
task, as currently available SLT datasets are restricted to a limited set of
language pairs. In this paper we present Europarl-ST, a novel multilingual SLT
corpus containing paired audio-text samples for SLT from and into 6 European
languages, for a total of 30 different translation directions. This corpus has
been compiled using the debates held in the European Parliament in the period
between 2008 and 2012. This paper describes the corpus creation process and
presents a series of automatic speech recognition, machine translation and
spoken language translation experiments that highlight the potential of this
new resource. The corpus is released under a Creative Commons license and is
freely accessible and downloadable.
| 2,020 | Computation and Language |
Lipschitz Constrained Parameter Initialization for Deep Transformers | The Transformer translation model employs residual connection and layer
normalization to ease the optimization difficulties caused by its multi-layer
encoder/decoder structure. Previous research shows that even with residual
connection and layer normalization, deep Transformers still have difficulty in
training, and particularly Transformer models with more than 12 encoder/decoder
layers fail to converge. In this paper, we first empirically demonstrate that a
simple modification made in the official implementation, which changes the
computation order of residual connection and layer normalization, can
significantly ease the optimization of deep Transformers. We then compare the
subtle differences in computation order in considerable detail, and present a
parameter initialization method that leverages the Lipschitz constraint on the
initialization of Transformer parameters that effectively ensures training
convergence. In contrast to findings in previous research we further
demonstrate that with Lipschitz parameter initialization, deep Transformers
with the original computation order can converge, and obtain significant BLEU
improvements with up to 24 layers. In contrast to previous research which
focuses on deep encoders, our approach additionally enables Transformers to
also benefit from deep decoders.
| 2,020 | Computation and Language |
Controlled Crowdsourcing for High-Quality QA-SRL Annotation | Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an
attractive open and natural flavour of SRL, potentially attainable from laymen.
Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were
released. Trying to replicate the QA-SRL annotation for new texts, we found
that the resulting annotations were lacking in quality, particularly in
coverage, making them insufficient for further research and evaluation. In this
paper, we present an improved crowdsourcing protocol for complex semantic
annotation, involving worker selection and training, and a data consolidation
phase. Applying this protocol to QA-SRL yielded high-quality annotation with
drastically higher coverage, producing a new gold evaluation dataset. We
believe that our annotation protocol and gold standard will facilitate future
replicable research of natural semantic annotations.
| 2,020 | Computation and Language |
Char-RNN and Active Learning for Hashtag Segmentation | We explore the abilities of character recurrent neural network (char-RNN) for
hashtag segmentation. Our approach to the task is the following: we generate
synthetic training dataset according to frequent n-grams that satisfy
predefined morpho-syntactic patterns to avoid any manual annotation. The active
learning strategy limits the training dataset and selects informative training
subset. The approach does not require any language-specific settings and is
compared for two languages, which differ in inflection degree.
| 2,023 | Computation and Language |
Composing and Embedding the Words-as-Classifiers Model of Grounded
Semantics | The words-as-classifiers model of grounded lexical semantics learns a
semantic fitness score between physical entities and the words that are used to
denote those entities. In this paper, we explore how such a model can
incrementally perform composition and how the model can be unified with a
distributional representation. For the latter, we leverage the classifier
coefficients as an embedding. For composition, we leverage the underlying
mechanics of three different classifier types (i.e., logistic regression,
decision trees, and multi-layer perceptrons) to arrive at a several systematic
approaches to composition unique to each classifier including both denotational
and connotational methods of composition. We compare these approaches to each
other and to prior work in a visual reference resolution task using the refCOCO
dataset. Our results demonstrate the need to expand upon existing composition
strategies and bring together grounded and distributional representations.
| 2,019 | Computation and Language |
How Language-Neutral is Multilingual BERT? | Multilingual BERT (mBERT) provides sentence representations for 104
languages, which are useful for many multi-lingual tasks. Previous work probed
the cross-linguality of mBERT using zero-shot transfer learning on
morphological and syntactic tasks. We instead focus on the semantic properties
of mBERT. We show that mBERT representations can be split into a
language-specific component and a language-neutral component, and that the
language-neutral component is sufficiently general in terms of modeling
semantics to allow high-accuracy word-alignment and sentence retrieval but is
not yet good enough for the more difficult task of MT quality estimation. Our
work presents interesting challenges which must be solved to build better
language-neutral representations, particularly for tasks requiring linguistic
transfer of semantics.
| 2,019 | Computation and Language |
Transforming Wikipedia into Augmented Data for Query-Focused
Summarization | The limited size of existing query-focused summarization datasets renders
training data-driven summarization models challenging. Meanwhile, the manual
construction of a query-focused summarization corpus is costly and
time-consuming. In this paper, we use Wikipedia to automatically collect a
large query-focused summarization dataset (named WIKIREF) of more than 280, 000
examples, which can serve as a means of data augmentation. We also develop a
BERT-based query-focused summarization model (Q-BERT) to extract sentences from
the documents as summaries. To better adapt a huge model containing millions of
parameters to tiny benchmarks, we identify and fine-tune only a sparse
subnetwork, which corresponds to a small fraction of the whole model
parameters. Experimental results on three DUC benchmarks show that the model
pre-trained on WIKIREF has already achieved reasonable performance. After
fine-tuning on the specific benchmark datasets, the model with data
augmentation outperforms strong comparison systems. Moreover, both our proposed
Q-BERT model and subnetwork fine-tuning further improve the model performance.
The dataset is publicly available at https://aka.ms/wikiref.
| 2,022 | Computation and Language |
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck
Languages | We introduce three memory-augmented Recurrent Neural Networks (MARNNs) and
explore their capabilities on a series of simple language modeling tasks whose
solutions require stack-based mechanisms. We provide the first demonstration of
neural networks recognizing the generalized Dyck languages, which express the
core of what it means to be a language with hierarchical structure. Our
memory-augmented architectures are easy to train in an end-to-end fashion and
can learn the Dyck languages over as many as six parenthesis-pairs, in addition
to two deterministic palindrome languages and the string-reversal transduction
task, by emulating pushdown automata. Our experiments highlight the increased
modeling capacity of memory-augmented models over simple RNNs, while inflecting
our understanding of the limitations of these models.
| 2,019 | Computation and Language |
Negated and Misprimed Probes for Pretrained Language Models: Birds Can
Talk, But Cannot Fly | Building on Petroni et al. (2019), we propose two new probing tasks analyzing
factual knowledge stored in Pretrained Language Models (PLMs). (1) Negation. We
find that PLMs do not distinguish between negated ("Birds cannot [MASK]") and
non-negated ("Birds can [MASK]") cloze questions. (2) Mispriming. Inspired by
priming methods in human psychology, we add "misprimes" to cloze questions
("Talk? Birds can [MASK]"). We find that PLMs are easily distracted by
misprimes. These results suggest that PLMs still have a long way to go to
adequately learn human-like factual knowledge.
| 2,020 | Computation and Language |
Ask to Learn: A Study on Curiosity-driven Question Generation | We propose a novel text generation task, namely Curiosity-driven Question
Generation. We start from the observation that the Question Generation task has
traditionally been considered as the dual problem of Question Answering, hence
tackling the problem of generating a question given the text that contains its
answer. Such questions can be used to evaluate machine reading comprehension.
However, in real life, and especially in conversational settings, humans tend
to ask questions with the goal of enriching their knowledge and/or clarifying
aspects of previously gathered information. We refer to these inquisitive
questions as Curiosity-driven: these questions are generated with the goal of
obtaining new information (the answer) which is not present in the input text.
In this work, we experiment on this new task using a conversational Question
Answering (QA) dataset; further, since the majority of QA dataset are not built
in a conversational manner, we describe a methodology to derive data for this
novel task from non-conversational QA data. We investigate several automated
metrics to measure the different properties of Curious Questions, and
experiment different approaches on the Curiosity-driven Question Generation
task, including model pre-training and reinforcement learning. Finally, we
report a qualitative evaluation of the generated outputs.
| 2,019 | Computation and Language |
SEPT: Improving Scientific Named Entity Recognition with Span
Representation | We introduce a new scientific named entity recognizer called SEPT, which
stands for Span Extractor with Pre-trained Transformers. In recent papers, span
extractors have been demonstrated to be a powerful model compared with sequence
labeling models. However, we discover that with the development of pre-trained
language models, the performance of span extractors appears to become similar
to sequence labeling models. To keep the advantages of span representation, we
modified the model by under-sampling to balance the positive and negative
samples and reduce the search space. Furthermore, we simplify the origin
network architecture to combine the span extractor with BERT. Experiments
demonstrate that even simplified architecture achieves the same performance and
SEPT achieves a new state of the art result in scientific named entity
recognition even without relation information involved.
| 2,020 | Computation and Language |
Domain, Translationese and Noise in Synthetic Data for Neural Machine
Translation | The quality of neural machine translation can be improved by leveraging
additional monolingual resources to create synthetic training data. Source-side
monolingual data can be (forward-)translated into the target language for
self-training; target-side monolingual data can be back-translated. It has been
widely reported that back-translation delivers superior results, but could this
be due to artefacts in the test sets? We perform a case study using
French-English news translation task and separate test sets based on their
original languages. We show that forward translation delivers superior gains in
terms of BLEU on sentences that were originally in the source language,
complementing previous studies which show large improvements with
back-translation on sentences that were originally in the target language. To
better understand when and why forward and back-translation are effective, we
study the role of domains, translationese, and noise. While translationese
effects are well known to influence MT evaluation, we also find evidence that
news data from different languages shows subtle domain differences, which is
another explanation for varying performance on different portions of the test
set. We perform additional low-resource experiments which demonstrate that
forward translation is more sensitive to the quality of the initial translation
system than back-translation, and tends to perform worse in low-resource
settings.
| 2,020 | Computation and Language |
A Good Sample is Hard to Find: Noise Injection Sampling and
Self-Training for Neural Language Generation Models | Deep neural networks (DNN) are quickly becoming the de facto standard
modeling method for many natural language generation (NLG) tasks. In order for
such models to truly be useful, they must be capable of correctly generating
utterances for novel meaning representations (MRs) at test time. In practice,
even sophisticated DNNs with various forms of semantic control frequently fail
to generate utterances faithful to the input MR. In this paper, we propose an
architecture agnostic self-training method to sample novel MR/text utterance
pairs to augment the original training data. Remarkably, after training on the
augmented data, even simple encoder-decoder models with greedy decoding are
capable of generating semantically correct utterances that are as good as
state-of-the-art outputs in both automatic and human evaluations of quality.
| 2,019 | Computation and Language |
Investigation of Error Simulation Techniques for Learning Dialog
Policies for Conversational Error Recovery | Training dialog policies for speech-based virtual assistants requires a
plethora of conversational data. The data collection phase is often expensive
and time consuming due to human involvement. To address this issue, a common
solution is to build user simulators for data generation. For the successful
deployment of the trained policies into real world domains, it is vital that
the user simulator mimics realistic conditions. In particular, speech-based
assistants are heavily affected by automatic speech recognition and language
understanding errors, hence the user simulator should be able to simulate
similar errors. In this paper, we review the existing error simulation methods
that induce errors at audio, phoneme, text, or semantic level; and conduct
detailed comparisons between the audio-level and text-level methods. In the
process, we improve the existing text-level method by introducing confidence
score prediction and out-of-vocabulary word mapping. We also explore the impact
of audio-level and text-level methods on learning a simple clarification dialog
policy to recover from errors to provide insight on future improvement for both
approaches.
| 2,019 | Computation and Language |
Low-Level Linguistic Controls for Style Transfer and Content
Preservation | Despite the success of style transfer in image processing, it has seen
limited progress in natural language generation. Part of the problem is that
content is not as easily decoupled from style in the text domain. Curiously, in
the field of stylometry, content does not figure prominently in practical
methods of discriminating stylistic elements, such as authorship and genre.
Rather, syntax and function words are the most salient features. Drawing on
this work, we model style as a suite of low-level linguistic controls, such as
frequency of pronouns, prepositions, and subordinate clause constructions. We
train a neural encoder-decoder model to reconstruct reference sentences given
only content words and the setting of the controls. We perform style transfer
by keeping the content words fixed while adjusting the controls to be
indicative of another style. In experiments, we show that the model reliably
responds to the linguistic controls and perform both automatic and manual
evaluations on style transfer. We find we can fool a style classifier 84% of
the time, and that our model produces highly diverse and stylistically
distinctive outputs. This work introduces a formal, extendable model of style
that can add control to any neural text generation system.
| 2,019 | Computation and Language |
Question Generation from Paragraphs: A Tale of Two Hierarchical Models | Automatic question generation from paragraphs is an important and challenging
problem, particularly due to the long context from paragraphs. In this paper,
we propose and study two hierarchical models for the task of question
generation from paragraphs. Specifically, we propose (a) a novel hierarchical
BiLSTM model with selective attention and (b) a novel hierarchical Transformer
architecture, both of which learn hierarchical representations of paragraphs.
We model a paragraph in terms of its constituent sentences, and a sentence in
terms of its constituent words. While the introduction of the attention
mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer,
with its inherent attention and positional encoding mechanisms also performs
better than flat transformer model. We conducted empirical evaluation on the
widely used SQuAD and MS MARCO datasets using standard metrics. The results
demonstrate the overall effectiveness of the hierarchical models over their
flat counterparts. Qualitatively, our hierarchical models are able to generate
fluent and relevant questions
| 2,019 | Computation and Language |
ERASER: A Benchmark to Evaluate Rationalized NLP Models | State-of-the-art models in NLP are now predominantly based on deep neural
networks that are opaque in terms of how they come to make predictions. This
limitation has increased interest in designing more interpretable deep models
for NLP that reveal the `reasoning' behind model outputs. But work in this
direction has been conducted on different datasets and tasks with
correspondingly unique aims and metrics; this makes it difficult to track
progress. We propose the Evaluating Rationales And Simple English Reasoning
(ERASER) benchmark to advance research on interpretable models in NLP. This
benchmark comprises multiple datasets and tasks for which human annotations of
"rationales" (supporting evidence) have been collected. We propose several
metrics that aim to capture how well the rationales provided by models align
with human rationales, and also how faithful these rationales are (i.e., the
degree to which provided rationales influenced the corresponding predictions).
Our hope is that releasing this benchmark facilitates progress on designing
more interpretable NLP systems. The benchmark, code, and documentation are
available at https://www.eraserbenchmark.com/
| 2,020 | Computation and Language |
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language
Models through Principled Regularized Optimization | Transfer learning has fundamentally changed the landscape of natural language
processing (NLP) research. Many existing state-of-the-art models are first
pre-trained on a large text corpus and then fine-tuned on downstream tasks.
However, due to limited data resources from downstream tasks and the extremely
large capacity of pre-trained models, aggressive fine-tuning often causes the
adapted model to overfit the data of downstream tasks and forget the knowledge
of the pre-trained model. To address the above issue in a more principled
manner, we propose a new computational framework for robust and efficient
fine-tuning for pre-trained language models. Specifically, our proposed
framework contains two important ingredients: 1. Smoothness-inducing
regularization, which effectively manages the capacity of the model; 2. Bregman
proximal point optimization, which is a class of trust-region methods and can
prevent knowledge forgetting. Our experiments demonstrate that our proposed
method achieves the state-of-the-art performance on multiple NLP benchmarks.
| 2,021 | Computation and Language |
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for
Discourse Parsing and Machine Comprehension | In this paper, we propose the scheme for annotating large-scale multi-party
chat dialogues for discourse parsing and machine comprehension. The main goal
of this project is to help understand multi-party dialogues. Our dataset is
based on the Ubuntu Chat Corpus. For each multi-party dialogue, we annotate the
discourse structure and question-answer pairs for dialogues. As we know, this
is the first large scale corpus for multi-party dialogues discourse parsing,
and we firstly propose the task for multi-party dialogues machine reading
comprehension.
| 2,019 | Computation and Language |
Neural Arabic Text Diacritization: State of the Art Results and a Novel
Approach for Machine Translation | In this work, we present several deep learning models for the automatic
diacritization of Arabic text. Our models are built using two main approaches,
viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN),
with several enhancements such as 100-hot encoding, embeddings, Conditional
Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested
on the only freely available benchmark dataset and the results show that our
models are either better or on par with other models, which require
language-dependent post-processing steps, unlike ours. Moreover, we show that
diacritics in Arabic can be used to enhance the models of NLP tasks such as
Machine Translation (MT) by proposing the Translation over Diacritization (ToD)
approach.
| 2,019 | Computation and Language |
Graph-to-Graph Transformer for Transition-based Dependency Parsing | We propose the Graph2Graph Transformer architecture for conditioning on and
predicting arbitrary graphs, and apply it to the challenging task of
transition-based dependency parsing. After proposing two novel Transformer
models of transition-based dependency parsing as strong baselines, we show that
adding the proposed mechanisms for conditioning on and predicting graphs of
Graph2Graph Transformer results in significant improvements, both with and
without BERT pre-training. The novel baselines and their integration with
Graph2Graph Transformer significantly outperform the state-of-the-art in
traditional transition-based dependency parsing on both English Penn Treebank,
and 13 languages of Universal Dependencies Treebanks. Graph2Graph Transformer
can be integrated with many previous structured prediction methods, making it
easy to apply to a wide range of NLP tasks.
| 2,021 | Computation and Language |
How Decoding Strategies Affect the Verifiability of Generated Text | Recent progress in pre-trained language models led to systems that are able
to generate text of an increasingly high quality. While several works have
investigated the fluency and grammatical correctness of such models, it is
still unclear to which extent the generated text is consistent with factual
world knowledge. Here, we go beyond fluency and also investigate the
verifiability of text generated by state-of-the-art pre-trained language
models. A generated sentence is verifiable if it can be corroborated or
disproved by Wikipedia, and we find that the verifiability of generated text
strongly depends on the decoding strategy. In particular, we discover a
tradeoff between factuality (i.e., the ability of generating Wikipedia
corroborated text) and repetitiveness. While decoding strategies such as top-k
and nucleus sampling lead to less repetitive generations, they also produce
less verifiable text. Based on these finding, we introduce a simple and
effective decoding strategy which, in comparison to previously used decoding
strategies, produces less repetitive and more verifiable text.
| 2,020 | Computation and Language |
MKD: a Multi-Task Knowledge Distillation Approach for Pretrained
Language Models | Pretrained language models have led to significant performance gains in many
NLP tasks. However, the intensive computing resources to train such models
remain an issue. Knowledge distillation alleviates this problem by learning a
light-weight student model. So far the distillation approaches are all
task-specific. In this paper, we explore knowledge distillation under the
multi-task learning setting. The student is jointly distilled across different
tasks. It acquires more general representation capacity through multi-tasking
distillation and can be further fine-tuned to improve the model in the target
domain. Unlike other BERT distillation methods which specifically designed for
Transformer-based architectures, we provide a general learning framework. Our
approach is model agnostic and can be easily applied on different future
teacher model architectures. We evaluate our approach on a Transformer-based
and LSTM based student model. Compared to a strong, similarly LSTM-based
approach, we achieve better quality under the same computational constraints.
Compared to the present state of the art, we reach comparable results with much
faster inference speed.
| 2,020 | Computation and Language |
Zero-Shot Paraphrase Generation with Multilingual Language Models | Leveraging multilingual parallel texts to automatically generate paraphrases
has drawn much attention as size of high-quality paraphrase corpus is limited.
Round-trip translation, also known as the pivoting method, is a typical
approach to this end. However, we notice that the pivoting process involves
multiple machine translation models and is likely to incur semantic drift
during the two-step translations. In this paper, inspired by the
Transformer-based language models, we propose a simple and unified paraphrasing
model, which is purely trained on multilingual parallel data and can conduct
zero-shot paraphrase generation in one step. Compared with the pivoting
approach, paraphrases generated by our model is more semantically similar to
the input sentence. Moreover, since our model shares the same architecture as
GPT (Radford et al., 2018), we are able to pre-train the model on large-scale
unparallel corpus, which further improves the fluency of the output sentences.
In addition, we introduce the mechanism of denoising auto-encoder (DAE) to
improve diversity and robustness of the model. Experimental results show that
our model surpasses the pivoting method in terms of relevance, diversity,
fluency and efficiency.
| 2,019 | Computation and Language |
Interactive Classification by Asking Informative Questions | We study the potential for interaction in natural language classification. We
add a limited form of interaction for intent classification, where users
provide an initial query using natural language, and the system asks for
additional information using binary or multi-choice questions. At each turn,
our system decides between asking the most informative question or making the
final classification prediction.The simplicity of the model allows for
bootstrapping of the system without interaction data, instead relying on simple
crowdsourcing tasks. We evaluate our approach on two domains, showing the
benefit of interaction and the advantage of learning to balance between asking
additional questions and making the final prediction.
| 2,020 | Computation and Language |
Table-to-Text Natural Language Generation with Unseen Schemas | Traditional table-to-text natural language generation (NLG) tasks focus on
generating text from schemas that are already seen in the training set. This
limitation curbs their generalizabilities towards real-world scenarios, where
the schemas of input tables are potentially infinite. In this paper, we propose
the new task of table-to-text NLG with unseen schemas, which specifically aims
to test the generalization of NLG for input tables with attribute types that
never appear during training. To do this, we construct a new benchmark dataset
for this task. To deal with the problem of unseen attribute types, we propose a
new model that first aligns unseen table schemas to seen ones, and then
generates text with updated table representations. Experimental evaluation on
the new benchmark demonstrates that our model outperforms baseline methods by a
large margin. In addition, comparison with standard data-to-text settings shows
the challenges and uniqueness of our proposed task.
| 2,019 | Computation and Language |
A Simplified Fully Quantized Transformer for End-to-end Speech
Recognition | While significant improvements have been made in recent years in terms of
end-to-end automatic speech recognition (ASR) performance, such improvements
were obtained through the use of very large neural networks, unfit for embedded
use on edge devices. That being said, in this paper, we work on simplifying and
compressing Transformer-based encoder-decoder architectures for the end-to-end
ASR task. We empirically introduce a more compact Speech-Transformer by
investigating the impact of discarding particular modules on the performance of
the model. Moreover, we evaluate reducing the numerical precision of our
network's weights and activations while maintaining the performance of the
full-precision model. Our experiments show that we can reduce the number of
parameters of the full-precision model and then further compress the model 4x
by fully quantizing to 8-bit fixed point precision.
| 2,020 | Computation and Language |
Improving Machine Reading Comprehension via Adversarial Training | Adversarial training (AT) as a regularization method has proved its
effectiveness in various tasks, such as image classification and text
classification. Though there are successful applications of AT in many tasks of
natural language processing (NLP), the mechanism behind it is still unclear. In
this paper, we aim to apply AT on machine reading comprehension (MRC) and study
its effects from multiple perspectives. We experiment with three different
kinds of RC tasks: span-based RC, span-based RC with unanswerable questions and
multi-choice RC. The experimental results show that the proposed method can
improve the performance significantly and universally on SQuAD1.1, SQuAD2.0 and
RACE. With virtual adversarial training (VAT), we explore the possibility of
improving the RC models with semi-supervised learning and prove that examples
from a different task are also beneficial. We also find that AT helps little in
defending against artificial adversarial examples, but AT helps the model to
learn better on examples that contain more low-frequency words.
| 2,019 | Computation and Language |
Beyond Statistical Relations: Integrating Knowledge Relations into Style
Correlations for Multi-Label Music Style Classification | Automatically labeling multiple styles for every song is a comprehensive
application in all kinds of music websites. Recently, some researches explore
review-driven multi-label music style classification and exploit style
correlations for this task. However, their methods focus on mining the
statistical relations between different music styles and only consider shallow
style relations. Moreover, these statistical relations suffer from the
underfitting problem because some music styles have little training data.
To tackle these problems, we propose a novel knowledge relations integrated
framework (KRF) to capture the complete style correlations, which jointly
exploits the inherent relations between music styles according to external
knowledge and their statistical relations. Based on the two types of relations,
we use a graph convolutional network to learn the deep correlations between
styles automatically. Experimental results show that our framework
significantly outperforms state-of-the-art methods. Further studies demonstrate
that our framework can effectively alleviate the underfitting problem and learn
meaningful style correlations. The source code can be available at
https://github.com/Makwen1995/MusicGenre.
| 2,021 | Computation and Language |
Learning to Copy for Automatic Post-Editing | Automatic post-editing (APE), which aims to correct errors in the output of
machine translation systems in a post-processing step, is an important task in
natural language processing. While recent work has achieved considerable
performance gains by using neural networks, how to model the copying mechanism
for APE remains a challenge. In this work, we propose a new method for modeling
copying for APE. To better identify translation errors, our method learns the
representations of source sentences and system outputs in an interactive way.
These representations are used to explicitly indicate which words in the system
outputs should be copied, which is useful to help CopyNet (Gu et al., 2016)
better generate post-edited translations. Experiments on the datasets of the
WMT 2016-2017 APE shared tasks show that our approach outperforms all best
published results.
| 2,019 | Computation and Language |
Hierarchical Graph Network for Multi-hop Question Answering | In this paper, we present Hierarchical Graph Network (HGN) for multi-hop
question answering. To aggregate clues from scattered texts across multiple
paragraphs, a hierarchical graph is created by constructing nodes on different
levels of granularity (questions, paragraphs, sentences, entities), the
representations of which are initialized with pre-trained contextual encoders.
Given this hierarchical graph, the initial node representations are updated
through graph propagation, and multi-hop reasoning is performed via traversing
through the graph edges for each subsequent sub-task (e.g., paragraph
selection, supporting facts extraction, answer prediction). By weaving
heterogeneous nodes into an integral unified graph, this hierarchical
differentiation of node granularity enables HGN to support different question
answering sub-tasks simultaneously. Experiments on the HotpotQA benchmark
demonstrate that the proposed model achieves new state of the art,
outperforming existing multi-hop QA approaches.
| 2,020 | Computation and Language |
Hate Speech Detection on Vietnamese Social Media Text using the
Bi-GRU-LSTM-CNN Model | In recent years, Hate Speech Detection has become one of the interesting
fields in natural language processing or computational linguistics. In this
paper, we present the description of our system to solve this problem at the
VLSP shared task 2019: Hate Speech Detection on Social Networks with the corpus
which contains 20,345 human-labeled comments/posts for training and 5,086 for
public-testing. We implement a deep learning method based on the
Bi-GRU-LSTM-CNN classifier into this task. Our result in this task is 70.576%
of F1-score, ranking the 5th of performance on public-test set.
| 2,019 | Computation and Language |
Hate Speech Detection on Vietnamese Social Media Text using the
Bidirectional-LSTM Model | In this paper, we describe our system which participates in the shared task
of Hate Speech Detection on Social Networks of VLSP 2019 evaluation campaign.
We are provided with the pre-labeled dataset and an unlabeled dataset for
social media comments or posts. Our mission is to pre-process and build machine
learning models to classify comments/posts. In this report, we use
Bidirectional Long Short-Term Memory to build the model that can predict labels
for social media text according to Clean, Offensive, Hate. With this system, we
achieve comparative results with 71.43% on the public standard test set of VLSP
2019.
| 2,019 | Computation and Language |
Style is NOT a single variable: Case Studies for Cross-Style Language
Understanding | Every natural text is written in some style. Style is formed by a complex
combination of different stylistic factors, including formality markers,
emotions, metaphors, etc. One cannot form a complete understanding of a text
without considering these factors. The factors combine and co-vary in complex
ways to form styles. Studying the nature of the co-varying combinations sheds
light on stylistic language in general, sometimes called cross-style language
understanding. This paper provides the benchmark corpus (xSLUE) that combines
existing datasets and collects a new one for sentence-level cross-style
language understanding and evaluation. The benchmark contains text in 15
different styles under the proposed four theoretical groupings: figurative,
personal, affective, and interpersonal groups. For valid evaluation, we collect
an additional diagnostic set by annotating all 15 styles on the same text.
Using xSLUE, we propose three interesting cross-style applications in
classification, correlation, and generation. First, our proposed cross-style
classifier trained with multiple styles together helps improve overall
classification performance against individually-trained style classifiers.
Second, our study shows that some styles are highly dependent on each other in
human-written text. Finally, we find that combinations of some contradictive
styles likely generate stylistically less appropriate text. We believe our
benchmark and case studies help explore interesting future directions for
cross-style research. The preprocessed datasets and code are publicly
available.
| 2,021 | Computation and Language |
Multi-Perspective Inferrer: Reasoning Sentences Relationship from
Holistic Perspective | Natural Language Inference (NLI) aims to determine the logic relationships
(i.e., entailment, neutral and contradiction) between a pair of premise and
hypothesis. Recently, the alignment mechanism effectively helps NLI by
capturing the aligned parts (i.e., the similar segments) in the sentence pairs,
which imply the perspective of entailment and contradiction. However, these
aligned parts will sometimes mislead the judgment of neutral relations.
Intuitively, NLI should rely more on multiple perspectives to form a holistic
view to eliminate bias. In this paper, we propose the Multi-Perspective
Inferrer (MPI), a novel NLI model that reasons relationships from multiple
perspectives associated with the three relationships. The MPI determines the
perspectives of different parts of the sentences via a routing-by-agreement
policy and makes the final decision from a holistic view. Additionally, we
introduce an auxiliary supervised signal to ensure the MPI to learn the
expected perspectives. Experiments on SNLI and MultiNLI show that 1) the MPI
achieves substantial improvements on the base model, which verifies the
motivation of multi-perspective inference; 2) visualized evidence verifies that
the MPI learns highly interpretable perspectives as expected; 3) more
importantly, the MPI is architecture-free and compatible with the powerful
BERT.
| 2,019 | Computation and Language |
A Reinforced Generation of Adversarial Examples for Neural Machine
Translation | Neural machine translation systems tend to fail on less decent inputs despite
its significant efficacy, which may significantly harm the credibility of this
systems-fathoming how and when neural-based systems fail in such cases is
critical for industrial maintenance. Instead of collecting and analyzing bad
cases using limited handcrafted error features, here we investigate this issue
by generating adversarial examples via a new paradigm based on reinforcement
learning. Our paradigm could expose pitfalls for a given performance metric,
e.g., BLEU, and could target any given neural machine translation architecture.
We conduct experiments of adversarial attacks on two mainstream neural machine
translation architectures, RNN-search, and Transformer. The results show that
our method efficiently produces stable attacks with meaning-preserving
adversarial examples. We also present a qualitative and quantitative analysis
for the preference pattern of the attack, demonstrating its capability of
pitfall exposure.
| 2,020 | Computation and Language |
Bootstrapping Disjoint Datasets for Multilingual Multimodal
Representation Learning | Recent work has highlighted the advantage of jointly learning grounded
sentence representations from multiple languages. However, the data used in
these studies has been limited to an aligned scenario: the same images
annotated with sentences in multiple languages. We focus on the more realistic
disjoint scenario in which there is no overlap between the images in
multilingual image--caption datasets. We confirm that training with aligned
data results in better grounded sentence representations than training with
disjoint data, as measured by image--sentence retrieval performance. In order
to close this gap in performance, we propose a pseudopairing method to generate
synthetically aligned English--German--image triplets from the disjoint sets.
The method works by first training a model on the disjoint data, and then
creating new triples across datasets using sentence similarity under the
learned model. Experiments show that pseudopairs improve image--sentence
retrieval performance compared to disjoint training, despite requiring no
external data or models. However, we do find that using an external machine
translation model to generate the synthetic data sets results in better
performance.
| 2,019 | Computation and Language |
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT | We present a novel way of injecting factual knowledge about entities into the
pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity
vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use
the aligned entity vectors as if they were wordpiece vectors. The resulting
entity-enhanced version of BERT (called E-BERT) is similar in spirit to ERNIE
(Zhang et al., 2019) and KnowBert (Peters et al., 2019), but it requires no
expensive further pretraining of the BERT encoder. We evaluate E-BERT on
unsupervised question answering (QA), supervised relation classification (RC)
and entity linking (EL). On all three tasks, E-BERT outperforms BERT and other
baselines. We also show quantitatively that the original BERT model is overly
reliant on the surface form of entity names (e.g., guessing that someone with
an Italian-sounding name speaks Italian), and that E-BERT mitigates this
problem.
| 2,020 | Computation and Language |
ConveRT: Efficient and Accurate Conversational Representations from
Transformers | General-purpose pretrained sentence encoders such as BERT are not ideal for
real-world conversational AI applications; they are computationally heavy,
slow, and expensive to train. We propose ConveRT (Conversational
Representations from Transformers), a pretraining framework for conversational
tasks satisfying all the following requirements: it is effective, affordable,
and quick to train. We pretrain using a retrieval-based response selection
task, effectively leveraging quantization and subword-level parameterization in
the dual encoder to build a lightweight memory- and energy-efficient model. We
show that ConveRT achieves state-of-the-art performance across widely
established response selection tasks. We also demonstrate that the use of
extended dialog history as context yields further performance gains. Finally,
we show that pretrained representations from the proposed encoder can be
transferred to the intent classification task, yielding strong results across
three diverse data sets. ConveRT trains substantially faster than standard
sentence encoders or previous state-of-the-art dual encoders. With its reduced
size and superior performance, we believe this model promises wider portability
and scalability for Conversational AI applications.
| 2,020 | Computation and Language |
Conditioned Query Generation for Task-Oriented Dialogue Systems | Scarcity of training data for task-oriented dialogue systems is a well known
problem that is usually tackled with costly and time-consuming manual data
annotation. An alternative solution is to rely on automatic text generation
which, although less accurate than human supervision, has the advantage of
being cheap and fast. In this paper we propose a novel controlled data
generation method that could be used as a training augmentation framework for
closed-domain dialogue. Our contribution is twofold. First we show how to
optimally train and control the generation of intent-specific sentences using a
conditional variational autoencoder. Then we introduce a novel protocol called
query transfer that allows to leverage a broad, unlabelled dataset to extract
relevant information. Comparison with two different baselines shows that our
method, in the appropriate regime, consistently improves the diversity of the
generated queries without compromising their quality.
| 2,019 | Computation and Language |
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity | We address the task of unsupervised Semantic Textual Similarity (STS) by
ensembling diverse pre-trained sentence encoders into sentence meta-embeddings.
We apply, extend and evaluate different meta-embedding methods from the word
embedding literature at the sentence level, including dimensionality reduction
(Yin and Sch\"utze, 2016), generalized Canonical Correlation Analysis (Rastogi
et al., 2015) and cross-view auto-encoders (Bollegala and Bao, 2018). Our
sentence meta-embeddings set a new unsupervised State of The Art (SoTA) on the
STS Benchmark and on the STS12-STS16 datasets, with gains of between 3.7% and
6.4% Pearson's r over single-source systems.
| 2,020 | Computation and Language |
CommonGen: A Constrained Text Generation Challenge for Generative
Commonsense Reasoning | Recently, large-scale pre-trained language models have demonstrated
impressive performance on several commonsense-reasoning benchmark datasets.
However, building machines with commonsense to compose realistically plausible
sentences remains challenging. In this paper, we present a constrained text
generation task, CommonGen associated with a benchmark dataset, to explicitly
test machines for the ability of generative commonsense reasoning. Given a set
of common concepts (e.g., {dog, frisbee, catch, throw}); the task is to
generate a coherent sentence describing an everyday scenario using these
concepts (e.g., "a man throws a frisbee and his dog catches it").
The CommonGen task is challenging because it inherently requires 1)
relational reasoning with background commonsense knowledge, and 2)
compositional generalization ability to work on unseen concept combinations.
Our dataset, constructed through a combination of crowdsourced and existing
caption corpora, consists of 79k commonsense descriptions over 35k unique
concept-sets. Experiments show that there is a large gap between
state-of-the-art text generation models (e.g., T5) and human performance.
Furthermore, we demonstrate that the learned generative commonsense reasoning
capability can be transferred to improve downstream tasks such as CommonsenseQA
by generating additional context.
| 2,020 | Computation and Language |
Error Analysis for Vietnamese Dependency Parsing | Dependency parsing is needed in different applications of natural language
processing. In this paper, we present a thorough error analysis for dependency
parsing for the Vietnamese language, using two state-of-the-art parsers:
MSTParser and MaltParser. The error analysis results provide us insights in
order to improve the performance of dependency parsing for the Vietnamese
language.
| 2,015 | Computation and Language |
Vietnamese transition-based dependency parsing with supertag features | In recent years, dependency parsing is a fascinating research topic and has a
lot of applications in natural language processing. In this paper, we present
an effective approach to improve dependency parsing by utilizing supertag
features. We performed experiments with the transition-based dependency parsing
approach because it can take advantage of rich features. Empirical evaluation
on Vietnamese Dependency Treebank showed that, we achieved an improvement of
18.92% in labeled attachment score with gold supertags and an improvement of
3.57% with automatic supertags.
| 2,016 | Computation and Language |
Speaker Adaptation for Attention-Based End-to-End Speech Recognition | We propose three regularization-based speaker adaptation approaches to adapt
the attention-based encoder-decoder (AED) model with very limited adaptation
data from target speakers for end-to-end automatic speech recognition. The
first method is Kullback-Leibler divergence (KLD) regularization, in which the
output distribution of a speaker-dependent (SD) AED is forced to be close to
that of the speaker-independent (SI) model by adding a KLD regularization to
the adaptation criterion. To compensate for the asymmetric deficiency in KLD
regularization, an adversarial speaker adaptation (ASA) method is proposed to
regularize the deep-feature distribution of the SD AED through the adversarial
learning of an auxiliary discriminator and the SD AED. The third approach is
the multi-task learning, in which an SD AED is trained to jointly perform the
primary task of predicting a large number of output units and an auxiliary task
of predicting a small number of output units to alleviate the target sparsity
issue. Evaluated on a Microsoft short message dictation task, all three methods
are highly effective in adapting the AED model, achieving up to 12.2% and 3.0%
word error rate improvement over an SI AED trained from 3400 hours data for
supervised and unsupervised adaptation, respectively.
| 2,019 | Computation and Language |
Multi-Sentence Argument Linking | We present a novel document-level model for finding argument spans that fill
an event's roles, connecting related ideas in sentence-level semantic role
labeling and coreference resolution. Because existing datasets for
cross-sentence linking are small, development of our neural model is supported
through the creation of a new resource, Roles Across Multiple Sentences (RAMS),
which contains 9,124 annotated events across 139 types. We demonstrate strong
performance of our model on RAMS and other event-related datasets.
| 2,020 | Computation and Language |
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded
Conversational Agents | We introduce dodecaDialogue: a set of 12 tasks that measures if a
conversational agent can communicate engagingly with personality and empathy,
ask questions, answer questions by utilizing knowledge resources, discuss
topics and situations, and perceive and converse about images. By multi-tasking
on such a broad large-scale set of data, we hope to both move towards and
measure progress in producing a single unified agent that can perceive, reason
and converse with humans in an open-domain setting. We show that such
multi-tasking improves over a BERT pre-trained baseline, largely due to
multi-tasking with very large dialogue datasets in a similar domain, and that
the multi-tasking in general provides gains to both text and image-based tasks
using several metrics in both the fine-tune and task transfer settings. We
obtain state-of-the-art results on many of the tasks, providing a strong
baseline for this challenge.
| 2,020 | Computation and Language |
Code-Mixed to Monolingual Translation Framework | The use of multilingualism in the new generation is widespread in the form of
code-mixed data on social media, and therefore a robust translation system is
required for catering to the monolingual users, as well as for easier
comprehension by language processing models. In this work, we present a
translation framework that uses a translation-transliteration strategy for
translating code-mixed data into their equivalent monolingual instances. For
converting the output to a more fluent form, it is reordered using a target
language model. The most important advantage of the proposed framework is that
it does not require a code-mixed to monolingual parallel corpus at any point.
On testing the framework, it achieved BLEU and TER scores of 16.47 and 55.45,
respectively. Since the proposed framework deals with various sub-modules, we
dive deeper into the importance of each of them, analyze the errors and
finally, discuss some improvement strategies.
| 2,019 | Computation and Language |
Subjective Sentiment Analysis for Arabic Newswire Comments | This paper presents an approach based on supervised machine learning methods
to discriminate between positive, negative and neutral Arabic reviews in online
newswire. The corpus is labeled for subjectivity and sentiment analysis (SSA)
at the sentence-level. The model uses both count and TF-IDF representations and
apply six machine learning algorithms; Multinomial Naive Bayes, Support Vector
Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and
k-nearest neighbors using uni-grams, bi-grams features. With the goal of
extracting users sentiment from written text. Experimental results showed that
n-gram features could substantially improve performance; and showed that the
Multinomial Naive Bayes approach is the most accurate in predicting topic
polarity. Best results were achieved using count vectors trained by combination
of word-based uni-grams and bi-grams with an overall accuracy of 85.57% over
two classes and 65.64% over three classes.
| 2,019 | Computation and Language |
Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models | Inspired by modular software design principles of independence,
interchangeability, and clarity of interface, we introduce a method for
enforcing encoder-decoder modularity in seq2seq models without sacrificing the
overall model quality or its full differentiability. We discretize the encoder
output units into a predefined interpretable vocabulary space using the
Connectionist Temporal Classification (CTC) loss. Our modular systems achieve
near SOTA performance on the 300h Switchboard benchmark, with WER of 8.3% and
17.6% on the SWB and CH subsets, using seq2seq models with encoder and decoder
modules which are independent and interchangeable.
| 2,019 | Computation and Language |
PoD: Positional Dependency-Based Word Embedding for Aspect Term
Extraction | Dependency context-based word embedding jointly learns the representations of
word and dependency context, and has been proved effective in aspect term
extraction. In this paper, we design the positional dependency-based word
embedding (PoD) which considers both dependency context and positional context
for aspect term extraction. Specifically, the positional context is modeled via
relative position encoding. Besides, we enhance the dependency context by
integrating more lexical information (e.g., POS tags) along dependency paths.
Experiments on SemEval 2014/2015/2016 datasets show that our approach
outperforms other embedding methods in aspect term extraction.
| 2,020 | Computation and Language |
Scalable Zero-shot Entity Linking with Dense Entity Retrieval | This paper introduces a conceptually simple, scalable, and highly effective
BERT-based entity linking model, along with an extensive evaluation of its
accuracy-speed trade-off. We present a two-stage zero-shot linking algorithm,
where each entity is defined only by a short textual description. The first
stage does retrieval in a dense space defined by a bi-encoder that
independently embeds the mention context and the entity descriptions. Each
candidate is then re-ranked with a cross-encoder, that concatenates the mention
and entity text. Experiments demonstrate that this approach is state of the art
on recent zero-shot benchmarks (6 point absolute gains) and also on more
established non-zero-shot evaluations (e.g. TACKBP-2010), despite its relative
simplicity (e.g. no explicit entity embeddings or manually engineered mention
tables). We also show that bi-encoder linking is very fast with nearest
neighbour search (e.g. linking with 5.9 million candidates in 2 milliseconds),
and that much of the accuracy gain from the more expensive cross-encoder can be
transferred to the bi-encoder via knowledge distillation. Our code and models
are available at https://github.com/facebookresearch/BLINK.
| 2,020 | Computation and Language |
Adversarial Learning on the Latent Space for Diverse Dialog Generation | Generating relevant responses in a dialog is challenging, and requires not
only proper modeling of context in the conversation but also being able to
generate fluent sentences during inference. In this paper, we propose a
two-step framework based on generative adversarial nets for generating
conditioned responses. Our model first learns a meaningful representation of
sentences by autoencoding and then learns to map an input query to the response
representation, which is in turn decoded as a response sentence. Both
quantitative and qualitative evaluations show that our model generates more
fluent, relevant, and diverse responses than existing state-of-the-art methods.
| 2,020 | Computation and Language |
Adaptive Fusion Techniques for Multimodal Data | Effective fusion of data from multiple modalities, such as video, speech, and
text, is challenging due to the heterogeneous nature of multimodal data. In
this paper, we propose adaptive fusion techniques that aim to model context
from different modalities effectively. Instead of defining a deterministic
fusion operation, such as concatenation, for the network, we let the network
decide "how" to combine a given set of multimodal features more effectively. We
propose two networks: 1) Auto-Fusion, which learns to compress information from
different modalities while preserving the context, and 2) GAN-Fusion, which
regularizes the learned latent space given context from complementing
modalities. A quantitative evaluation on the tasks of multimodal machine
translation and emotion recognition suggests that our lightweight, adaptive
networks can better model context from other modalities than existing methods,
many of which employ massive transformer-based networks.
| 2,021 | Computation and Language |
Generalizing Natural Language Analysis through Span-relation
Representations | Natural language processing covers a wide variety of tasks predicting syntax,
semantics, and information content, and usually each type of output is
generated with specially designed architectures. In this paper, we provide the
simple insight that a great variety of tasks can be represented in a single
unified format consisting of labeling spans and relations between spans, thus a
single task-independent model can be used across different tasks. We perform
extensive experiments to test this insight on 10 disparate tasks spanning
dependency parsing (syntax), semantic role labeling (semantics), relation
extraction (information content), aspect based sentiment analysis (sentiment),
and many others, achieving performance comparable to state-of-the-art
specialized models. We further demonstrate benefits of multi-task learning, and
also show that the proposed method makes it easy to analyze differences and
similarities in how the model handles different tasks. Finally, we convert
these datasets into a unified format to build a benchmark, which provides a
holistic testbed for evaluating future models for generalized natural language
analysis.
| 2,020 | Computation and Language |
Translationese as a Language in "Multilingual" NMT | Machine translation has an undesirable propensity to produce "translationese"
artifacts, which can lead to higher BLEU scores while being liked less by human
raters. Motivated by this, we model translationese and original (i.e. natural)
text as separate languages in a multilingual model, and pose the question: can
we perform zero-shot translation between original source text and original
target text? There is no data with original source and original target, so we
train sentence-level classifiers to distinguish translationese from original
target text, and use this classifier to tag the training data for an NMT model.
Using this technique we bias the model to produce more natural outputs at test
time, yielding gains in human evaluation scores on both accuracy and fluency.
Additionally, we demonstrate that it is possible to bias the model to produce
translationese and game the BLEU score, increasing it while decreasing
human-rated quality. We analyze these models using metrics to measure the
degree of translationese in the output, and present an analysis of the
capriciousness of heuristically-based train-data tagging.
| 2,020 | Computation and Language |
Stylized Text Generation Using Wasserstein Autoencoders with a Mixture
of Gaussian Prior | Wasserstein autoencoders are effective for text generation. They do not
however provide any control over the style and topic of the generated sentences
if the dataset has multiple classes and includes different topics. In this
work, we present a semi-supervised approach for generating stylized sentences.
Our model is trained on a multi-class dataset and learns the latent
representation of the sentences using a mixture of Gaussian prior without any
adversarial losses. This allows us to generate sentences in the style of a
specified class or multiple classes by sampling from their corresponding prior
distributions. Moreover, we can train our model on relatively small datasets
and learn the latent representation of a specified class by adding external
data with other styles/classes to our dataset. While a simple WAE or VAE cannot
generate diverse sentences in this case, generated sentences with our approach
are diverse, fluent, and preserve the style and the content of the desired
classes.
| 2,019 | Computation and Language |
Distilling Knowledge Learned in BERT for Text Generation | Large-scale pre-trained language model such as BERT has achieved great
success in language understanding tasks. However, it remains an open question
how to utilize BERT for language generation. In this paper, we present a novel
approach, Conditional Masked Language Modeling (C-MLM), to enable the
finetuning of BERT on target generation tasks. The finetuned BERT (teacher) is
exploited as extra supervision to improve conventional Seq2Seq models (student)
for better text generation performance. By leveraging BERT's idiosyncratic
bidirectional nature, distilling knowledge learned in BERT can encourage
auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level
supervision for coherent text generation. Experiments show that the proposed
approach significantly outperforms strong Transformer baselines on multiple
language generation tasks such as machine translation and text summarization.
Our proposed model also achieves new state of the art on IWSLT German-English
and English-Vietnamese MT datasets. Code is available at
https://github.com/ChenRocks/Distill-BERT-Textgen.
| 2,020 | Computation and Language |
Contextualized End-to-End Neural Entity Linking | We propose yet another entity linking model (YELM) which links words to
entities instead of spans. This overcomes any difficulties associated with the
selection of good candidate mention spans and makes the joint training of
mention detection (MD) and entity disambiguation (ED) easily possible. Our
model is based on BERT and produces contextualized word embeddings which are
trained against a joint MD and ED objective. We achieve state-of-the-art
results on several standard entity linking (EL) datasets.
| 2,020 | Computation and Language |
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation | Models often easily learn biases present in the training data, and their
predictions directly reflect this bias. We analyze gender bias in dialogue
data, and examine how this bias is actually amplified in subsequent generative
chit-chat dialogue models. We measure gender bias in six existing dialogue
datasets, and focus on the most biased one, the multi-player text-based fantasy
adventure dataset LIGHT, as a testbed for our bias mitigation techniques. The
LIGHT dataset is highly imbalanced with respect to gender, containing
predominantly male characters, likely because it is entirely collected by
crowdworkers and reflects common biases that exist in fantasy or medieval
settings. We consider three techniques to mitigate gender bias: counterfactual
data augmentation, targeted data collection, and bias controlled training. We
show that our proposed techniques mitigate gender bias in LIGHT by balancing
the genderedness of generated dialogue utterances and are particularly
effective in combination. We quantify performance using various evaluation
methods---such as quantity of gendered words, a dialogue safety classifier, and
human studies---all of which show that our models generate less gendered, but
equally engaging chit-chat responses.
| 2,020 | Computation and Language |
Not All Claims are Created Equal: Choosing the Right Statistical
Approach to Assess Hypotheses | Empirical research in Natural Language Processing (NLP) has adopted a narrow
set of principles for assessing hypotheses, relying mainly on p-value
computation, which suffers from several known issues. While alternative
proposals have been well-debated and adopted in other fields, they remain
rarely discussed or used within the NLP community. We address this gap by
contrasting various hypothesis assessment techniques, especially those not
commonly used in the field (such as evaluations based on Bayesian inference).
Since these statistical techniques differ in the hypotheses they can support,
we argue that practitioners should first decide their target hypothesis before
choosing an assessment method. This is crucial because common fallacies,
misconceptions, and misinterpretation surrounding hypothesis assessment methods
often stem from a discrepancy between what one would like to claim versus what
the method used actually assesses. Our survey reveals that these issues are
omnipresent in the NLP research community. As a step forward, we provide best
practices and guidelines tailored to NLP research, as well as an easy-to-use
package called 'HyBayes' for Bayesian assessment of hypotheses, complementing
existing tools.
| 2,020 | Computation and Language |
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake
News Detection | Fake news has altered society in negative ways in politics and culture. It
has adversely affected both online social network systems as well as offline
communities and conversations. Using automatic machine learning classification
models is an efficient way to combat the widespread dissemination of fake news.
However, a lack of effective, comprehensive datasets has been a problem for
fake news research and detection model development. Prior fake news datasets do
not provide multimodal text and image data, metadata, comment data, and
fine-grained fake news categorization at the scale and breadth of our dataset.
We present Fakeddit, a novel multimodal dataset consisting of over 1 million
samples from multiple categories of fake news. After being processed through
several stages of review, the samples are labeled according to 2-way, 3-way,
and 6-way classification categories through distant supervision. We construct
hybrid text+image models and perform extensive experiments for multiple
variations of classification, demonstrating the importance of the novel aspect
of multimodality and fine-grained classification unique to Fakeddit.
| 2,020 | Computation and Language |
Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood
Training | Generative dialogue models currently suffer from a number of problems which
standard maximum likelihood training does not address. They tend to produce
generations that (i) rely too much on copying from the context, (ii) contain
repetitions within utterances, (iii) overuse frequent words, and (iv) at a
deeper level, contain logical flaws. In this work we show how all of these
problems can be addressed by extending the recently introduced unlikelihood
loss (Welleck et al., 2019) to these cases. We show that appropriate loss
functions which regularize generated outputs to match human distributions are
effective for the first three issues. For the last important general issue, we
show applying unlikelihood to collected data of what a model should not do is
effective for improving logical consistency, potentially paving the way to
generative models with greater reasoning ability. We demonstrate the efficacy
of our approach across several dialogue tasks.
| 2,020 | Computation and Language |
Increasing Robustness to Spurious Correlations using Forgettable
Examples | Neural NLP models tend to rely on spurious correlations between labels and
input features to perform their tasks. Minority examples, i.e., examples that
contradict the spurious correlations present in the majority of data points,
have been shown to increase the out-of-distribution generalization of
pre-trained language models. In this paper, we first propose using example
forgetting to find minority examples without prior knowledge of the spurious
correlations present in the dataset. Forgettable examples are instances either
learned and then forgotten during training or never learned. We empirically
show how these examples are related to minorities in our training sets. Then,
we introduce a new approach to robustify models by fine-tuning our models
twice, first on the full training data and second on the minorities only. We
obtain substantial improvements in out-of-distribution generalization when
applying our approach to the MNLI, QQP, and FEVER datasets.
| 2,021 | Computation and Language |
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent
Representations on Electronic Health Records | The extraction of phenotype information which is naturally contained in
electronic health records (EHRs) has been found to be useful in various
clinical informatics applications such as disease diagnosis. However, due to
imprecise descriptions, lack of gold standards and the demand for efficiency,
annotating phenotypic abnormalities on millions of EHR narratives is still
challenging. In this work, we propose a novel unsupervised deep learning
framework to annotate the phenotypic abnormalities from EHRs via semantic
latent representations. The proposed framework takes the advantage of Human
Phenotype Ontology (HPO), which is a knowledge base of phenotypic
abnormalities, to standardize the annotation results. Experiments have been
conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative
analysis have shown the proposed framework achieves state-of-the-art annotation
performance and computational efficiency compared with other methods.
| 2,019 | Computation and Language |
Learning to Few-Shot Learn Across Diverse Natural Language
Classification Tasks | Self-supervised pre-training of transformer models has shown enormous success
in improving performance on a number of downstream tasks. However, fine-tuning
on a new task still requires large amounts of task-specific labelled data to
achieve good performance. We consider this problem of learning to generalize to
new tasks with few examples as a meta-learning problem. While meta-learning has
shown tremendous progress in recent years, its application is still limited to
simulated problems or problems with limited diversity across tasks. We develop
a novel method, LEOPARD, which enables optimization-based meta-learning across
tasks with different number of classes, and evaluate different methods on
generalization to diverse NLP classification tasks. LEOPARD is trained with the
state-of-the-art transformer architecture and shows better generalization to
tasks not seen at all during training, with as few as 4 examples per label.
Across 17 NLP tasks, including diverse domains of entity typing, natural
language inference, sentiment analysis, and several other text classification
tasks, we show that LEOPARD learns better initial parameters for few-shot
learning than self-supervised pre-training or multi-task training,
outperforming many strong baselines, for example, yielding 14.5% average
relative gain in accuracy on unseen tasks with only 4 examples per label.
| 2,020 | Computation and Language |
Improving Transformer Models by Reordering their Sublayers | Multilayer transformer networks consist of interleaved self-attention and
feedforward sublayers. Could ordering the sublayers in a different pattern lead
to better performance? We generate randomly ordered transformers and train them
with the language modeling objective. We observe that some of these models are
able to achieve better performance than the interleaved baseline, and that
those successful variants tend to have more self-attention at the bottom and
more feedforward sublayers at the top. We propose a new transformer pattern
that adheres to this property, the sandwich transformer, and show that it
improves perplexity on multiple word-level and character-level language
modeling benchmarks, at no cost in parameters, memory, or training time.
However, the sandwich reordering pattern does not guarantee performance gains
across every task, as we demonstrate on machine translation models. Instead, we
suggest that further exploration of task-specific sublayer reorderings is
needed in order to unlock additional gains.
| 2,020 | Computation and Language |
Knowledge Guided Text Retrieval and Reading for Open Domain Question
Answering | We introduce an approach for open-domain question answering (QA) that
retrieves and reads a passage graph, where vertices are passages of text and
edges represent relationships that are derived from an external knowledge base
or co-occurrence in the same article. Our goals are to boost coverage by using
knowledge-guided retrieval to find more relevant passages than text-matching
methods, and to improve accuracy by allowing for better knowledge-guided fusion
of information across related passages. Our graph retrieval method expands a
set of seed keyword-retrieved passages by traversing the graph structure of the
knowledge base. Our reader extends a BERT-based architecture and updates
passage representations by propagating information from related passages and
their relations, instead of reading each passage in isolation. Experiments on
three open-domain QA datasets, WebQuestions, Natural Questions and TriviaQA,
show improved performance over non-graph baselines by 2-11% absolute. Our
approach also matches or exceeds the state-of-the-art in every case, without
using an expensive end-to-end training regime.
| 2,020 | Computation and Language |
Knowledge Guided Named Entity Recognition for BioMedical Text | In this work, we formulate the NER task as a multi-answer knowledge guided QA
task (KGQA) which helps to predict entities only by assigning B, I and O tags
without associating entity types with the tags. We provide different knowledge
contexts, such as, entity types, questions, definitions and examples along with
the text and train on a combined dataset of 18 biomedical corpora. This
formulation (a) enables systems to jointly learn NER specific features from
varied NER datasets, (b) can use knowledge-text attention to identify words
having higher similarity to provided knowledge, improving performance, (c)
reduces system confusion by reducing the prediction classes to B, I, O only,
and (d) makes detection of nested entities easier. We perform extensive
experiments of this KGQA formulation on 18 biomedical NER datasets, and through
experiments we note that knowledge helps in achieving better performance. Our
problem formulation is able to achieve state-of-the-art results in 12 datasets.
| 2,020 | Computation and Language |
Rethinking Self-Attention: Towards Interpretability in Neural Parsing | Attention mechanisms have improved the performance of NLP tasks while
allowing models to remain explainable. Self-attention is currently widely used,
however interpretability is difficult due to the numerous attention
distributions. Recent work has shown that model representations can benefit
from label-specific information, while facilitating interpretation of
predictions. We introduce the Label Attention Layer: a new form of
self-attention where attention heads represent labels. We test our novel layer
by running constituency and dependency parsing experiments and show our new
model obtains new state-of-the-art results for both tasks on both the Penn
Treebank (PTB) and Chinese Treebank. Additionally, our model requires fewer
self-attention layers compared to existing work. Finally, we find that the
Label Attention heads learn relations between syntactic categories and show
pathways to analyze errors.
| 2,020 | Computation and Language |
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot
Commonsense Question Answering | Understanding narratives requires reasoning about implicit world knowledge
related to the causes, effects, and states of situations described in text. At
the core of this challenge is how to access contextually relevant knowledge on
demand and reason over it.
In this paper, we present initial studies toward zero-shot commonsense
question answering by formulating the task as inference over dynamically
generated commonsense knowledge graphs. In contrast to previous studies for
knowledge integration that rely on retrieval of existing knowledge from static
knowledge graphs, our study requires commonsense knowledge integration where
contextually relevant knowledge is often not present in existing knowledge
bases. Therefore, we present a novel approach that generates
contextually-relevant symbolic knowledge structures on demand using generative
neural commonsense knowledge models.
Empirical results on two datasets demonstrate the efficacy of our
neuro-symbolic approach for dynamically constructing knowledge graphs for
reasoning. Our approach achieves significant performance boosts over pretrained
language models and vanilla knowledge models, all while providing interpretable
reasoning paths for its predictions.
| 2,020 | Computation and Language |
Pre-train and Plug-in: Flexible Conditional Text Generation with
Variational Auto-Encoders | Conditional Text Generation has drawn much attention as a topic of Natural
Language Generation (NLG) which provides the possibility for humans to control
the properties of generated contents. Current conditional generation models
cannot handle emerging conditions due to their joint end-to-end learning
fashion. When a new condition added, these techniques require full retraining.
In this paper, we present a new framework named Pre-train and Plug-in
Variational Auto-Encoder (PPVAE) towards flexible conditional text generation.
PPVAE decouples the text generation module from the condition representation
module to allow "one-to-many" conditional generation. When a fresh condition
emerges, only a lightweight network needs to be trained and works as a plug-in
for PPVAE, which is efficient and desirable for real-world applications.
Extensive experiments demonstrate the superiority of PPVAE against the existing
alternatives with better conditionality and diversity but less training effort.
| 2,020 | Computation and Language |
Social Bias Frames: Reasoning about Social and Power Implications of
Language | Warning: this paper contains content that may be offensive or upsetting.
Language has the power to reinforce stereotypes and project social biases
onto others. At the core of the challenge is that it is rarely what is stated
explicitly, but rather the implied meanings, that frame people's judgments
about others. For example, given a statement that "we shouldn't lower our
standards to hire more women," most listeners will infer the implicature
intended by the speaker -- that "women (candidates) are less qualified." Most
semantic formalisms, to date, do not capture such pragmatic implications in
which people express social biases and power differentials in language.
We introduce Social Bias Frames, a new conceptual formalism that aims to
model the pragmatic frames in which people project social biases and
stereotypes onto others. In addition, we introduce the Social Bias Inference
Corpus to support large-scale modelling and evaluation with 150k structured
annotations of social media posts, covering over 34k implications about a
thousand demographic groups.
We then establish baseline approaches that learn to recover Social Bias
Frames from unstructured text. We find that while state-of-the-art neural
models are effective at high-level categorization of whether a given statement
projects unwanted social bias (80% F1), they are not effective at spelling out
more detailed explanations in terms of Social Bias Frames. Our study motivates
future work that combines structured pragmatic inference with commonsense
reasoning on social implications.
| 2,020 | Computation and Language |
INSET: Sentence Infilling with INter-SEntential Transformer | Missing sentence generation (or sentence infilling) fosters a wide range of
applications in natural language generation, such as document auto-completion
and meeting note expansion. This task asks the model to generate intermediate
missing sentences that can syntactically and semantically bridge the
surrounding context. Solving the sentence infilling task requires techniques in
natural language processing ranging from understanding to discourse-level
planning to generation. In this paper, we propose a framework to decouple the
challenge and address these three aspects respectively, leveraging the power of
existing large-scale pre-trained models such as BERT and GPT-2. We empirically
demonstrate the effectiveness of our model in learning a sentence
representation for generation and further generating a missing sentence that
fits the context.
| 2,020 | Computation and Language |
CamemBERT: a Tasty French Language Model | Pretrained language models are now ubiquitous in Natural Language Processing.
Despite their success, most available models have either been trained on
English data or on the concatenation of data in multiple languages. This makes
practical use of such models --in all languages except English-- very limited.
In this paper, we investigate the feasibility of training monolingual
Transformer-based language models for other languages, taking French as an
example and evaluating our language models on part-of-speech tagging,
dependency parsing, named entity recognition and natural language inference
tasks. We show that the use of web crawled data is preferable to the use of
Wikipedia data. More surprisingly, we show that a relatively small web crawled
dataset (4GB) leads to results that are as good as those obtained using larger
datasets (130+GB). Our best performing model CamemBERT reaches or improves the
state of the art in all four downstream tasks.
| 2,020 | Computation and Language |
A Bilingual Generative Transformer for Semantic Sentence Embedding | Semantic sentence embedding models encode natural language sentences into
vectors, such that closeness in embedding space indicates closeness in the
semantics between the sentences. Bilingual data offers a useful signal for
learning such embeddings: properties shared by both sentences in a translation
pair are likely semantic, while divergent properties are likely stylistic or
language-specific. We propose a deep latent variable model that attempts to
perform source separation on parallel sentences, isolating what they have in
common in a latent semantic vector, and explaining what is left over with
language-specific latent vectors. Our proposed approach differs from past work
on semantic sentence encoding in two ways. First, by using a variational
probabilistic framework, we introduce priors that encourage source separation,
and can use our model's posterior to predict sentence embeddings for
monolingual data at test time. Second, we use high-capacity transformers as
both data generating distributions and inference networks -- contrasting with
most past work on sentence embeddings. In experiments, our approach
substantially outperforms the state-of-the-art on a standard suite of
unsupervised semantic similarity evaluations. Further, we demonstrate that our
approach yields the largest gains on more difficult subsets of these
evaluations where simple word overlap is not a good indicator of similarity.
| 2,020 | Computation and Language |
Two-Headed Monster And Crossed Co-Attention Networks | This paper presents some preliminary investigations of a new co-attention
mechanism in neural transduction models. We propose a paradigm, termed
Two-Headed Monster (THM), which consists of two symmetric encoder modules and
one decoder module connected with co-attention. As a specific and concrete
implementation of THM, Crossed Co-Attention Networks (CCNs) are designed based
on the Transformer model. We demonstrate CCNs on WMT 2014 EN-DE and WMT 2016
EN-FI translation tasks and our model outperforms the strong Transformer
baseline by 0.51 (big) and 0.74 (base) BLEU points on EN-DE and by 0.17 (big)
and 0.47 (base) BLEU points on EN-FI.
| 2,019 | Computation and Language |
Understanding Multi-Head Attention in Abstractive Summarization | Attention mechanisms in deep learning architectures have often been used as a
means of transparency and, as such, to shed light on the inner workings of the
architectures. Recently, there has been a growing interest in whether or not
this assumption is correct. In this paper we investigate the interpretability
of multi-head attention in abstractive summarization, a sequence-to-sequence
task for which attention does not have an intuitive alignment role, such as in
machine translation. We first introduce three metrics to gain insight in the
focus of attention heads and observe that these heads specialize towards
relative positions, specific part-of-speech tags, and named entities. However,
we also find that ablating and pruning these heads does not lead to a
significant drop in performance, indicating redundancy. By replacing the
softmax activation functions with sparsemax activation functions, we find that
attention heads behave seemingly more transparent: we can ablate fewer heads
and heads score higher on our interpretability metrics. However, if we apply
pruning to the sparsemax model we find that we can prune even more heads,
raising the question whether enforced sparsity actually improves transparency.
Finally, we find that relative positions heads seem integral to summarization
performance and persistently remain after pruning.
| 2,019 | Computation and Language |
A Re-evaluation of Knowledge Graph Completion Methods | Knowledge Graph Completion (KGC) aims at automatically predicting missing
links for large-scale knowledge graphs. A vast number of state-of-the-art KGC
techniques have got published at top conferences in several research fields,
including data mining, machine learning, and natural language processing.
However, we notice that several recent papers report very high performance,
which largely outperforms previous state-of-the-art methods. In this paper, we
find that this can be attributed to the inappropriate evaluation protocol used
by them and propose a simple evaluation protocol to address this problem. The
proposed protocol is robust to handle bias in the model, which can
substantially affect the final results. We conduct extensive experiments and
report the performance of several existing methods using our protocol. The
reproducible code has been made publicly available
| 2,020 | Computation and Language |
Semantic Noise Matters for Neural Natural Language Generation | Neural natural language generation (NNLG) systems are known for their
pathological outputs, i.e. generating text which is unrelated to the input
specification. In this paper, we show the impact of semantic noise on
state-of-the-art NNLG models which implement different semantic control
mechanisms. We find that cleaned data can improve semantic correctness by up to
97%, while maintaining fluency. We also find that the most common error is
omitting information, rather than hallucination.
| 2,019 | Computation and Language |
Efficient Dialogue State Tracking by Selectively Overwriting Memory | Recent works in dialogue state tracking (DST) focus on an open
vocabulary-based setting to resolve scalability and generalization issues of
the predefined ontology-based approaches. However, they are inefficient in that
they predict the dialogue state at every turn from scratch. Here, we consider
dialogue state as an explicit fixed-sized memory and propose a selectively
overwriting mechanism for more efficient DST. This mechanism consists of two
steps: (1) predicting state operation on each of the memory slots, and (2)
overwriting the memory with new values, of which only a few are generated
according to the predicted state operations. Our method decomposes DST into two
sub-tasks and guides the decoder to focus only on one of the tasks, thus
reducing the burden of the decoder. This enhances the effectiveness of training
and DST performance. Our SOM-DST (Selectively Overwriting Memory for Dialogue
State Tracking) model achieves state-of-the-art joint goal accuracy with 51.72%
in MultiWOZ 2.0 and 53.01% in MultiWOZ 2.1 in an open vocabulary-based DST
setting. In addition, we analyze the accuracy gaps between the current and the
ground truth-given situations and suggest that it is a promising direction to
improve state operation prediction to boost the DST performance.
| 2,020 | Computation and Language |
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge
with Competitive Baselines | We propose a new shared task of semantic retrieval from legal texts, in which
a so-called contract discovery is to be performed, where legal clauses are
extracted from documents, given a few examples of similar clauses from other
legal acts. The task differs substantially from conventional NLI and shared
tasks on legal information extraction (e.g., one has to identify text span
instead of a single document, page, or paragraph). The specification of the
proposed task is followed by an evaluation of multiple solutions within the
unified framework proposed for this branch of methods. It is shown that
state-of-the-art pretrained encoders fail to provide satisfactory results on
the task proposed. In contrast, Language Model-based solutions perform better,
especially when unsupervised fine-tuning is applied. Besides the ablation
studies, we addressed questions regarding detection accuracy for relevant text
fragments depending on the number of examples available. In addition to the
dataset and reference results, LMs specialized in the legal domain were made
publicly available.
| 2,020 | Computation and Language |
Effectiveness of self-supervised pre-training for speech recognition | We compare self-supervised representation learning algorithms which either
explicitly quantize the audio data or learn representations without
quantization. We find the former to be more accurate since it builds a good
vocabulary of the data through vq-wav2vec [1] to enable learning of effective
representations in subsequent BERT training. Different to previous work, we
directly fine-tune the pre-trained BERT models on transcribed speech using a
Connectionist Temporal Classification (CTC) loss instead of feeding the
representations into a task-specific model. We also propose a BERT-style model
learning directly from the continuous audio data and compare pre-training on
raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled
Librispeech data with a vq-wav2vec vocabulary is almost as good as the best
known reported system trained on 100 hours of labeled data on testclean, while
achieving a 25% WER reduction on test-other. When using only 10 minutes of
labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This
demonstrates that self-supervision can enable speech recognition systems
trained on a near-zero amount of transcribed data.
| 2,020 | Computation and Language |
Can Monolingual Pretrained Models Help Cross-Lingual Classification? | Multilingual pretrained language models (such as multilingual BERT) have
achieved impressive results for cross-lingual transfer. However, due to the
constant model capacity, multilingual pre-training usually lags behind the
monolingual competitors. In this work, we present two approaches to improve
zero-shot cross-lingual classification, by transferring the knowledge from
monolingual pretrained models to multilingual ones. Experimental results on two
cross-lingual classification benchmarks show that our methods outperform
vanilla multilingual fine-tuning.
| 2,019 | Computation and Language |
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