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The World is Not Binary: Learning to Rank with Grayscale Data for
Dialogue Response Selection | Response selection plays a vital role in building retrieval-based
conversation systems. Despite that response selection is naturally a
learning-to-rank problem, most prior works take a point-wise view and train
binary classifiers for this task: each response candidate is labeled either
relevant (one) or irrelevant (zero). On the one hand, this formalization can be
sub-optimal due to its ignorance of the diversity of response quality. On the
other hand, annotating grayscale data for learning-to-rank can be prohibitively
expensive and challenging. In this work, we show that grayscale data can be
automatically constructed without human effort. Our method employs
off-the-shelf response retrieval models and response generation models as
automatic grayscale data generators. With the constructed grayscale data, we
propose multi-level ranking objectives for training, which can (1) teach a
matching model to capture more fine-grained context-response relevance
difference and (2) reduce the train-test discrepancy in terms of distractor
strength. Our method is simple, effective, and universal. Experiments on three
benchmark datasets and four state-of-the-art matching models show that the
proposed approach brings significant and consistent performance improvements.
| 2,020 | Computation and Language |
SelfORE: Self-supervised Relational Feature Learning for Open Relation
Extraction | Open relation extraction is the task of extracting open-domain relation facts
from natural language sentences. Existing works either utilize heuristics or
distant-supervised annotations to train a supervised classifier over
pre-defined relations, or adopt unsupervised methods with additional
assumptions that have less discriminative power. In this work, we proposed a
self-supervised framework named SelfORE, which exploits weak, self-supervised
signals by leveraging large pretrained language model for adaptive clustering
on contextualized relational features, and bootstraps the self-supervised
signals by improving contextualized features in relation classification.
Experimental results on three datasets show the effectiveness and robustness of
SelfORE on open-domain Relation Extraction when comparing with competitive
baselines.
| 2,020 | Computation and Language |
An Analysis of the Utility of Explicit Negative Examples to Improve the
Syntactic Abilities of Neural Language Models | We explore the utilities of explicit negative examples in training neural
language models. Negative examples here are incorrect words in a sentence, such
as "barks" in "*The dogs barks". Neural language models are commonly trained
only on positive examples, a set of sentences in the training data, but recent
studies suggest that the models trained in this way are not capable of robustly
handling complex syntactic constructions, such as long-distance agreement. In
this paper, using English data, we first demonstrate that appropriately using
negative examples about particular constructions (e.g., subject-verb agreement)
will boost the model's robustness on them, with a negligible loss of
perplexity. The key to our success is an additional margin loss between the
log-likelihoods of a correct word and an incorrect word. We then provide a
detailed analysis of the trained models. One of our findings is the difficulty
of object-relative clauses for RNNs. We find that even with our direct learning
signals the models still suffer from resolving agreement across an
object-relative clause. Augmentation of training sentences involving the
constructions somewhat helps, but the accuracy still does not reach the level
of subject-relative clauses. Although not directly cognitively appealing, our
method can be a tool to analyze the true architectural limitation of neural
models on challenging linguistic constructions.
| 2,020 | Computation and Language |
Building a Norwegian Lexical Resource for Medical Entity Recognition | We present a large Norwegian lexical resource of categorized medical terms.
The resource merges information from large medical databases, and contains over
77,000 unique entries, including automatically mapped terms from a Norwegian
medical dictionary. We describe the methodology behind this automatic
dictionary entry mapping based on keywords and suffixes and further present the
results of a manual evaluation performed on a subset by a domain expert. The
evaluation indicated that ca. 80% of the mappings were correct.
| 2,020 | Computation and Language |
Distinguish Confusing Law Articles for Legal Judgment Prediction | Legal Judgment Prediction (LJP) is the task of automatically predicting a law
case's judgment results given a text describing its facts, which has excellent
prospects in judicial assistance systems and convenient services for the
public. In practice, confusing charges are frequent, because law cases
applicable to similar law articles are easily misjudged. For addressing this
issue, the existing method relies heavily on domain experts, which hinders its
application in different law systems. In this paper, we present an end-to-end
model, LADAN, to solve the task of LJP. To distinguish confusing charges, we
propose a novel graph neural network to automatically learn subtle differences
between confusing law articles and design a novel attention mechanism that
fully exploits the learned differences to extract compelling discriminative
features from fact descriptions attentively. Experiments conducted on
real-world datasets demonstrate the superiority of our LADAN.
| 2,020 | Computation and Language |
Dictionary-based Data Augmentation for Cross-Domain Neural Machine
Translation | Existing data augmentation approaches for neural machine translation (NMT)
have predominantly relied on back-translating in-domain (IND) monolingual
corpora. These methods suffer from issues associated with a domain information
gap, which leads to translation errors for low frequency and out-of-vocabulary
terminology. This paper proposes a dictionary-based data augmentation (DDA)
method for cross-domain NMT. DDA synthesizes a domain-specific dictionary with
general domain corpora to automatically generate a large-scale pseudo-IND
parallel corpus. The generated pseudo-IND data can be used to enhance a general
domain trained baseline. The experiments show that the DDA-enhanced NMT models
demonstrate consistent significant improvements, outperforming the baseline
models by 3.75-11.53 BLEU. The proposed method is also able to further improve
the performance of the back-translation based and IND-finetuned NMT models. The
improvement is associated with the enhanced domain coverage produced by DDA.
| 2,020 | Computation and Language |
Bootstrapping a Crosslingual Semantic Parser | Recent progress in semantic parsing scarcely considers languages other than
English but professional translation can be prohibitively expensive. We adapt a
semantic parser trained on a single language, such as English, to new languages
and multiple domains with minimal annotation. We query if machine translation
is an adequate substitute for training data, and extend this to investigate
bootstrapping using joint training with English, paraphrasing, and multilingual
pre-trained models. We develop a Transformer-based parser combining paraphrases
by ensembling attention over multiple encoders and present new versions of ATIS
and Overnight in German and Chinese for evaluation. Experimental results
indicate that MT can approximate training data in a new language for accurate
parsing when augmented with paraphrasing through multiple MT engines.
Considering when MT is inadequate, we also find that using our approach
achieves parsing accuracy within 2% of complete translation using only 50% of
training data.
| 2,020 | Computation and Language |
Learning to Summarize Passages: Mining Passage-Summary Pairs from
Wikipedia Revision Histories | In this paper, we propose a method for automatically constructing a
passage-to-summary dataset by mining the Wikipedia page revision histories. In
particular, the method mines the main body passages and the introduction
sentences which are added to the pages simultaneously. The constructed dataset
contains more than one hundred thousand passage-summary pairs. The quality
analysis shows that it is promising that the dataset can be used as a training
and validation set for passage summarization. We validate and analyze the
performance of various summarization systems on the proposed dataset. The
dataset will be available online at https://res.qyzhou.me.
| 2,020 | Computation and Language |
Data Manipulation: Towards Effective Instance Learning for Neural
Dialogue Generation via Learning to Augment and Reweight | Current state-of-the-art neural dialogue models learn from human
conversations following the data-driven paradigm. As such, a reliable training
corpus is the crux of building a robust and well-behaved dialogue model.
However, due to the open-ended nature of human conversations, the quality of
user-generated training data varies greatly, and effective training samples are
typically insufficient while noisy samples frequently appear. This impedes the
learning of those data-driven neural dialogue models. Therefore, effective
dialogue learning requires not only more reliable learning samples, but also
fewer noisy samples. In this paper, we propose a data manipulation framework to
proactively reshape the data distribution towards reliable samples by
augmenting and highlighting effective learning samples as well as reducing the
effect of inefficient samples simultaneously. In particular, the data
manipulation model selectively augments the training samples and assigns an
importance weight to each instance to reform the training data. Note that, the
proposed data manipulation framework is fully data-driven and learnable. It not
only manipulates training samples to optimize the dialogue generation model,
but also learns to increase its manipulation skills through gradient descent
with validation samples. Extensive experiments show that our framework can
improve the dialogue generation performance with respect to various automatic
evaluation metrics and human judgments.
| 2,020 | Computation and Language |
Sparse Text Generation | Current state-of-the-art text generators build on powerful language models
such as GPT-2, achieving impressive performance. However, to avoid degenerate
text, they require sampling from a modified softmax, via temperature parameters
or ad-hoc truncation techniques, as in top-$k$ or nucleus sampling. This
creates a mismatch between training and testing conditions. In this paper, we
use the recently introduced entmax transformation to train and sample from a
natively sparse language model, avoiding this mismatch. The result is a text
generator with favorable performance in terms of fluency and consistency, fewer
repetitions, and n-gram diversity closer to human text. In order to evaluate
our model, we propose three new metrics for comparing sparse or truncated
distributions: $\epsilon$-perplexity, sparsemax score, and Jensen-Shannon
divergence. Human-evaluated experiments in story completion and dialogue
generation show that entmax sampling leads to more engaging and coherent
stories and conversations.
| 2,020 | Computation and Language |
At Which Level Should We Extract? An Empirical Analysis on Extractive
Document Summarization | Extractive methods have been proven effective in automatic document
summarization. Previous works perform this task by identifying informative
contents at sentence level. However, it is unclear whether performing
extraction at sentence level is the best solution. In this work, we show that
unnecessity and redundancy issues exist when extracting full sentences, and
extracting sub-sentential units is a promising alternative. Specifically, we
propose extracting sub-sentential units based on the constituency parsing tree.
A neural extractive model which leverages the sub-sentential information and
extracts them is presented. Extensive experiments and analyses show that
extracting sub-sentential units performs competitively comparing to full
sentence extraction under the evaluation of both automatic and human
evaluations. Hopefully, our work could provide some inspiration of the basic
extraction units in extractive summarization for future research.
| 2,020 | Computation and Language |
Quantum Inspired Word Representation and Computation | Word meaning has different aspects, while the existing word representation
"compresses" these aspects into a single vector, and it needs further analysis
to recover the information in different dimensions. Inspired by quantum
probability, we represent words as density matrices, which are inherently
capable of representing mixed states. The experiment shows that the density
matrix representation can effectively capture different aspects of word meaning
while maintaining comparable reliability with the vector representation.
Furthermore, we propose a novel method to combine the coherent summation and
incoherent summation in the computation of both vectors and density matrices.
It achieves consistent improvement on word analogy task.
| 2,020 | Computation and Language |
Evaluating Models' Local Decision Boundaries via Contrast Sets | Standard test sets for supervised learning evaluate in-distribution
generalization. Unfortunately, when a dataset has systematic gaps (e.g.,
annotation artifacts), these evaluations are misleading: a model can learn
simple decision rules that perform well on the test set but do not capture a
dataset's intended capabilities. We propose a new annotation paradigm for NLP
that helps to close systematic gaps in the test data. In particular, after a
dataset is constructed, we recommend that the dataset authors manually perturb
the test instances in small but meaningful ways that (typically) change the
gold label, creating contrast sets. Contrast sets provide a local view of a
model's decision boundary, which can be used to more accurately evaluate a
model's true linguistic capabilities. We demonstrate the efficacy of contrast
sets by creating them for 10 diverse NLP datasets (e.g., DROP reading
comprehension, UD parsing, IMDb sentiment analysis). Although our contrast sets
are not explicitly adversarial, model performance is significantly lower on
them than on the original test sets---up to 25\% in some cases. We release our
contrast sets as new evaluation benchmarks and encourage future dataset
construction efforts to follow similar annotation processes.
| 2,020 | Computation and Language |
Meta-Learning for Few-Shot NMT Adaptation | We present META-MT, a meta-learning approach to adapt Neural Machine
Translation (NMT) systems in a few-shot setting. META-MT provides a new
approach to make NMT models easily adaptable to many target domains with the
minimal amount of in-domain data. We frame the adaptation of NMT systems as a
meta-learning problem, where we learn to adapt to new unseen domains based on
simulated offline meta-training domain adaptation tasks. We evaluate the
proposed meta-learning strategy on ten domains with general large scale NMT
systems. We show that META-MT significantly outperforms classical domain
adaptation when very few in-domain examples are available. Our experiments
shows that META-MT can outperform classical fine-tuning by up to 2.5 BLEU
points after seeing only 4, 000 translated words (300 parallel sentences).
| 2,020 | Computation and Language |
Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job
Ontology Expansion | Machine learning plays an ever-bigger part in online recruitment, powering
intelligent matchmaking and job recommendations across many of the world's
largest job platforms. However, the main text is rarely enough to fully
understand a job posting: more often than not, much of the required information
is condensed into the job title. Several organised efforts have been made to
map job titles onto a hand-made knowledge base as to provide this information,
but these only cover around 60\% of online vacancies. We introduce a novel,
purely data-driven approach towards the detection of new job titles. Our method
is conceptually simple, extremely efficient and competitive with traditional
NER-based approaches. Although the standalone application of our method does
not outperform a finetuned BERT model, it can be applied as a preprocessing
step as well, substantially boosting accuracy across several architectures.
| 2,020 | Computation and Language |
Speaker-change Aware CRF for Dialogue Act Classification | Recent work in Dialogue Act (DA) classification approaches the task as a
sequence labeling problem, using neural network models coupled with a
Conditional Random Field (CRF) as the last layer. CRF models the conditional
probability of the target DA label sequence given the input utterance sequence.
However, the task involves another important input sequence, that of speakers,
which is ignored by previous work. To address this limitation, this paper
proposes a simple modification of the CRF layer that takes speaker-change into
account. Experiments on the SwDA corpus show that our modified CRF layer
outperforms the original one, with very wide margins for some DA labels.
Further, visualizations demonstrate that our CRF layer can learn meaningful,
sophisticated transition patterns between DA label pairs conditioned on
speaker-change in an end-to-end way. Code is publicly available.
| 2,023 | Computation and Language |
An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper
Headlines | The extraction of anglicisms (lexical borrowings from English) is relevant
both for lexicographic purposes and for NLP downstream tasks. We introduce a
corpus of European Spanish newspaper headlines annotated with anglicisms and a
baseline model for anglicism extraction. In this paper we present: (1) a corpus
of 21,570 newspaper headlines written in European Spanish annotated with
emergent anglicisms and (2) a conditional random field baseline model with
handcrafted features for anglicism extraction. We present the newspaper
headlines corpus, describe the annotation tagset and guidelines and introduce a
CRF model that can serve as baseline for the task of detecting anglicisms. The
presented work is a first step towards the creation of an anglicism extractor
for Spanish newswire.
| 2,020 | Computation and Language |
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices | Natural Language Processing (NLP) has recently achieved great success by
using huge pre-trained models with hundreds of millions of parameters. However,
these models suffer from heavy model sizes and high latency such that they
cannot be deployed to resource-limited mobile devices. In this paper, we
propose MobileBERT for compressing and accelerating the popular BERT model.
Like the original BERT, MobileBERT is task-agnostic, that is, it can be
generically applied to various downstream NLP tasks via simple fine-tuning.
Basically, MobileBERT is a thin version of BERT_LARGE, while equipped with
bottleneck structures and a carefully designed balance between self-attentions
and feed-forward networks. To train MobileBERT, we first train a specially
designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model.
Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical
studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE
while achieving competitive results on well-known benchmarks. On the natural
language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6
lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD
v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
90.0/79.2 (1.5/2.1 higher than BERT_BASE).
| 2,020 | Computation and Language |
Zero-Shot Learning of Text Adventure Games with Sentence-Level Semantics | Reinforcement learning algorithms such as Q-learning have shown great promise
in training models to learn the optimal action to take for a given system
state; a goal in applications with an exploratory or adversarial nature such as
task-oriented dialogues or games. However, models that do not have direct
access to their state are harder to train; when the only state access is via
the medium of language, this can be particularly pronounced. We introduce a new
model amenable to deep Q-learning that incorporates a Siamese neural network
architecture and a novel refactoring of the Q-value function in order to better
represent system state given its approximation over a language channel. We
evaluate the model in the context of zero-shot text-based adventure game
learning. Extrinsically, our model reaches the baseline's convergence
performance point needing only 15% of its iterations, reaches a convergence
performance point 15% higher than the baseline's, and is able to play unseen,
unrelated games with no fine-tuning. We probe our new model's representation
space to determine that intrinsically, this is due to the appropriate
clustering of different linguistic mediation into the same state.
| 2,020 | Computation and Language |
Evaluating the Evaluation of Diversity in Natural Language Generation | Despite growing interest in natural language generation (NLG) models that
produce diverse outputs, there is currently no principled method for evaluating
the diversity of an NLG system. In this work, we propose a framework for
evaluating diversity metrics. The framework measures the correlation between a
proposed diversity metric and a diversity parameter, a single parameter that
controls some aspect of diversity in generated text. For example, a diversity
parameter might be a binary variable used to instruct crowdsourcing workers to
generate text with either low or high content diversity. We demonstrate the
utility of our framework by: (a) establishing best practices for eliciting
diversity judgments from humans, (b) showing that humans substantially
outperform automatic metrics in estimating content diversity, and (c)
demonstrating that existing methods for controlling diversity by tuning a
"decoding parameter" mostly affect form but not meaning. Our framework can
advance the understanding of different diversity metrics, an essential step on
the road towards better NLG systems.
| 2,021 | Computation and Language |
Multi-Step Inference for Reasoning Over Paragraphs | Complex reasoning over text requires understanding and chaining together
free-form predicates and logical connectives. Prior work has largely tried to
do this either symbolically or with black-box transformers. We present a middle
ground between these two extremes: a compositional model reminiscent of neural
module networks that can perform chained logical reasoning. This model first
finds relevant sentences in the context and then chains them together using
neural modules. Our model gives significant performance improvements (up to
29\% relative error reduction when comfibined with a reranker) on ROPES, a
recently introduced complex reasoning dataset.
| 2,021 | Computation and Language |
"You are grounded!": Latent Name Artifacts in Pre-trained Language
Models | Pre-trained language models (LMs) may perpetuate biases originating in their
training corpus to downstream models. We focus on artifacts associated with the
representation of given names (e.g., Donald), which, depending on the corpus,
may be associated with specific entities, as indicated by next token prediction
(e.g., Trump). While helpful in some contexts, grounding happens also in
under-specified or inappropriate contexts. For example, endings generated for
`Donald is a' substantially differ from those of other names, and often have
more-than-average negative sentiment. We demonstrate the potential effect on
downstream tasks with reading comprehension probes where name perturbation
changes the model answers. As a silver lining, our experiments suggest that
additional pre-training on different corpora may mitigate this bias.
| 2,020 | Computation and Language |
Enhancing Review Comprehension with Domain-Specific Commonsense | Review comprehension has played an increasingly important role in improving
the quality of online services and products and commonsense knowledge can
further enhance review comprehension. However, existing general-purpose
commonsense knowledge bases lack sufficient coverage and precision to
meaningfully improve the comprehension of domain-specific reviews. In this
paper, we introduce xSense, an effective system for review comprehension using
domain-specific commonsense knowledge bases (xSense KBs). We show that xSense
KBs can be constructed inexpensively and present a knowledge distillation
method that enables us to use xSense KBs along with BERT to boost the
performance of various review comprehension tasks. We evaluate xSense over
three review comprehension tasks: aspect extraction, aspect sentiment
classification, and question answering. We find that xSense outperforms the
state-of-the-art models for the first two tasks and improves the baseline BERT
QA model significantly, demonstrating the usefulness of incorporating
commonsense into review comprehension pipelines. To facilitate future research
and applications, we publicly release three domain-specific knowledge bases and
a domain-specific question answering benchmark along with this paper.
| 2,020 | Computation and Language |
Query Focused Multi-Document Summarization with Distant Supervision | We consider the problem of better modeling query-cluster interactions to
facilitate query focused multi-document summarization (QFS). Due to the lack of
training data, existing work relies heavily on retrieval-style methods for
estimating the relevance between queries and text segments. In this work, we
leverage distant supervision from question answering where various resources
are available to more explicitly capture the relationship between queries and
documents. We propose a coarse-to-fine modeling framework which introduces
separate modules for estimating whether segments are relevant to the query,
likely to contain an answer, and central. Under this framework, a trained
evidence estimator further discerns which retrieved segments might answer the
query for final selection in the summary. We demonstrate that our framework
outperforms strong comparison systems on standard QFS benchmarks.
| 2,020 | Computation and Language |
A Systematic Analysis of Morphological Content in BERT Models for
Multiple Languages | This work describes experiments which probe the hidden representations of
several BERT-style models for morphological content. The goal is to examine the
extent to which discrete linguistic structure, in the form of morphological
features and feature values, presents itself in the vector representations and
attention distributions of pre-trained language models for five European
languages. The experiments contained herein show that (i) Transformer
architectures largely partition their embedding space into convex sub-regions
highly correlated with morphological feature value, (ii) the contextualized
nature of transformer embeddings allows models to distinguish ambiguous
morphological forms in many, but not all cases, and (iii) very specific
attention head/layer combinations appear to hone in on subject-verb agreement.
| 2,020 | Computation and Language |
The Role of Pragmatic and Discourse Context in Determining Argument
Impact | Research in the social sciences and psychology has shown that the
persuasiveness of an argument depends not only the language employed, but also
on attributes of the source/communicator, the audience, and the appropriateness
and strength of the argument's claims given the pragmatic and discourse context
of the argument. Among these characteristics of persuasive arguments, prior
work in NLP does not explicitly investigate the effect of the pragmatic and
discourse context when determining argument quality. This paper presents a new
dataset to initiate the study of this aspect of argumentation: it consists of a
diverse collection of arguments covering 741 controversial topics and
comprising over 47,000 claims. We further propose predictive models that
incorporate the pragmatic and discourse context of argumentative claims and
show that they outperform models that rely only on claim-specific linguistic
features for predicting the perceived impact of individual claims within a
particular line of argument.
| 2,020 | Computation and Language |
Information-Theoretic Probing for Linguistic Structure | The success of neural networks on a diverse set of NLP tasks has led
researchers to question how much these networks actually ``know'' about natural
language. Probes are a natural way of assessing this. When probing, a
researcher chooses a linguistic task and trains a supervised model to predict
annotations in that linguistic task from the network's learned representations.
If the probe does well, the researcher may conclude that the representations
encode knowledge related to the task. A commonly held belief is that using
simpler models as probes is better; the logic is that simpler models will
identify linguistic structure, but not learn the task itself. We propose an
information-theoretic operationalization of probing as estimating mutual
information that contradicts this received wisdom: one should always select the
highest performing probe one can, even if it is more complex, since it will
result in a tighter estimate, and thus reveal more of the linguistic
information inherent in the representation. The experimental portion of our
paper focuses on empirically estimating the mutual information between a
linguistic property and BERT, comparing these estimates to several baselines.
We evaluate on a set of ten typologically diverse languages often
underrepresented in NLP research---plus English---totalling eleven languages.
| 2,020 | Computation and Language |
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature
and PRESupposition | Natural language inference (NLI) is an increasingly important task for
natural language understanding, which requires one to infer whether a sentence
entails another. However, the ability of NLI models to make pragmatic
inferences remains understudied. We create an IMPlicature and PRESupposition
diagnostic dataset (IMPPRES), consisting of >25k semiautomatically generated
sentence pairs illustrating well-studied pragmatic inference types. We use
IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on
MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although
MultiNLI appears to contain very few pairs illustrating these inference types,
we find that BERT learns to draw pragmatic inferences. It reliably treats
scalar implicatures triggered by "some" as entailments. For some presupposition
triggers like "only", BERT reliably recognizes the presupposition as an
entailment, even when the trigger is embedded under an entailment canceling
operator like negation. BOW and InferSent show weaker evidence of pragmatic
reasoning. We conclude that NLI training encourages models to learn some, but
not all, pragmatic inferences.
| 2,020 | Computation and Language |
Inferential Text Generation with Multiple Knowledge Sources and
Meta-Learning | We study the problem of generating inferential texts of events for a variety
of commonsense like \textit{if-else} relations. Existing approaches typically
use limited evidence from training examples and learn for each relation
individually. In this work, we use multiple knowledge sources as fuels for the
model. Existing commonsense knowledge bases like ConceptNet are dominated by
taxonomic knowledge (e.g., \textit{isA} and \textit{relatedTo} relations),
having a limited number of inferential knowledge. We use not only structured
commonsense knowledge bases, but also natural language snippets from
search-engine results. These sources are incorporated into a generative base
model via key-value memory network. In addition, we introduce a meta-learning
based multi-task learning algorithm. For each targeted commonsense relation, we
regard the learning of examples from other relations as the meta-training
process, and the evaluation on examples from the targeted relation as the
meta-test process. We conduct experiments on Event2Mind and ATOMIC datasets.
Results show that both the integration of multiple knowledge sources and the
use of the meta-learning algorithm improve the performance.
| 2,020 | Computation and Language |
Interview: A Large-Scale Open-Source Corpus of Media Dialog | Existing conversational datasets consist either of written proxies for dialog
or small-scale transcriptions of natural speech. We introduce 'Interview': a
large-scale (105K conversations) media dialog dataset collected from news
interview transcripts. Compared to existing large-scale proxies for
conversational data, language models trained on our dataset exhibit better
zero-shot out-of-domain performance on existing spoken dialog datasets,
demonstrating its usefulness in modeling real-world conversations. 'Interview'
contains speaker role annotations for each turn, facilitating the development
of engaging, responsive dialog systems. In fact, experiments on two dialog
tasks show that leveraging such labels improves performance over strong
speaker-agnostic baselines, and enabling models to generate more specific and
inquisitive responses in interview-style conversations.
| 2,020 | Computation and Language |
Exemplar Auditing for Multi-Label Biomedical Text Classification | Many practical applications of AI in medicine consist of semi-supervised
discovery: The investigator aims to identify features of interest at a
resolution more fine-grained than that of the available human labels. This is
often the scenario faced in healthcare applications as coarse, high-level
labels (e.g., billing codes) are often the only sources that are readily
available. These challenges are compounded for modalities such as text, where
the feature space is very high-dimensional, and often contains considerable
amounts of noise.
In this work, we generalize a recently proposed zero-shot sequence labeling
method, "binary labeling via a convolutional decomposition", to the case where
the available document-level human labels are themselves relatively
high-dimensional. The approach yields classification with "introspection",
relating the fine-grained features of an inference-time prediction to their
nearest neighbors from the training set, under the model. The approach is
effective, yet parsimonious, as demonstrated on a well-studied MIMIC-III
multi-label classification task of electronic health record data, and is useful
as a tool for organizing the analysis of neural model predictions and
high-dimensional datasets. Our proposed approach yields both a competitively
effective classification model and an interrogation mechanism to aid healthcare
workers in understanding the salient features that drive the model's
predictions.
| 2,020 | Computation and Language |
Is Graph Structure Necessary for Multi-hop Question Answering? | Recently, attempting to model texts as graph structure and introducing graph
neural networks to deal with it has become a trend in many NLP research areas.
In this paper, we investigate whether the graph structure is necessary for
multi-hop question answering. Our analysis is centered on HotpotQA. We
construct a strong baseline model to establish that, with the proper use of
pre-trained models, graph structure may not be necessary for multi-hop question
answering. We point out that both graph structure and adjacency matrix are
task-related prior knowledge, and graph-attention can be considered as a
special case of self-attention. Experiments and visualized analysis demonstrate
that graph-attention or the entire graph structure can be replaced by
self-attention or Transformers.
| 2,020 | Computation and Language |
Towards Non-task-specific Distillation of BERT via Sentence
Representation Approximation | Recently, BERT has become an essential ingredient of various NLP deep models
due to its effectiveness and universal-usability. However, the online
deployment of BERT is often blocked by its large-scale parameters and high
computational cost. There are plenty of studies showing that the knowledge
distillation is efficient in transferring the knowledge from BERT into the
model with a smaller size of parameters. Nevertheless, current BERT
distillation approaches mainly focus on task-specified distillation, such
methodologies lead to the loss of the general semantic knowledge of BERT for
universal-usability. In this paper, we propose a sentence representation
approximating oriented distillation framework that can distill the pre-trained
BERT into a simple LSTM based model without specifying tasks. Consistent with
BERT, our distilled model is able to perform transfer learning via fine-tuning
to adapt to any sentence-level downstream task. Besides, our model can further
cooperate with task-specific distillation procedures. The experimental results
on multiple NLP tasks from the GLUE benchmark show that our approach
outperforms other task-specific distillation methods or even much larger
models, i.e., ELMO, with efficiency well-improved.
| 2,020 | Computation and Language |
Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question
Answering | Open Domain Question Answering requires systems to retrieve external
knowledge and perform multi-hop reasoning by composing knowledge spread over
multiple sentences. In the recently introduced open domain question answering
challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts
and compose facts to correctly answer questions. In our work, we learn a
semantic knowledge ranking model to re-rank knowledge retrieved through Lucene
based information retrieval systems. We further propose a "knowledge fusion
model" which leverages knowledge in BERT-based language models with externally
retrieved knowledge and improves the knowledge understanding of the BERT-based
language models. On both OpenBookQA and QASC datasets, the knowledge fusion
model with semantically re-ranked knowledge outperforms previous attempts.
| 2,020 | Computation and Language |
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension | Owing to the continuous efforts by the Chinese NLP community, more and more
Chinese machine reading comprehension datasets become available. To add
diversity in this area, in this paper, we propose a new task called Sentence
Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to
fill the right candidate sentence into the passage that has several blanks. We
built a Chinese dataset called CMRC 2019 to evaluate the difficulty of the
SC-MRC task. Moreover, to add more difficulties, we also made fake candidates
that are similar to the correct ones, which requires the machine to judge their
correctness in the context. The proposed dataset contains over 100K blanks
(questions) within over 10K passages, which was originated from Chinese
narrative stories. To evaluate the dataset, we implement several baseline
systems based on the pre-trained models, and the results show that the
state-of-the-art model still underperforms human performance by a large margin.
We release the dataset and baseline system to further facilitate our community.
Resources available through https://github.com/ymcui/cmrc2019
| 2,021 | Computation and Language |
RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex
Text-to-SQL in Cross-Domain Databases | Text-to-SQL is the problem of converting a user question into an SQL query,
when the question and database are given. In this paper, we present a neural
network approach called RYANSQL (Recursively Yielding Annotation Network for
SQL) to solve complex Text-to-SQL tasks for cross-domain databases. State-ment
Position Code (SPC) is defined to trans-form a nested SQL query into a set of
non-nested SELECT statements; a sketch-based slot filling approach is proposed
to synthesize each SELECT statement for its corresponding SPC. Additionally,
two input manipulation methods are presented to improve generation performance
further. RYANSQL achieved 58.2% accuracy on the challenging Spider benchmark,
which is a 3.2%p improvement over previous state-of-the-art approaches. At the
time of writing, RYANSQL achieves the first position on the Spider leaderboard.
| 2,020 | Computation and Language |
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement
and Counterfactual Generation | Recent research demonstrates that word embeddings, trained on the
human-generated corpus, have strong gender biases in embedding spaces, and
these biases can result in the discriminative results from the various
downstream tasks. Whereas the previous methods project word embeddings into a
linear subspace for debiasing, we introduce a \textit{Latent Disentanglement}
method with a siamese auto-encoder structure with an adapted gradient reversal
layer. Our structure enables the separation of the semantic latent information
and gender latent information of given word into the disjoint latent
dimensions. Afterwards, we introduce a \textit{Counterfactual Generation} to
convert the gender information of words, so the original and the modified
embeddings can produce a gender-neutralized word embedding after geometric
alignment regularization, without loss of semantic information. From the
various quantitative and qualitative debiasing experiments, our method shows to
be better than existing debiasing methods in debiasing word embeddings. In
addition, Our method shows the ability to preserve semantic information during
debiasing by minimizing the semantic information losses for extrinsic NLP
downstream tasks.
| 2,020 | Computation and Language |
g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin
Chinese Based on a New Open Benchmark Dataset | Conversion of Chinese graphemes to phonemes (G2P) is an essential component
in Mandarin Chinese Text-To-Speech (TTS) systems. One of the biggest challenges
in Chinese G2P conversion is how to disambiguate the pronunciation of
polyphones - characters having multiple pronunciations. Although many academic
efforts have been made to address it, there has been no open dataset that can
serve as a standard benchmark for fair comparison to date. In addition, most of
the reported systems are hard to employ for researchers or practitioners who
want to convert Chinese text into pinyin at their convenience. Motivated by
these, in this work, we introduce a new benchmark dataset that consists of
99,000+ sentences for Chinese polyphone disambiguation. We train a simple
neural network model on it, and find that it outperforms other preexisting G2P
systems. Finally, we package our project and share it on PyPi.
| 2,020 | Computation and Language |
Cross-lingual Supervision Improves Unsupervised Neural Machine
Translation | Neural machine translation~(NMT) is ineffective for zero-resource languages.
Recent works exploring the possibility of unsupervised neural machine
translation (UNMT) with only monolingual data can achieve promising results.
However, there are still big gaps between UNMT and NMT with parallel
supervision. In this work, we introduce a multilingual unsupervised NMT
(\method) framework to leverage weakly supervised signals from high-resource
language pairs to zero-resource translation directions. More specifically, for
unsupervised language pairs \texttt{En-De}, we can make full use of the
information from parallel dataset \texttt{En-Fr} to jointly train the
unsupervised translation directions all in one model. \method is based on
multilingual models which require no changes to the standard unsupervised NMT.
Empirical results demonstrate that \method significantly improves the
translation quality by more than 3 BLEU score on six benchmark unsupervised
translation directions.
| 2,021 | Computation and Language |
Self-Induced Curriculum Learning in Self-Supervised Neural Machine
Translation | Self-supervised neural machine translation (SSNMT) jointly learns to identify
and select suitable training data from comparable (rather than parallel)
corpora and to translate, in a way that the two tasks support each other in a
virtuous circle. In this study, we provide an in-depth analysis of the sampling
choices the SSNMT model makes during training. We show how, without it having
been told to do so, the model self-selects samples of increasing (i) complexity
and (ii) task-relevance in combination with (iii) performing a denoising
curriculum. We observe that the dynamics of the mutual-supervision signals of
both system internal representation types are vital for the extraction and
translation performance. We show that in terms of the Gunning-Fog Readability
index, SSNMT starts extracting and learning from Wikipedia data suitable for
high school students and quickly moves towards content suitable for first year
undergraduate students.
| 2,020 | Computation and Language |
Machine Translation with Unsupervised Length-Constraints | We have seen significant improvements in machine translation due to the usage
of deep learning. While the improvements in translation quality are impressive,
the encoder-decoder architecture enables many more possibilities. In this
paper, we explore one of these, the generation of constraint translation. We
focus on length constraints, which are essential if the translation should be
displayed in a given format. In this work, we propose an end-to-end approach
for this task. Compared to a traditional method that first translates and then
performs sentence compression, the text compression is learned completely
unsupervised. By combining the idea with zero-shot multilingual machine
translation, we are also able to perform unsupervised monolingual sentence
compression. In order to fulfill the length constraints, we investigated
several methods to integrate the constraints into the model. Using the
presented technique, we are able to significantly improve the translation
quality under constraints. Furthermore, we are able to perform unsupervised
monolingual sentence compression.
| 2,020 | Computation and Language |
Towards Multimodal Simultaneous Neural Machine Translation | Simultaneous translation involves translating a sentence before the speaker's
utterance is completed in order to realize real-time understanding in multiple
languages. This task is significantly more challenging than the general full
sentence translation because of the shortage of input information during
decoding. To alleviate this shortage, we propose multimodal simultaneous neural
machine translation (MSNMT), which leverages visual information as an
additional modality. Our experiments with the Multi30k dataset showed that
MSNMT significantly outperforms its text-only counterpart in more timely
translation situations with low latency. Furthermore, we verified the
importance of visual information during decoding by performing an adversarial
evaluation of MSNMT, where we studied how models behaved with incongruent input
modality and analyzed the effect of different word order between source and
target languages.
| 2,020 | Computation and Language |
More Data, More Relations, More Context and More Openness: A Review and
Outlook for Relation Extraction | Relational facts are an important component of human knowledge, which are
hidden in vast amounts of text. In order to extract these facts from text,
people have been working on relation extraction (RE) for years. From early
pattern matching to current neural networks, existing RE methods have achieved
significant progress. Yet with explosion of Web text and emergence of new
relations, human knowledge is increasing drastically, and we thus require
"more" from RE: a more powerful RE system that can robustly utilize more data,
efficiently learn more relations, easily handle more complicated context, and
flexibly generalize to more open domains. In this paper, we look back at
existing RE methods, analyze key challenges we are facing nowadays, and show
promising directions towards more powerful RE. We hope our view can advance
this field and inspire more efforts in the community.
| 2,020 | Computation and Language |
Improving Fluency of Non-Autoregressive Machine Translation | Non-autoregressive (nAR) models for machine translation (MT) manifest
superior decoding speed when compared to autoregressive (AR) models, at the
expense of impaired fluency of their outputs. We improve the fluency of a nAR
model with connectionist temporal classification (CTC) by employing additional
features in the scoring model used during beam search decoding. Since the beam
search decoding in our model only requires to run the network in a single
forward pass, the decoding speed is still notably higher than in standard AR
models. We train models for three language pairs: German, Czech, and Romanian
from and into English. The results show that our proposed models can be more
efficient in terms of decoding speed and still achieve a competitive BLEU score
relative to AR models.
| 2,020 | Computation and Language |
Improving the Robustness of QA Models to Challenge Sets with Variational
Question-Answer Pair Generation | Question answering (QA) models for reading comprehension have achieved
human-level accuracy on in-distribution test sets. However, they have been
demonstrated to lack robustness to challenge sets, whose distribution is
different from that of training sets. Existing data augmentation methods
mitigate this problem by simply augmenting training sets with synthetic
examples sampled from the same distribution as the challenge sets. However,
these methods assume that the distribution of a challenge set is known a
priori, making them less applicable to unseen challenge sets. In this study, we
focus on question-answer pair generation (QAG) to mitigate this problem. While
most existing QAG methods aim to improve the quality of synthetic examples, we
conjecture that diversity-promoting QAG can mitigate the sparsity of training
sets and lead to better robustness. We present a variational QAG model that
generates multiple diverse QA pairs from a paragraph. Our experiments show that
our method can improve the accuracy of 12 challenge sets, as well as the
in-distribution accuracy. Our code and data are available at
https://github.com/KazutoshiShinoda/VQAG.
| 2,021 | Computation and Language |
A German Corpus for Fine-Grained Named Entity Recognition and Relation
Extraction of Traffic and Industry Events | Monitoring mobility- and industry-relevant events is important in areas such
as personal travel planning and supply chain management, but extracting events
pertaining to specific companies, transit routes and locations from
heterogeneous, high-volume text streams remains a significant challenge. This
work describes a corpus of German-language documents which has been annotated
with fine-grained geo-entities, such as streets, stops and routes, as well as
standard named entity types. It has also been annotated with a set of 15
traffic- and industry-related n-ary relations and events, such as accidents,
traffic jams, acquisitions, and strikes. The corpus consists of newswire texts,
Twitter messages, and traffic reports from radio stations, police and railway
companies. It allows for training and evaluating both named entity recognition
algorithms that aim for fine-grained typing of geo-entities, as well as n-ary
relation extraction systems.
| 2,020 | Computation and Language |
A Corpus Study and Annotation Schema for Named Entity Recognition and
Relation Extraction of Business Products | Recognizing non-standard entity types and relations, such as B2B products,
product classes and their producers, in news and forum texts is important in
application areas such as supply chain monitoring and market research. However,
there is a decided lack of annotated corpora and annotation guidelines in this
domain. In this work, we present a corpus study, an annotation schema and
associated guidelines, for the annotation of product entity and company-product
relation mentions. We find that although product mentions are often realized as
noun phrases, defining their exact extent is difficult due to high boundary
ambiguity and the broad syntactic and semantic variety of their surface
realizations. We also describe our ongoing annotation effort, and present a
preliminary corpus of English web and social media documents annotated
according to the proposed guidelines.
| 2,020 | Computation and Language |
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language
Understanding | Natural language inference (NLI) and semantic textual similarity (STS) are
key tasks in natural language understanding (NLU). Although several benchmark
datasets for those tasks have been released in English and a few other
languages, there are no publicly available NLI or STS datasets in the Korean
language. Motivated by this, we construct and release new datasets for Korean
NLI and STS, dubbed KorNLI and KorSTS, respectively. Following previous
approaches, we machine-translate existing English training sets and manually
translate development and test sets into Korean. To accelerate research on
Korean NLU, we also establish baselines on KorNLI and KorSTS. Our datasets are
publicly available at https://github.com/kakaobrain/KorNLUDatasets.
| 2,020 | Computation and Language |
Windowing Models for Abstractive Summarization of Long Texts | Neural summarization models suffer from the fixed-size input limitation: if
text length surpasses the model's maximal number of input tokens, some document
content (possibly summary-relevant) gets truncated Independently summarizing
windows of maximal input size disallows for information flow between windows
and leads to incoherent summaries. We propose windowing models for neural
abstractive summarization of (arbitrarily) long texts. We extend the
sequence-to-sequence model augmented with pointer generator network by (1)
allowing the encoder to slide over different windows of the input document and
(2) sharing the decoder and retaining its state across different input windows.
We explore two windowing variants: Static Windowing precomputes the number of
tokens the decoder should generate from each window (based on training corpus
statistics); in Dynamic Windowing the decoder learns to emit a token that
signals encoder's shift to the next input window. Empirical results render our
models effective in their intended use-case: summarizing long texts with
relevant content not bound to the very document beginning.
| 2,020 | Computation and Language |
Evaluating Online Continual Learning with CALM | Online Continual Learning (OCL) studies learning over a continuous data
stream without observing any single example more than once, a setting that is
closer to the experience of humans and systems that must learn "on-the-wild".
Yet, commonly available benchmarks are far from these real-world conditions,
because they explicitly signal different tasks, lack latent similarity
structure or assume temporal independence between different examples. Here, we
propose a new benchmark for OCL based on language modelling in which input
alternates between different languages and domains without any explicit
delimitation. Additionally, we propose new metrics to study catastrophic
forgetting in this setting and evaluate multiple baseline models based on
compositions of experts. Finally, we introduce a simple gating technique that
learns the latent similarities between different inputs, improving the
performance of a Products of Experts model.
| 2,021 | Computation and Language |
Inexpensive Domain Adaptation of Pretrained Language Models: Case
Studies on Biomedical NER and Covid-19 QA | Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved
by unsupervised pretraining on target-domain text. While successful, this
approach is expensive in terms of hardware, runtime and CO_2 emissions. Here,
we propose a cheaper alternative: We train Word2Vec on target-domain text and
align the resulting word vectors with the wordpiece vectors of a general-domain
PTLM. We evaluate on eight biomedical Named Entity Recognition (NER) tasks and
compare against the recently proposed BioBERT model. We cover over 60% of the
BioBERT-BERT F1 delta, at 5% of BioBERT's CO_2 footprint and 2% of its cloud
compute cost. We also show how to quickly adapt an existing general-domain
Question Answering (QA) model to an emerging domain: the Covid-19 pandemic.
| 2,020 | Computation and Language |
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue
State Tracking | Dialogue state tracking (DST) aims at estimating the current dialogue state
given all the preceding conversation. For multi-domain DST, the data sparsity
problem is a major obstacle due to increased numbers of state candidates and
dialogue lengths. To encode the dialogue context efficiently, we utilize the
previous dialogue state (predicted) and the current dialogue utterance as the
input for DST. To consider relations among different domain-slots, the schema
graph involving prior knowledge is exploited. In this paper, a novel context
and schema fusion network is proposed to encode the dialogue context and schema
graph by using internal and external attention mechanisms. Experiment results
show that our approach can obtain new state-of-the-art performance of the
open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.
| 2,020 | Computation and Language |
Emergent Language Generalization and Acquisition Speed are not tied to
Compositionality | Studies of discrete languages emerging when neural agents communicate to
solve a joint task often look for evidence of compositional structure. This
stems for the expectation that such a structure would allow languages to be
acquired faster by the agents and enable them to generalize better. We argue
that these beneficial properties are only loosely connected to
compositionality. In two experiments, we demonstrate that, depending on the
task, non-compositional languages might show equal, or better, generalization
performance and acquisition speed than compositional ones. Further research in
the area should be clearer about what benefits are expected from
compositionality, and how the latter would lead to them.
| 2,020 | Computation and Language |
A Legal Approach to Hate Speech: Operationalizing the EU's Legal
Framework against the Expression of Hatred as an NLP Task | We propose a 'legal approach' to hate speech detection by operationalization
of the decision as to whether a post is subject to criminal law into an NLP
task. Comparing existing regulatory regimes for hate speech, we base our
investigation on the European Union's framework as it provides a widely
applicable legal minimum standard. Accurately judging whether a post is
punishable or not usually requires legal training. We show that, by breaking
the legal assessment down into a series of simpler sub-decisions, even
laypersons can annotate consistently. Based on a newly annotated dataset, our
experiments show that directly learning an automated model of punishable
content is challenging. However, learning the two sub-tasks of `target group'
and `targeting conduct' instead of an end-to-end approach to punishability
yields better results. Overall, our method also provides decisions that are
more transparent than those of end-to-end models, which is a crucial point in
legal decision-making.
| 2,021 | Computation and Language |
Automated Utterance Generation | Conversational AI assistants are becoming popular and question-answering is
an important part of any conversational assistant. Using relevant utterances as
features in question-answering has shown to improve both the precision and
recall for retrieving the right answer by a conversational assistant. Hence,
utterance generation has become an important problem with the goal of
generating relevant utterances (sentences or phrases) from a knowledge base
article that consists of a title and a description. However, generating good
utterances usually requires a lot of manual effort, creating the need for an
automated utterance generation. In this paper, we propose an utterance
generation system which 1) uses extractive summarization to extract important
sentences from the description, 2) uses multiple paraphrasing techniques to
generate a diverse set of paraphrases of the title and summary sentences, and
3) selects good candidate paraphrases with the help of a novel candidate
selection algorithm.
| 2,020 | Computation and Language |
What do Models Learn from Question Answering Datasets? | While models have reached superhuman performance on popular question
answering (QA) datasets such as SQuAD, they have yet to outperform humans on
the task of question answering itself. In this paper, we investigate if models
are learning reading comprehension from QA datasets by evaluating BERT-based
models across five datasets. We evaluate models on their generalizability to
out-of-domain examples, responses to missing or incorrect data, and ability to
handle question variations. We find that no single dataset is robust to all of
our experiments and identify shortcomings in both datasets and evaluation
methods. Following our analysis, we make recommendations for building future QA
datasets that better evaluate the task of question answering through reading
comprehension. We also release code to convert QA datasets to a shared format
for easier experimentation at
https://github.com/amazon-research/qa-dataset-converter.
| 2,021 | Computation and Language |
Fine-Grained Named Entity Typing over Distantly Supervised Data Based on
Refined Representations | Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural
Language Processing (NLP). It aims at classifying an entity mention into a wide
range of entity types. Due to a large number of entity types, distant
supervision is used to collect training data for this task, which noisily
assigns type labels to entity mentions irrespective of the context. In order to
alleviate the noisy labels, existing approaches on FGNET analyze the entity
mentions entirely independent of each other and assign type labels solely based
on mention sentence-specific context. This is inadequate for highly overlapping
and noisy type labels as it hinders information passing across sentence
boundaries. For this, we propose an edge-weighted attentive graph convolution
network that refines the noisy mention representations by attending over
corpus-level contextual clues prior to the end classification. Experimental
evaluation shows that the proposed model outperforms the existing research by a
relative score of upto 10.2% and 8.3% for macro f1 and micro f1 respectively.
| 2,020 | Computation and Language |
Entity Linking via Dual and Cross-Attention Encoders | Entity Linking has two main open areas of research: 1) generate candidate
entities without using alias tables and 2) generate more contextual
representations for both mentions and entities. Recently, a solution has been
proposed for the former as a dual-encoder entity retrieval system (Gillick et
al., 2019) that learns mention and entity representations in the same space,
and performs linking by selecting the nearest entity to the mention in this
space. In this work, we use this retrieval system solely for generating
candidate entities. We then rerank the entities by using a cross-attention
encoder over the target mention and each of the candidate entities. Whereas a
dual encoder approach forces all information to be contained in the small,
fixed set of vector dimensions used to represent mentions and entities, a
crossattention model allows for the use of detailed information (read:
features) from the entirety of each <mention, context, candidate entity> tuple.
We experiment with features used in the reranker including different ways of
incorporating document-level context. We achieve state-of-the-art results on
TACKBP-2010 dataset, with 92.05% accuracy. Furthermore, we show how the
rescoring model generalizes well when trained on the larger CoNLL-2003 dataset
and evaluated on TACKBP-2010.
| 2,020 | Computation and Language |
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for
Span-based Question Answering | We introduce a novel approach to transformers that learns hierarchical
representations in multiparty dialogue. First, three language modeling tasks
are used to pre-train the transformers, token- and utterance-level language
modeling and utterance order prediction, that learn both token and utterance
embeddings for better understanding in dialogue contexts. Then, multi-task
learning between the utterance prediction and the token span prediction is
applied to fine-tune for span-based question answering (QA). Our approach is
evaluated on the FriendsQA dataset and shows improvements of 3.8% and 1.4% over
the two state-of-the-art transformer models, BERT and RoBERTa, respectively.
| 2,020 | Computation and Language |
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based
Chatbots | In this paper, we study the problem of employing pre-trained language models
for multi-turn response selection in retrieval-based chatbots. A new model,
named Speaker-Aware BERT (SA-BERT), is proposed in order to make the model
aware of the speaker change information, which is an important and intrinsic
property of multi-turn dialogues. Furthermore, a speaker-aware disentanglement
strategy is proposed to tackle the entangled dialogues. This strategy selects a
small number of most important utterances as the filtered context according to
the speakers' information in them. Finally, domain adaptation is performed to
incorporate the in-domain knowledge into pre-trained language models.
Experiments on five public datasets show that our proposed model outperforms
the present models on all metrics by large margins and achieves new
state-of-the-art performances for multi-turn response selection.
| 2,020 | Computation and Language |
Salience Estimation with Multi-Attention Learning for Abstractive Text
Summarization | Attention mechanism plays a dominant role in the sequence generation models
and has been used to improve the performance of machine translation and
abstractive text summarization. Different from neural machine translation, in
the task of text summarization, salience estimation for words, phrases or
sentences is a critical component, since the output summary is a distillation
of the input text. Although the typical attention mechanism can conduct text
fragment selection from the input text conditioned on the decoder states, there
is still a gap to conduct direct and effective salience detection. To bring
back direct salience estimation for summarization with neural networks, we
propose a Multi-Attention Learning framework which contains two new attention
learning components for salience estimation: supervised attention learning and
unsupervised attention learning. We regard the attention weights as the
salience information, which means that the semantic units with large attention
value will be more important. The context information obtained based on the
estimated salience is incorporated with the typical attention mechanism in the
decoder to conduct summary generation. Extensive experiments on some benchmark
datasets in different languages demonstrate the effectiveness of the proposed
framework for the task of abstractive summarization.
| 2,020 | Computation and Language |
TuringAdvice: A Generative and Dynamic Evaluation of Language Use | We propose TuringAdvice, a new challenge task and dataset for language
understanding models. Given a written situation that a real person is currently
facing, a model must generate helpful advice in natural language. Our
evaluation framework tests a fundamental aspect of human language
understanding: our ability to use language to resolve open-ended situations by
communicating with each other.
Empirical results show that today's models struggle at TuringAdvice, even
multibillion parameter models finetuned on 600k in-domain training examples.
The best model, a finetuned T5, writes advice that is at least as helpful as
human-written advice in only 14% of cases; a much larger non-finetunable GPT3
model does even worse at 4%. This low performance reveals language
understanding errors that are hard to spot outside of a generative setting,
showing much room for progress.
| 2,021 | Computation and Language |
Efficient long-distance relation extraction with DG-SpanBERT | In natural language processing, relation extraction seeks to rationally
understand unstructured text. Here, we propose a novel SpanBERT-based graph
convolutional network (DG-SpanBERT) that extracts semantic features from a raw
sentence using the pre-trained language model SpanBERT and a graph
convolutional network to pool latent features. Our DG-SpanBERT model inherits
the advantage of SpanBERT on learning rich lexical features from large-scale
corpus. It also has the ability to capture long-range relations between
entities due to the usage of GCN on dependency tree. The experimental results
show that our model outperforms other existing dependency-based and
sequence-based models and achieves a state-of-the-art performance on the TACRED
dataset.
| 2,021 | Computation and Language |
Re-translation versus Streaming for Simultaneous Translation | There has been great progress in improving streaming machine translation, a
simultaneous paradigm where the system appends to a growing hypothesis as more
source content becomes available. We study a related problem in which revisions
to the hypothesis beyond strictly appending words are permitted. This is
suitable for applications such as live captioning an audio feed. In this
setting, we compare custom streaming approaches to re-translation, a
straightforward strategy where each new source token triggers a distinct
translation from scratch. We find re-translation to be as good or better than
state-of-the-art streaming systems, even when operating under constraints that
allow very few revisions. We attribute much of this success to a previously
proposed data-augmentation technique that adds prefix-pairs to the training
data, which alongside wait-k inference forms a strong baseline for streaming
translation. We also highlight re-translation's ability to wrap arbitrarily
powerful MT systems with an experiment showing large improvements from an
upgrade to its base model.
| 2,020 | Computation and Language |
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions
and Effectiveness Detection in User Reviews | The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus
of consumer reviews in Russian about pharmaceutical products for the detection
of health-related named entities and the effectiveness of pharmaceutical
products. The corpus itself consists of two parts, the raw one and the labelled
one. The raw part includes 1.4 million health-related user-generated texts
collected from various Internet sources, including social media. The labelled
part contains 500 consumer reviews about drug therapy with drug- and
disease-related information. Labels for sentences include health-related issues
or their absence. The sentences with one are additionally labelled at the
expression level for identification of fine-grained subtypes such as drug
classes and drug forms, drug indications, and drug reactions. Further, we
present a baseline model for named entity recognition (NER) and multi-label
sentence classification tasks on this corpus. The macro F1 score of 74.85% in
the NER task was achieved by our RuDR-BERT model. For the sentence
classification task, our model achieves the macro F1 score of 68.82% gaining
7.47% over the score of BERT model trained on Russian data. We make the RuDReC
corpus and pretrained weights of domain-specific BERT models freely available
at https://github.com/cimm-kzn/RuDReC
| 2,020 | Computation and Language |
Dynamic Data Selection and Weighting for Iterative Back-Translation | Back-translation has proven to be an effective method to utilize monolingual
data in neural machine translation (NMT), and iteratively conducting
back-translation can further improve the model performance. Selecting which
monolingual data to back-translate is crucial, as we require that the resulting
synthetic data are of high quality and reflect the target domain. To achieve
these two goals, data selection and weighting strategies have been proposed,
with a common practice being to select samples close to the target domain but
also dissimilar to the average general-domain text. In this paper, we provide
insights into this commonly used approach and generalize it to a dynamic
curriculum learning strategy, which is applied to iterative back-translation
models. In addition, we propose weighting strategies based on both the current
quality of the sentence and its improvement over the previous iteration. We
evaluate our models on domain adaptation, low-resource, and high-resource MT
settings and on two language pairs. Experimental results demonstrate that our
methods achieve improvements of up to 1.8 BLEU points over competitive
baselines.
| 2,020 | Computation and Language |
Towards Faithfully Interpretable NLP Systems: How should we define and
evaluate faithfulness? | With the growing popularity of deep-learning based NLP models, comes a need
for interpretable systems. But what is interpretability, and what constitutes a
high-quality interpretation? In this opinion piece we reflect on the current
state of interpretability evaluation research. We call for more clearly
differentiating between different desired criteria an interpretation should
satisfy, and focus on the faithfulness criteria. We survey the literature with
respect to faithfulness evaluation, and arrange the current approaches around
three assumptions, providing an explicit form to how faithfulness is "defined"
by the community. We provide concrete guidelines on how evaluation of
interpretation methods should and should not be conducted. Finally, we claim
that the current binary definition for faithfulness sets a potentially
unrealistic bar for being considered faithful. We call for discarding the
binary notion of faithfulness in favor of a more graded one, which we believe
will be of greater practical utility.
| 2,020 | Computation and Language |
Deep Learning Based Text Classification: A Comprehensive Review | Deep learning based models have surpassed classical machine learning based
approaches in various text classification tasks, including sentiment analysis,
news categorization, question answering, and natural language inference. In
this paper, we provide a comprehensive review of more than 150 deep learning
based models for text classification developed in recent years, and discuss
their technical contributions, similarities, and strengths. We also provide a
summary of more than 40 popular datasets widely used for text classification.
Finally, we provide a quantitative analysis of the performance of different
deep learning models on popular benchmarks, and discuss future research
directions.
| 2,021 | Computation and Language |
Byte Pair Encoding is Suboptimal for Language Model Pretraining | The success of pretrained transformer language models (LMs) in natural
language processing has led to a wide range of pretraining setups. In
particular, these models employ a variety of subword tokenization methods, most
notably byte-pair encoding (BPE) (Sennrich et al., 2016; Gage, 1994), the
WordPiece method (Schuster and Nakajima, 2012), and unigram language modeling
(Kudo, 2018), to segment text. However, to the best of our knowledge, the
literature does not contain a direct evaluation of the impact of tokenization
on language model pretraining. We analyze differences between BPE and unigram
LM tokenization, finding that the latter method recovers subword units that
align more closely with morphology and avoids problems stemming from BPE's
greedy construction procedure. We then compare the fine-tuned task performance
of identical transformer masked language models pretrained with these
tokenizations. Across downstream tasks and two languages (English and
Japanese), we find that the unigram LM tokenization method matches or
outperforms BPE. We hope that developers of future pretrained LMs will consider
adopting the unigram LM method over the more prevalent BPE.
| 2,020 | Computation and Language |
Towards Evaluating the Robustness of Chinese BERT Classifiers | Recent advances in large-scale language representation models such as BERT
have improved the state-of-the-art performances in many NLP tasks. Meanwhile,
character-level Chinese NLP models, including BERT for Chinese, have also
demonstrated that they can outperform the existing models. In this paper, we
show that, however, such BERT-based models are vulnerable under character-level
adversarial attacks. We propose a novel Chinese char-level attack method
against BERT-based classifiers. Essentially, we generate "small" perturbation
on the character level in the embedding space and guide the character
substitution procedure. Extensive experiments show that the classification
accuracy on a Chinese news dataset drops from 91.8% to 0% by manipulating less
than 2 characters on average based on the proposed attack. Human evaluations
also confirm that our generated Chinese adversarial examples barely affect
human performance on these NLP tasks.
| 2,020 | Computation and Language |
e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language
Explanations | The recently proposed SNLI-VE corpus for recognising visual-textual
entailment is a large, real-world dataset for fine-grained multimodal
reasoning. However, the automatic way in which SNLI-VE has been assembled (via
combining parts of two related datasets) gives rise to a large number of errors
in the labels of this corpus. In this paper, we first present a data collection
effort to correct the class with the highest error rate in SNLI-VE. Secondly,
we re-evaluate an existing model on the corrected corpus, which we call
SNLI-VE-2.0, and provide a quantitative comparison with its performance on the
non-corrected corpus. Thirdly, we introduce e-SNLI-VE, which appends
human-written natural language explanations to SNLI-VE-2.0. Finally, we train
models that learn from these explanations at training time, and output such
explanations at testing time.
| 2,020 | Computation and Language |
Understanding Knowledge Gaps in Visual Question Answering: Implications
for Gap Identification and Testing | Visual Question Answering (VQA) systems are tasked with answering natural
language questions corresponding to a presented image. Traditional VQA datasets
typically contain questions related to the spatial information of objects,
object attributes, or general scene questions. Recently, researchers have
recognized the need to improve the balance of such datasets to reduce the
system's dependency on memorized linguistic features and statistical biases,
while aiming for enhanced visual understanding. However, it is unclear whether
any latent patterns exist to quantify and explain these failures. As an initial
step towards better quantifying our understanding of the performance of VQA
models, we use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or
more types of KGs. Each Knowledge Gap (KG) describes the reasoning abilities
needed to arrive at a resolution. After identifying KGs for each question, we
examine the skew in the distribution of questions for each KG. We then
introduce a targeted question generation model to reduce this skew, which
allows us to generate new types of questions for an image. These new questions
can be added to existing VQA datasets to increase the diversity of questions
and reduce the skew.
| 2,020 | Computation and Language |
DialBERT: A Hierarchical Pre-Trained Model for Conversation
Disentanglement | Disentanglement is a problem in which multiple conversations occur in the
same channel simultaneously, and the listener should decide which utterance is
part of the conversation he will respond to. We propose a new model, named
Dialogue BERT (DialBERT), which integrates local and global semantics in a
single stream of messages to disentangle the conversations that mixed together.
We employ BERT to capture the matching information in each utterance pair at
the utterance-level, and use a BiLSTM to aggregate and incorporate the
context-level information. With only a 3% increase in parameters, a 12%
improvement has been attained in comparison to BERT, based on the F1-Score. The
model achieves a state-of-the-art result on the a new dataset proposed by IBM
and surpasses previous work by a substantial margin.
| 2,021 | Computation and Language |
Generating Narrative Text in a Switching Dynamical System | Early work on narrative modeling used explicit plans and goals to generate
stories, but the language generation itself was restricted and inflexible.
Modern methods use language models for more robust generation, but often lack
an explicit representation of the scaffolding and dynamics that guide a
coherent narrative. This paper introduces a new model that integrates explicit
narrative structure with neural language models, formalizing narrative modeling
as a Switching Linear Dynamical System (SLDS). A SLDS is a dynamical system in
which the latent dynamics of the system (i.e. how the state vector transforms
over time) is controlled by top-level discrete switching variables. The
switching variables represent narrative structure (e.g., sentiment or discourse
states), while the latent state vector encodes information on the current state
of the narrative. This probabilistic formulation allows us to control
generation, and can be learned in a semi-supervised fashion using both labeled
and unlabeled data. Additionally, we derive a Gibbs sampler for our model that
can fill in arbitrary parts of the narrative, guided by the switching
variables. Our filled-in (English language) narratives outperform several
baselines on both automatic and human evaluations.
| 2,020 | Computation and Language |
Downstream Model Design of Pre-trained Language Model for Relation
Extraction Task | Supervised relation extraction methods based on deep neural network play an
important role in the recent information extraction field. However, at present,
their performance still fails to reach a good level due to the existence of
complicated relations. On the other hand, recently proposed pre-trained
language models (PLMs) have achieved great success in multiple tasks of natural
language processing through fine-tuning when combined with the model of
downstream tasks. However, original standard tasks of PLM do not include the
relation extraction task yet. We believe that PLMs can also be used to solve
the relation extraction problem, but it is necessary to establish a specially
designed downstream task model or even loss function for dealing with
complicated relations. In this paper, a new network architecture with a special
loss function is designed to serve as a downstream model of PLMs for supervised
relation extraction. Experiments have shown that our method significantly
exceeded the current optimal baseline models across multiple public datasets of
relation extraction.
| 2,020 | Computation and Language |
Satirical News Detection with Semantic Feature Extraction and
Game-theoretic Rough Sets | Satirical news detection is an important yet challenging task to prevent
spread of misinformation. Many feature based and end-to-end neural nets based
satirical news detection systems have been proposed and delivered promising
results. Existing approaches explore comprehensive word features from satirical
news articles, but lack semantic metrics using word vectors for tweet form
satirical news. Moreover, the vagueness of satire and news parody determines
that a news tweet can hardly be classified with a binary decision, that is,
satirical or legitimate. To address these issues, we collect satirical and
legitimate news tweets, and propose a semantic feature based approach. Features
are extracted by exploring inconsistencies in phrases, entities, and between
main and relative clauses. We apply game-theoretic rough set model to detect
satirical news, in which probabilistic thresholds are derived by game
equilibrium and repetition learning mechanism. Experimental results on the
collected dataset show the robustness and improvement of the proposed approach
compared with Pawlak rough set model and SVM.
| 2,020 | Computation and Language |
CALM: Continuous Adaptive Learning for Language Modeling | Training large language representation models has become a standard in the
natural language processing community. This allows for fine tuning on any
number of specific tasks, however, these large high capacity models can
continue to train on domain specific unlabeled data to make initialization even
more robust for supervised tasks. We demonstrate that in practice these
pre-trained models present performance deterioration in the form of
catastrophic forgetting when evaluated on tasks from a general domain such as
GLUE. In this work we propose CALM, Continuous Adaptive Learning for Language
Modeling: techniques to render models which retain knowledge across multiple
domains. With these methods, we are able to reduce the performance gap across
supervised tasks introduced by task specific models which we demonstrate using
a continual learning setting in biomedical and clinical domains.
| 2,020 | Computation and Language |
Improving BERT with Self-Supervised Attention | One of the most popular paradigms of applying large pre-trained NLP models
such as BERT is to fine-tune it on a smaller dataset. However, one challenge
remains as the fine-tuned model often overfits on smaller datasets. A symptom
of this phenomenon is that irrelevant or misleading words in the sentence,
which are easy to understand for human beings, can substantially degrade the
performance of these finetuned BERT models. In this paper, we propose a novel
technique, called Self-Supervised Attention (SSA) to help facilitate this
generalization challenge. Specifically, SSA automatically generates weak,
token-level attention labels iteratively by probing the fine-tuned model from
the previous iteration. We investigate two different ways of integrating SSA
into BERT and propose a hybrid approach to combine their benefits. Empirically,
through a variety of public datasets, we illustrate significant performance
improvement using our SSA-enhanced BERT model.
| 2,021 | Computation and Language |
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward
Decomposition | Many studies have applied reinforcement learning to train a dialog policy and
show great promise these years. One common approach is to employ a user
simulator to obtain a large number of simulated user experiences for
reinforcement learning algorithms. However, modeling a realistic user simulator
is challenging. A rule-based simulator requires heavy domain expertise for
complex tasks, and a data-driven simulator requires considerable data and it is
even unclear how to evaluate a simulator. To avoid explicitly building a user
simulator beforehand, we propose Multi-Agent Dialog Policy Learning, which
regards both the system and the user as the dialog agents. Two agents interact
with each other and are jointly learned simultaneously. The method uses the
actor-critic framework to facilitate pretraining and improve scalability. We
also propose Hybrid Value Network for the role-aware reward decomposition to
integrate role-specific domain knowledge of each agent in the task-oriented
dialog. Results show that our method can successfully build a system policy and
a user policy simultaneously, and two agents can achieve a high task success
rate through conversational interaction.
| 2,020 | Computation and Language |
Explicit Reordering for Neural Machine Translation | In Transformer-based neural machine translation (NMT), the positional
encoding mechanism helps the self-attention networks to learn the source
representation with order dependency, which makes the Transformer-based NMT
achieve state-of-the-art results for various translation tasks. However,
Transformer-based NMT only adds representations of positions sequentially to
word vectors in the input sentence and does not explicitly consider reordering
information in this sentence. In this paper, we first empirically investigate
the relationship between source reordering information and translation
performance. The empirical findings show that the source input with the target
order learned from the bilingual parallel dataset can substantially improve
translation performance. Thus, we propose a novel reordering method to
explicitly model this reordering information for the Transformer-based NMT. The
empirical results on the WMT14 English-to-German, WAT ASPEC
Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the
effectiveness of the proposed approach.
| 2,020 | Computation and Language |
SIA: A Scalable Interoperable Annotation Server for Biomedical Named
Entities | Recent years showed a strong increase in biomedical sciences and an inherent
increase in publication volume. Extraction of specific information from these
sources requires highly sophisticated text mining and information extraction
tools. However, the integration of freely available tools into customized
workflows is often cumbersome and difficult. We describe SIA (Scalable
Interoperable Annotation Server), our contribution to the BeCalm-Technical
interoperability and performance of annotation servers (BeCalm-TIPS) task, a
scalable, extensible, and robust annotation service. The system currently
covers six named entity types (i.e., Chemicals, Diseases, Genes, miRNA,
Mutations, and Organisms) and is freely available under Apache 2.0 license at
https://github.com/Erechtheus/sia.
| 2,018 | Computation and Language |
Exploring Versatile Generative Language Model Via Parameter-Efficient
Transfer Learning | Fine-tuning pre-trained generative language models to down-stream language
generation tasks has shown promising results. However, this comes with the cost
of having a single, large model for each task, which is not ideal in
low-memory/power scenarios (e.g., mobile). In this paper, we propose an
effective way to fine-tune multiple down-stream generation tasks simultaneously
using a single, large pre-trained model. The experiments on five diverse
language generation tasks show that by just using an additional 2-3% parameters
for each task, our model can maintain or even improve the performance of
fine-tuning the whole model.
| 2,020 | Computation and Language |
On the Effect of Dropping Layers of Pre-trained Transformer Models | Transformer-based NLP models are trained using hundreds of millions or even
billions of parameters, limiting their applicability in computationally
constrained environments. While the number of parameters generally correlates
with performance, it is not clear whether the entire network is required for a
downstream task. Motivated by the recent work on pruning and distilling
pre-trained models, we explore strategies to drop layers in pre-trained models,
and observe the effect of pruning on downstream GLUE tasks. We were able to
prune BERT, RoBERTa and XLNet models up to 40%, while maintaining up to 98% of
their original performance. Additionally we show that our pruned models are on
par with those built using knowledge distillation, both in terms of size and
performance. Our experiments yield interesting observations such as, (i) the
lower layers are most critical to maintain downstream task performance, (ii)
some tasks such as paraphrase detection and sentence similarity are more robust
to the dropping of layers, and (iii) models trained using a different objective
function exhibit different learning patterns and w.r.t the layer dropping.
| 2,022 | Computation and Language |
Structure-Level Knowledge Distillation For Multilingual Sequence
Labeling | Multilingual sequence labeling is a task of predicting label sequences using
a single unified model for multiple languages. Compared with relying on
multiple monolingual models, using a multilingual model has the benefit of a
smaller model size, easier in online serving, and generalizability to
low-resource languages. However, current multilingual models still underperform
individual monolingual models significantly due to model capacity limitations.
In this paper, we propose to reduce the gap between monolingual models and the
unified multilingual model by distilling the structural knowledge of several
monolingual models (teachers) to the unified multilingual model (student). We
propose two novel KD methods based on structure-level information: (1)
approximately minimizes the distance between the student's and the teachers'
structure level probability distributions, (2) aggregates the structure-level
knowledge to local distributions and minimizes the distance between two local
probability distributions. Our experiments on 4 multilingual tasks with 25
datasets show that our approaches outperform several strong baselines and have
stronger zero-shot generalizability than both the baseline model and teacher
models.
| 2,020 | Computation and Language |
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with
Second-Order Edge Inference for Cross-Framework Meaning Representation
Parsing | This paper presents the system used in our submission to the \textit{CoNLL
2019 shared task: Cross-Framework Meaning Representation Parsing}. Our system
is a graph-based parser which combines an extended pointer-generator network
that generates nodes and a second-order mean field variational inference module
that predicts edges. Our system achieved \nth{1} and \nth{2} place for the DM
and PSD frameworks respectively on the in-framework ranks and achieved \nth{3}
place for the DM framework on the cross-framework ranks.
| 2,020 | Computation and Language |
Internal and external pressures on language emergence: least effort,
object constancy and frequency | In previous work, artificial agents were shown to achieve almost perfect
accuracy in referential games where they have to communicate to identify
images. Nevertheless, the resulting communication protocols rarely display
salient features of natural languages, such as compositionality. In this paper,
we propose some realistic sources of pressure on communication that avert this
outcome. More specifically, we formalise the principle of least effort through
an auxiliary objective. Moreover, we explore several game variants, inspired by
the principle of object constancy, in which we alter the frequency, position,
and luminosity of the objects in the images. We perform an extensive analysis
on their effect through compositionality metrics, diagnostic classifiers, and
zero-shot evaluation. Our findings reveal that the proposed sources of pressure
result in emerging languages with less redundancy, more focus on high-level
conceptual information, and better abilities of generalisation. Overall, our
contributions reduce the gap between emergent and natural languages.
| 2,020 | Computation and Language |
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News
Multi-Headline Generation | News headline generation aims to produce a short sentence to attract readers
to read the news. One news article often contains multiple keyphrases that are
of interest to different users, which can naturally have multiple reasonable
headlines. However, most existing methods focus on the single headline
generation. In this paper, we propose generating multiple headlines with
keyphrases of user interests, whose main idea is to generate multiple
keyphrases of interest to users for the news first, and then generate multiple
keyphrase-relevant headlines. We propose a multi-source Transformer decoder,
which takes three sources as inputs: (a) keyphrase, (b) keyphrase-filtered
article, and (c) original article to generate keyphrase-relevant, high-quality,
and diverse headlines. Furthermore, we propose a simple and effective method to
mine the keyphrases of interest in the news article and build a first
large-scale keyphrase-aware news headline corpus, which contains over 180K
aligned triples of $<$news article, headline, keyphrase$>$. Extensive
experimental comparisons on the real-world dataset show that the proposed
method achieves state-of-the-art results in terms of quality and diversity
| 2,020 | Computation and Language |
Deep daxes: Mutual exclusivity arises through both learning biases and
pragmatic strategies in neural networks | Children's tendency to associate novel words with novel referents has been
taken to reflect a bias toward mutual exclusivity. This tendency may be
advantageous both as (1) an ad-hoc referent selection heuristic to single out
referents lacking a label and as (2) an organizing principle of lexical
acquisition. This paper investigates under which circumstances
cross-situational neural models can come to exhibit analogous behavior to
children, focusing on these two possibilities and their interaction. To this
end, we evaluate neural networks' on both symbolic data and, as a first, on
large-scale image data. We find that constraints in both learning and selection
can foster mutual exclusivity, as long as they put words in competition for
lexical meaning. For computational models, these findings clarify the role of
available options for better performance in tasks where mutual exclusivity is
advantageous. For cognitive research, they highlight latent interactions
between word learning, referent selection mechanisms, and the structure of
stimuli of varying complexity: symbolic and visual.
| 2,020 | Computation and Language |
Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders | The ability to combine symbols to generate language is a defining
characteristic of human intelligence, particularly in the context of artistic
story-telling through lyrics. We develop a method for synthesizing a rap verse
based on the content of any text (e.g., a news article), or for augmenting
pre-existing rap lyrics. Our method, called Rapformer, is based on training a
Transformer-based denoising autoencoder to reconstruct rap lyrics from content
words extracted from the lyrics, trying to preserve the essential meaning,
while matching the target style. Rapformer features a novel BERT-based
paraphrasing scheme for rhyme enhancement which increases the average rhyme
density of output lyrics by 10%. Experimental results on three diverse input
domains show that Rapformer is capable of generating technically fluent verses
that offer a good trade-off between content preservation and style transfer.
Furthermore, a Turing-test-like experiment reveals that Rapformer fools human
lyrics experts 25% of the time.
| 2,020 | Computation and Language |
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve
Topic Coherence | Topic models extract groups of words from documents, whose interpretation as
a topic hopefully allows for a better understanding of the data. However, the
resulting word groups are often not coherent, making them harder to interpret.
Recently, neural topic models have shown improvements in overall coherence.
Concurrently, contextual embeddings have advanced the state of the art of
neural models in general. In this paper, we combine contextualized
representations with neural topic models. We find that our approach produces
more meaningful and coherent topics than traditional bag-of-words topic models
and recent neural models. Our results indicate that future improvements in
language models will translate into better topic models.
| 2,021 | Computation and Language |
Transfer learning and subword sampling for asymmetric-resource
one-to-many neural translation | There are several approaches for improving neural machine translation for
low-resource languages: Monolingual data can be exploited via pretraining or
data augmentation; Parallel corpora on related language pairs can be used via
parameter sharing or transfer learning in multilingual models; Subword
segmentation and regularization techniques can be applied to ensure high
coverage of the vocabulary. We review these approaches in the context of an
asymmetric-resource one-to-many translation task, in which the pair of target
languages are related, with one being a very low-resource and the other a
higher-resource language. We test various methods on three artificially
restricted translation tasks -- English to Estonian (low-resource) and Finnish
(high-resource), English to Slovak and Czech, English to Danish and Swedish --
and one real-world task, Norwegian to North S\'ami and Finnish. The experiments
show positive effects especially for scheduled multi-task learning, denoising
autoencoder, and subword sampling.
| 2,020 | Computation and Language |
Analyzing Redundancy in Pretrained Transformer Models | Transformer-based deep NLP models are trained using hundreds of millions of
parameters, limiting their applicability in computationally constrained
environments. In this paper, we study the cause of these limitations by
defining a notion of Redundancy, which we categorize into two classes: General
Redundancy and Task-specific Redundancy. We dissect two popular pretrained
models, BERT and XLNet, studying how much redundancy they exhibit at a
representation-level and at a more fine-grained neuron-level. Our analysis
reveals interesting insights, such as: i) 85% of the neurons across the network
are redundant and ii) at least 92% of them can be removed when optimizing
towards a downstream task. Based on our analysis, we present an efficient
feature-based transfer learning procedure, which maintains 97% performance
while using at-most 10% of the original neurons.
| 2,020 | Computation and Language |
DynaBERT: Dynamic BERT with Adaptive Width and Depth | The pre-trained language models like BERT, though powerful in many natural
language processing tasks, are both computation and memory expensive. To
alleviate this problem, one approach is to compress them for specific tasks
before deployment. However, recent works on BERT compression usually compress
the large BERT model to a fixed smaller size. They can not fully satisfy the
requirements of different edge devices with various hardware performances. In
this paper, we propose a novel dynamic BERT model (abbreviated as DynaBERT),
which can flexibly adjust the size and latency by selecting adaptive width and
depth. The training process of DynaBERT includes first training a
width-adaptive BERT and then allowing both adaptive width and depth, by
distilling knowledge from the full-sized model to small sub-networks. Network
rewiring is also used to keep the more important attention heads and neurons
shared by more sub-networks. Comprehensive experiments under various efficiency
constraints demonstrate that our proposed dynamic BERT (or RoBERTa) at its
largest size has comparable performance as BERT-base (or RoBERTa-base), while
at smaller widths and depths consistently outperforms existing BERT compression
methods. Code is available at
https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT.
| 2,020 | Computation and Language |
Self-Attention Gazetteer Embeddings for Named-Entity Recognition | Recent attempts to ingest external knowledge into neural models for
named-entity recognition (NER) have exhibited mixed results. In this work, we
present GazSelfAttn, a novel gazetteer embedding approach that uses
self-attention and match span encoding to build enhanced gazetteer embeddings.
In addition, we demonstrate how to build gazetteer resources from the open
source Wikidata knowledge base. Evaluations on CoNLL-03 and Ontonotes 5
datasets, show F1 improvements over baseline model from 92.34 to 92.86 and
89.11 to 89.32 respectively, achieving performance comparable to large
state-of-the-art models.
| 2,020 | Computation and Language |
Are All Good Word Vector Spaces Isomorphic? | Existing algorithms for aligning cross-lingual word vector spaces assume that
vector spaces are approximately isomorphic. As a result, they perform poorly or
fail completely on non-isomorphic spaces. Such non-isomorphism has been
hypothesised to result from typological differences between languages. In this
work, we ask whether non-isomorphism is also crucially a sign of degenerate
word vector spaces. We present a series of experiments across diverse languages
which show that variance in performance across language pairs is not only due
to typological differences, but can mostly be attributed to the size of the
monolingual resources available, and to the properties and duration of
monolingual training (e.g. "under-training").
| 2,020 | Computation and Language |
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space | When trained effectively, the Variational Autoencoder (VAE) can be both a
powerful generative model and an effective representation learning framework
for natural language. In this paper, we propose the first large-scale language
VAE model, Optimus. A universal latent embedding space for sentences is first
pre-trained on large text corpus, and then fine-tuned for various language
generation and understanding tasks. Compared with GPT-2, Optimus enables guided
language generation from an abstract level using the latent vectors. Compared
with BERT, Optimus can generalize better on low-resource language understanding
tasks due to the smooth latent space structure. Extensive experimental results
on a wide range of language tasks demonstrate the effectiveness of Optimus. It
achieves new state-of-the-art on VAE language modeling benchmarks. We hope that
our first pre-trained big VAE language model itself and results can help the
NLP community renew the interests of deep generative models in the era of
large-scale pre-training, and make these principled methods more practical.
| 2,020 | Computation and Language |
KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn
Knowledge-driven Conversation | The research of knowledge-driven conversational systems is largely limited
due to the lack of dialog data which consist of multi-turn conversations on
multiple topics and with knowledge annotations. In this paper, we propose a
Chinese multi-domain knowledge-driven conversation dataset, KdConv, which
grounds the topics in multi-turn conversations to knowledge graphs. Our corpus
contains 4.5K conversations from three domains (film, music, and travel), and
86K utterances with an average turn number of 19.0. These conversations contain
in-depth discussions on related topics and natural transition between multiple
topics. To facilitate the following research on this corpus, we provide several
benchmark models. Comparative results show that the models can be enhanced by
introducing background knowledge, yet there is still a large space for
leveraging knowledge to model multi-turn conversations for further research.
Results also show that there are obvious performance differences between
different domains, indicating that it is worth to further explore transfer
learning and domain adaptation. The corpus and benchmark models are publicly
available.
| 2,020 | Computation and Language |
Cross-lingual Emotion Intensity Prediction | Emotion intensity prediction determines the degree or intensity of an emotion
that the author expresses in a text, extending previous categorical approaches
to emotion detection. While most previous work on this topic has concentrated
on English texts, other languages would also benefit from fine-grained emotion
classification, preferably without having to recreate the amount of annotated
data available in English in each new language. Consequently, we explore
cross-lingual transfer approaches for fine-grained emotion detection in Spanish
and Catalan tweets. To this end we annotate a test set of Spanish and Catalan
tweets using Best-Worst scaling. We compare six cross-lingual approaches, e.g.,
machine translation and cross-lingual embeddings, which have varying
requirements for parallel data -- from millions of parallel sentences to
completely unsupervised. The results show that on this data, methods with low
parallel-data requirements perform surprisingly better than methods that use
more parallel data, which we explain through an in-depth error analysis. We
make the dataset and the code available at
\url{https://github.com/jerbarnes/fine-grained_cross-lingual_emotion}
| 2,020 | Computation and Language |
Frequency, Acceptability, and Selection: A case study of
clause-embedding | We investigate the relationship between the frequency with which verbs are
found in particular subcategorization frames and the acceptability of those
verbs in those frames, focusing in particular on subordinate clause-taking
verbs, such as "think", "want", and "tell". We show that verbs'
subcategorization frame frequency distributions are poor predictors of their
acceptability in those frames---explaining, at best, less than 1/3 of the total
information about acceptability across the lexicon---and, further, that common
matrix factorization techniques used to model the acquisition of verbs'
acceptability in subcategorization frames fare only marginally better. All data
and code are available at http://megaattitude.io.
| 2,020 | Computation and Language |
Entity-Switched Datasets: An Approach to Auditing the In-Domain
Robustness of Named Entity Recognition Models | Named entity recognition systems perform well on standard datasets comprising
English news. But given the paucity of data, it is difficult to draw
conclusions about the robustness of systems with respect to recognizing a
diverse set of entities. We propose a method for auditing the in-domain
robustness of systems, focusing specifically on differences in performance due
to the national origin of entities. We create entity-switched datasets, in
which named entities in the original texts are replaced by plausible named
entities of the same type but of different national origin. We find that
state-of-the-art systems' performance vary widely even in-domain: In the same
context, entities from certain origins are more reliably recognized than
entities from elsewhere. Systems perform best on American and Indian entities,
and worst on Vietnamese and Indonesian entities. This auditing approach can
facilitate the development of more robust named entity recognition systems, and
will allow research in this area to consider fairness criteria that have
received heightened attention in other predictive technology work.
| 2,021 | Computation and Language |
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