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Learning Relation Ties with a Force-Directed Graph in Distant Supervised
Relation Extraction | Relation ties, defined as the correlation and mutual exclusion between
different relations, are critical for distant supervised relation extraction.
Existing approaches model this property by greedily learning local
dependencies. However, they are essentially limited by failing to capture the
global topology structure of relation ties. As a result, they may easily fall
into a locally optimal solution. To solve this problem, in this paper, we
propose a novel force-directed graph based relation extraction model to
comprehensively learn relation ties. Specifically, we first build a graph
according to the global co-occurrence of relations. Then, we borrow the idea of
Coulomb's Law from physics and introduce the concept of attractive force and
repulsive force to this graph to learn correlation and mutual exclusion between
relations. Finally, the obtained relation representations are applied as an
inter-dependent relation classifier. Experimental results on a large scale
benchmark dataset demonstrate that our model is capable of modeling global
relation ties and significantly outperforms other baselines. Furthermore, the
proposed force-directed graph can be used as a module to augment existing
relation extraction systems and improve their performance.
| 2,020 | Computation and Language |
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent
Detection and Slot Filling | In real-world scenarios, users usually have multiple intents in the same
utterance. Unfortunately, most spoken language understanding (SLU) models
either mainly focused on the single intent scenario, or simply incorporated an
overall intent context vector for all tokens, ignoring the fine-grained
multiple intents information integration for token-level slot prediction. In
this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint
multiple intent detection and slot filling, where we introduce an intent-slot
graph interaction layer to model the strong correlation between the slot and
intents. Such an interaction layer is applied to each token adaptively, which
has the advantage to automatically extract the relevant intents information,
making a fine-grained intent information integration for the token-level slot
prediction. Experimental results on three multi-intent datasets show that our
framework obtains substantial improvement and achieves the state-of-the-art
performance. In addition, our framework achieves new state-of-the-art
performance on two single-intent datasets.
| 2,020 | Computation and Language |
Curriculum Pre-training for End-to-End Speech Translation | End-to-end speech translation poses a heavy burden on the encoder, because it
has to transcribe, understand, and learn cross-lingual semantics
simultaneously. To obtain a powerful encoder, traditional methods pre-train it
on ASR data to capture speech features. However, we argue that pre-training the
encoder only through simple speech recognition is not enough and high-level
linguistic knowledge should be considered. Inspired by this, we propose a
curriculum pre-training method that includes an elementary course for
transcription learning and two advanced courses for understanding the utterance
and mapping words in two languages. The difficulty of these courses is
gradually increasing. Experiments show that our curriculum pre-training method
leads to significant improvements on En-De and En-Fr speech translation
benchmarks.
| 2,020 | Computation and Language |
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms | Attention is a key component of Transformers, which have recently achieved
considerable success in natural language processing. Hence, attention is being
extensively studied to investigate various linguistic capabilities of
Transformers, focusing on analyzing the parallels between attention weights and
specific linguistic phenomena. This paper shows that attention weights alone
are only one of the two factors that determine the output of attention and
proposes a norm-based analysis that incorporates the second factor, the norm of
the transformed input vectors. The findings of our norm-based analyses of BERT
and a Transformer-based neural machine translation system include the
following: (i) contrary to previous studies, BERT pays poor attention to
special tokens, and (ii) reasonable word alignment can be extracted from
attention mechanisms of Transformer. These findings provide insights into the
inner workings of Transformers.
| 2,020 | Computation and Language |
Unsupervised Opinion Summarization with Noising and Denoising | The supervised training of high-capacity models on large datasets containing
hundreds of thousands of document-summary pairs is critical to the recent
success of deep learning techniques for abstractive summarization.
Unfortunately, in most domains (other than news) such training data is not
available and cannot be easily sourced. In this paper we enable the use of
supervised learning for the setting where there are only documents available
(e.g.,~product or business reviews) without ground truth summaries. We create a
synthetic dataset from a corpus of user reviews by sampling a review,
pretending it is a summary, and generating noisy versions thereof which we
treat as pseudo-review input. We introduce several linguistically motivated
noise generation functions and a summarization model which learns to denoise
the input and generate the original review. At test time, the model accepts
genuine reviews and generates a summary containing salient opinions, treating
those that do not reach consensus as noise. Extensive automatic and human
evaluation shows that our model brings substantial improvements over both
abstractive and extractive baselines.
| 2,020 | Computation and Language |
Experience Grounds Language | Language understanding research is held back by a failure to relate language
to the physical world it describes and to the social interactions it
facilitates. Despite the incredible effectiveness of language processing models
to tackle tasks after being trained on text alone, successful linguistic
communication relies on a shared experience of the world. It is this shared
experience that makes utterances meaningful.
Natural language processing is a diverse field, and progress throughout its
development has come from new representational theories, modeling techniques,
data collection paradigms, and tasks. We posit that the present success of
representation learning approaches trained on large, text-only corpora requires
the parallel tradition of research on the broader physical and social context
of language to address the deeper questions of communication.
| 2,020 | Computation and Language |
Logic-Guided Data Augmentation and Regularization for Consistent
Question Answering | Many natural language questions require qualitative, quantitative or logical
comparisons between two entities or events. This paper addresses the problem of
improving the accuracy and consistency of responses to comparison questions by
integrating logic rules and neural models. Our method leverages logical and
linguistic knowledge to augment labeled training data and then uses a
consistency-based regularizer to train the model. Improving the global
consistency of predictions, our approach achieves large improvements over
previous methods in a variety of question answering (QA) tasks including
multiple-choice qualitative reasoning, cause-effect reasoning, and extractive
machine reading comprehension. In particular, our method significantly improves
the performance of RoBERTa-based models by 1-5% across datasets. We advance the
state of the art by around 5-8% on WIQA and QuaRel and reduce consistency
violations by 58% on HotpotQA. We further demonstrate that our approach can
learn effectively from limited data.
| 2,020 | Computation and Language |
Knowledge Distillation for Multilingual Unsupervised Neural Machine
Translation | Unsupervised neural machine translation (UNMT) has recently achieved
remarkable results for several language pairs. However, it can only translate
between a single language pair and cannot produce translation results for
multiple language pairs at the same time. That is, research on multilingual
UNMT has been limited. In this paper, we empirically introduce a simple method
to translate between thirteen languages using a single encoder and a single
decoder, making use of multilingual data to improve UNMT for all language
pairs. On the basis of the empirical findings, we propose two knowledge
distillation methods to further enhance multilingual UNMT performance. Our
experiments on a dataset with English translated to and from twelve other
languages (including three language families and six language branches) show
remarkable results, surpassing strong unsupervised individual baselines while
achieving promising performance between non-English language pairs in zero-shot
translation scenarios and alleviating poor performance in low-resource language
pairs.
| 2,020 | Computation and Language |
Residual Energy-Based Models for Text | Current large-scale auto-regressive language models display impressive
fluency and can generate convincing text. In this work we start by asking the
question: Can the generations of these models be reliably distinguished from
real text by statistical discriminators? We find experimentally that the answer
is affirmative when we have access to the training data for the model, and
guardedly affirmative even if we do not.
This suggests that the auto-regressive models can be improved by
incorporating the (globally normalized) discriminators into the generative
process. We give a formalism for this using the Energy-Based Model framework,
and show that it indeed improves the results of the generative models, measured
both in terms of perplexity and in terms of human evaluation.
| 2,020 | Computation and Language |
Domain-Guided Task Decomposition with Self-Training for Detecting
Personal Events in Social Media | Mining social media content for tasks such as detecting personal experiences
or events, suffer from lexical sparsity, insufficient training data, and
inventive lexicons. To reduce the burden of creating extensive labeled data and
improve classification performance, we propose to perform these tasks in two
steps: 1. Decomposing the task into domain-specific sub-tasks by identifying
key concepts, thus utilizing human domain understanding; and 2. Combining the
results of learners for each key concept using co-training to reduce the
requirements for labeled training data. We empirically show the effectiveness
and generality of our approach, Co-Decomp, using three representative social
media mining tasks, namely Personal Health Mention detection, Crisis Report
detection, and Adverse Drug Reaction monitoring. The experiments show that our
model is able to outperform the state-of-the-art text classification
models--including those using the recently introduced BERT model--when small
amounts of training data are available.
| 2,020 | Computation and Language |
MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask
Learning | Clinical notes contain an abundance of important but not-readily accessible
information about patients. Systems to automatically extract this information
rely on large amounts of training data for which their exists limited resources
to create. Furthermore, they are developed dis-jointly; meaning that no
information can be shared amongst task-specific systems. This bottle-neck
unnecessarily complicates practical application, reduces the performance
capabilities of each individual solution and associates the engineering debt of
managing multiple information extraction systems. We address these challenges
by developing Multitask-Clinical BERT: a single deep learning model that
simultaneously performs eight clinical tasks spanning entity extraction, PHI
identification, language entailment and similarity by sharing representations
amongst tasks. We find our single system performs competitively with all
state-the-art task-specific systems while also benefiting from massive
computational benefits at inference.
| 2,020 | Computation and Language |
ESPnet-ST: All-in-One Speech Translation Toolkit | We present ESPnet-ST, which is designed for the quick development of
speech-to-speech translation systems in a single framework. ESPnet-ST is a new
project inside end-to-end speech processing toolkit, ESPnet, which integrates
or newly implements automatic speech recognition, machine translation, and
text-to-speech functions for speech translation. We provide all-in-one recipes
including data pre-processing, feature extraction, training, and decoding
pipelines for a wide range of benchmark datasets. Our reproducible results can
match or even outperform the current state-of-the-art performances; these
pre-trained models are downloadable. The toolkit is publicly available at
https://github.com/espnet/espnet.
| 2,020 | Computation and Language |
Learnings from Technological Interventions in a Low Resource Language: A
Case-Study on Gondi | The primary obstacle to developing technologies for low-resource languages is
the lack of usable data. In this paper, we report the adoption and deployment
of 4 technology-driven methods of data collection for Gondi, a low-resource
vulnerable language spoken by around 2.3 million tribal people in south and
central India. In the process of data collection, we also help in its revival
by expanding access to information in Gondi through the creation of linguistic
resources that can be used by the community, such as a dictionary, children's
stories, an app with Gondi content from multiple sources and an Interactive
Voice Response (IVR) based mass awareness platform. At the end of these
interventions, we collected a little less than 12,000 translated words and/or
sentences and identified more than 650 community members whose help can be
solicited for future translation efforts. The larger goal of the project is
collecting enough data in Gondi to build and deploy viable language
technologies like machine translation and speech to text systems that can help
take the language onto the internet.
| 2,021 | Computation and Language |
Observations on Annotations | The annotation of textual information is a fundamental activity in
Linguistics and Computational Linguistics. This article presents various
observations on annotations. It approaches the topic from several angles
including Hypertext, Computational Linguistics and Language Technology,
Artificial Intelligence and Open Science. Annotations can be examined along
different dimensions. In terms of complexity, they can range from trivial to
highly sophisticated, in terms of maturity from experimental to standardised.
Annotations can be annotated themselves using more abstract annotations.
Primary research data such as, e.g., text documents can be annotated on
different layers concurrently, which are independent but can be exploited using
multi-layer querying. Standards guarantee interoperability and reusability of
data sets. The chapter concludes with four final observations, formulated as
research questions or rather provocative remarks on the current state of
annotation research.
| 2,020 | Computation and Language |
A Deep Learning System for Sentiment Analysis of Service Calls | Sentiment analysis is crucial for the advancement of artificial intelligence
(AI). Sentiment understanding can help AI to replicate human language and
discourse. Studying the formation and response of sentiment state from
well-trained Customer Service Representatives (CSRs) can help make the
interaction between humans and AI more intelligent. In this paper, a sentiment
analysis pipeline is first carried out with respect to real-world multi-party
conversations - that is, service calls. Based on the acoustic and linguistic
features extracted from the source information, a novel aggregated method for
voice sentiment recognition framework is built. Each party's sentiment pattern
during the communication is investigated along with the interaction sentiment
pattern between all parties.
| 2,020 | Computation and Language |
Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in
Hindi and Punjabi | Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one
exception: whether a schwa represented in the orthography is pronounced or
unpronounced (deleted). Previous work has attempted to predict schwa deletion
in a rule-based fashion using prosodic or phonetic analysis. We present the
first statistical schwa deletion classifier for Hindi, which relies solely on
the orthography as the input and outperforms previous approaches. We trained
our model on a newly-compiled pronunciation lexicon extracted from various
online dictionaries. Our best Hindi model achieves state of the art
performance, and also achieves good performance on a closely related language,
Punjabi, without modification.
| 2,020 | Computation and Language |
Testing Machine Translation via Referential Transparency | Machine translation software has seen rapid progress in recent years due to
the advancement of deep neural networks. People routinely use machine
translation software in their daily lives, such as ordering food in a foreign
restaurant, receiving medical diagnosis and treatment from foreign doctors, and
reading international political news online. However, due to the complexity and
intractability of the underlying neural networks, modern machine translation
software is still far from robust and can produce poor or incorrect
translations; this can lead to misunderstanding, financial loss, threats to
personal safety and health, and political conflicts. To address this problem,
we introduce referentially transparent inputs (RTIs), a simple, widely
applicable methodology for validating machine translation software. A
referentially transparent input is a piece of text that should have similar
translations when used in different contexts. Our practical implementation,
Purity, detects when this property is broken by a translation. To evaluate RTI,
we use Purity to test Google Translate and Bing Microsoft Translator with 200
unlabeled sentences, which detected 123 and 142 erroneous translations with
high precision (79.3% and 78.3%). The translation errors are diverse, including
examples of under-translation, over-translation, word/phrase mistranslation,
incorrect modification, and unclear logic.
| 2,021 | Computation and Language |
Logical Natural Language Generation from Open-Domain Tables | Neural natural language generation (NLG) models have recently shown
remarkable progress in fluency and coherence. However, existing studies on
neural NLG are primarily focused on surface-level realizations with limited
emphasis on logical inference, an important aspect of human thinking and
language. In this paper, we suggest a new NLG task where a model is tasked with
generating natural language statements that can be \emph{logically entailed} by
the facts in an open-domain semi-structured table. To facilitate the study of
the proposed logical NLG problem, we use the existing TabFact dataset
\cite{chen2019tabfact} featured with a wide range of logical/symbolic
inferences as our testbed, and propose new automatic metrics to evaluate the
fidelity of generation models w.r.t.\ logical inference. The new task poses
challenges to the existing monotonic generation frameworks due to the mismatch
between sequence order and logical order. In our experiments, we
comprehensively survey different generation architectures (LSTM, Transformer,
Pre-Trained LM) trained with different algorithms (RL, Adversarial Training,
Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained
LM can significantly boost both the fluency and logical fidelity metrics, 2) RL
and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine
generation can help partially alleviate the fidelity issue while maintaining
high language fluency. The code and data are available at
\url{https://github.com/wenhuchen/LogicNLG}.
| 2,020 | Computation and Language |
Trading Off Diversity and Quality in Natural Language Generation | For open-ended language generation tasks such as storytelling and dialogue,
choosing the right decoding algorithm is critical to controlling the tradeoff
between generation quality and diversity. However, there presently exists no
consensus on which decoding procedure is best or even the criteria by which to
compare them. We address these issues by casting decoding as a multi-objective
optimization problem aiming to simultaneously maximize both response quality
and diversity. Our framework enables us to perform the first large-scale
evaluation of decoding methods along the entire quality-diversity spectrum. We
find that when diversity is a priority, all methods perform similarly, but when
quality is viewed as more important, the recently proposed nucleus sampling
(Holtzman et al. 2019) outperforms all other evaluated decoding algorithms. Our
experiments also confirm the existence of the `likelihood trap', the
counter-intuitive observation that high likelihood sequences are often
surprisingly low quality. We leverage our findings to create and evaluate an
algorithm called \emph{selective sampling} which tractably approximates
globally-normalized temperature sampling.
| 2,020 | Computation and Language |
A Study of Non-autoregressive Model for Sequence Generation | Non-autoregressive (NAR) models generate all the tokens of a sequence in
parallel, resulting in faster generation speed compared to their autoregressive
(AR) counterparts but at the cost of lower accuracy. Different techniques
including knowledge distillation and source-target alignment have been proposed
to bridge the gap between AR and NAR models in various tasks such as neural
machine translation (NMT), automatic speech recognition (ASR), and text to
speech (TTS). With the help of those techniques, NAR models can catch up with
the accuracy of AR models in some tasks but not in some others. In this work,
we conduct a study to understand the difficulty of NAR sequence generation and
try to answer: (1) Why NAR models can catch up with AR models in some tasks but
not all? (2) Why techniques like knowledge distillation and source-target
alignment can help NAR models. Since the main difference between AR and NAR
models is that NAR models do not use dependency among target tokens while AR
models do, intuitively the difficulty of NAR sequence generation heavily
depends on the strongness of dependency among target tokens. To quantify such
dependency, we propose an analysis model called CoMMA to characterize the
difficulty of different NAR sequence generation tasks. We have several
interesting findings: 1) Among the NMT, ASR and TTS tasks, ASR has the most
target-token dependency while TTS has the least. 2) Knowledge distillation
reduces the target-token dependency in target sequence and thus improves the
accuracy of NAR models. 3) Source-target alignment constraint encourages
dependency of a target token on source tokens and thus eases the training of
NAR models.
| 2,020 | Computation and Language |
Keyphrase Prediction With Pre-trained Language Model | Recently, generative methods have been widely used in keyphrase prediction,
thanks to their capability to produce both present keyphrases that appear in
the source text and absent keyphrases that do not match any source text.
However, the absent keyphrases are generated at the cost of the performance on
present keyphrase prediction, since previous works mainly use generative models
that rely on the copying mechanism and select words step by step. Besides, the
extractive model that directly extracts a text span is more suitable for
predicting the present keyphrase. Considering the different characteristics of
extractive and generative methods, we propose to divide the keyphrase
prediction into two subtasks, i.e., present keyphrase extraction (PKE) and
absent keyphrase generation (AKG), to fully exploit their respective
advantages. On this basis, a joint inference framework is proposed to make the
most of BERT in two subtasks. For PKE, we tackle this task as a sequence
labeling problem with the pre-trained language model BERT. For AKG, we
introduce a Transformer-based architecture, which fully integrates the present
keyphrase knowledge learned from PKE by the fine-tuned BERT. The experimental
results show that our approach can achieve state-of-the-art results on both
tasks on benchmark datasets.
| 2,020 | Computation and Language |
Where is the context? -- A critique of recent dialogue datasets | Recent dialogue datasets like MultiWOZ 2.1 and Taskmaster-1 constitute some
of the most challenging tasks for present-day dialogue models and, therefore,
are widely used for system evaluation. We identify several issues with the
above-mentioned datasets, such as history independence, strong knowledge base
dependence, and ambiguous system responses. Finally, we outline key desiderata
for future datasets that we believe would be more suitable for the construction
of conversational artificial intelligence.
| 2,020 | Computation and Language |
When and Why is Unsupervised Neural Machine Translation Useless? | This paper studies the practicality of the current state-of-the-art
unsupervised methods in neural machine translation (NMT). In ten translation
tasks with various data settings, we analyze the conditions under which the
unsupervised methods fail to produce reasonable translations. We show that
their performance is severely affected by linguistic dissimilarity and domain
mismatch between source and target monolingual data. Such conditions are common
for low-resource language pairs, where unsupervised learning works poorly. In
all of our experiments, supervised and semi-supervised baselines with
50k-sentence bilingual data outperform the best unsupervised results. Our
analyses pinpoint the limits of the current unsupervised NMT and also suggest
immediate research directions.
| 2,020 | Computation and Language |
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised
Prerequisite Chain Learning | The task of concept prerequisite chain learning is to automatically determine
the existence of prerequisite relationships among concept pairs. In this paper,
we frame learning prerequisite relationships among concepts as an unsupervised
task with no access to labeled concept pairs during training. We propose a
model called the Relational-Variational Graph AutoEncoder (R-VGAE) to predict
concept relations within a graph consisting of concept and resource nodes.
Results show that our unsupervised approach outperforms graph-based
semi-supervised methods and other baseline methods by up to 9.77% and 10.47% in
terms of prerequisite relation prediction accuracy and F1 score. Our method is
notably the first graph-based model that attempts to make use of deep learning
representations for the task of unsupervised prerequisite learning. We also
expand an existing corpus which totals 1,717 English Natural Language
Processing (NLP)-related lecture slide files and manual concept pair
annotations over 322 topics.
| 2,020 | Computation and Language |
Contextualised Graph Attention for Improved Relation Extraction | This paper presents a contextualized graph attention network that combines
edge features and multiple sub-graphs for improving relation extraction. A
novel method is proposed to use multiple sub-graphs to learn rich node
representations in graph-based networks. To this end multiple sub-graphs are
obtained from a single dependency tree. Two types of edge features are
proposed, which are effectively combined with GAT and GCN models to apply for
relation extraction. The proposed model achieves state-of-the-art performance
on Semeval 2010 Task 8 dataset, achieving an F1-score of 86.3.
| 2,020 | Computation and Language |
Semantic Entity Enrichment by Leveraging Multilingual Descriptions for
Link Prediction | Most Knowledge Graphs (KGs) contain textual descriptions of entities in
various natural languages. These descriptions of entities provide valuable
information that may not be explicitly represented in the structured part of
the KG. Based on this fact, some link prediction methods which make use of the
information presented in the textual descriptions of entities have been
proposed to learn representations of (monolingual) KGs. However, these methods
use entity descriptions in only one language and ignore the fact that
descriptions given in different languages may provide complementary information
and thereby also additional semantics. In this position paper, the problem of
effectively leveraging multilingual entity descriptions for the purpose of link
prediction in KGs will be discussed along with potential solutions to the
problem.
| 2,020 | Computation and Language |
Universal Dependencies v2: An Evergrowing Multilingual Treebank
Collection | Universal Dependencies is an open community effort to create
cross-linguistically consistent treebank annotation for many languages within a
dependency-based lexicalist framework. The annotation consists in a
linguistically motivated word segmentation; a morphological layer comprising
lemmas, universal part-of-speech tags, and standardized morphological features;
and a syntactic layer focusing on syntactic relations between predicates,
arguments and modifiers. In this paper, we describe version 2 of the guidelines
(UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of
the currently available treebanks for 90 languages.
| 2,020 | Computation and Language |
AmbigQA: Answering Ambiguous Open-domain Questions | Ambiguity is inherent to open-domain question answering; especially when
exploring new topics, it can be difficult to ask questions that have a single,
unambiguous answer. In this paper, we introduce AmbigQA, a new open-domain
question answering task which involves finding every plausible answer, and then
rewriting the question for each one to resolve the ambiguity. To study this
task, we construct AmbigNQ, a dataset covering 14,042 questions from NQ-open,
an existing open-domain QA benchmark. We find that over half of the questions
in NQ-open are ambiguous, with diverse sources of ambiguity such as event and
entity references. We also present strong baseline models for AmbigQA which we
show benefit from weakly supervised learning that incorporates NQ-open,
strongly suggesting our new task and data will support significant future
research effort. Our data and baselines are available at
https://nlp.cs.washington.edu/ambigqa.
| 2,020 | Computation and Language |
Fast and Scalable Dialogue State Tracking with Explicit Modular
Decomposition | We present a fast and scalable architecture called Explicit Modular
Decomposition (EMD), in which we incorporate both classification-based and
extraction-based methods and design four modules (for classification and
sequence labelling) to jointly extract dialogue states. Experimental results
based on the MultiWoz 2.0 dataset validates the superiority of our proposed
model in terms of both complexity and scalability when compared to the
state-of-the-art methods, especially in the scenario of multi-domain dialogues
entangled with many turns of utterances.
| 2,021 | Computation and Language |
Categories of Semantic Concepts | Modelling concept representation is a foundational problem in the study of
cognition and linguistics. This work builds on the confluence of conceptual
tools from G\"ardenfors semantic spaces, categorical compositional linguistics,
and applied category theory to present a domain-independent and categorical
formalism of 'concept'.
| 2,020 | Computation and Language |
Learning to Classify Intents and Slot Labels Given a Handful of Examples | Intent classification (IC) and slot filling (SF) are core components in most
goal-oriented dialogue systems. Current IC/SF models perform poorly when the
number of training examples per class is small. We propose a new few-shot
learning task, few-shot IC/SF, to study and improve the performance of IC and
SF models on classes not seen at training time in ultra low resource scenarios.
We establish a few-shot IC/SF benchmark by defining few-shot splits for three
public IC/SF datasets, ATIS, TOP, and Snips. We show that two popular few-shot
learning algorithms, model agnostic meta learning (MAML) and prototypical
networks, outperform a fine-tuning baseline on this benchmark. Prototypical
networks achieves significant gains in IC performance on the ATIS and TOP
datasets, while both prototypical networks and MAML outperform the baseline
with respect to SF on all three datasets. In addition, we demonstrate that
joint training as well as the use of pre-trained language models, ELMo and BERT
in our case, are complementary to these few-shot learning methods and yield
further gains.
| 2,020 | Computation and Language |
Polarized-VAE: Proximity Based Disentangled Representation Learning for
Text Generation | Learning disentangled representations of real-world data is a challenging
open problem. Most previous methods have focused on either supervised
approaches which use attribute labels or unsupervised approaches that
manipulate the factorization in the latent space of models such as the
variational autoencoder (VAE) by training with task-specific losses. In this
work, we propose polarized-VAE, an approach that disentangles select attributes
in the latent space based on proximity measures reflecting the similarity
between data points with respect to these attributes. We apply our method to
disentangle the semantics and syntax of sentences and carry out transfer
experiments. Polarized-VAE outperforms the VAE baseline and is competitive with
state-of-the-art approaches, while being more a general framework that is
applicable to other attribute disentanglement tasks.
| 2,021 | Computation and Language |
Revisiting the Context Window for Cross-lingual Word Embeddings | Existing approaches to mapping-based cross-lingual word embeddings are based
on the assumption that the source and target embedding spaces are structurally
similar. The structures of embedding spaces largely depend on the co-occurrence
statistics of each word, which the choice of context window determines. Despite
this obvious connection between the context window and mapping-based
cross-lingual embeddings, their relationship has been underexplored in prior
work. In this work, we provide a thorough evaluation, in various languages,
domains, and tasks, of bilingual embeddings trained with different context
windows. The highlight of our findings is that increasing the size of both the
source and target window sizes improves the performance of bilingual lexicon
induction, especially the performance on frequent nouns.
| 2,020 | Computation and Language |
ParsEL 1.0: Unsupervised Entity Linking in Persian Social Media Texts | In recent years, social media data has exponentially increased, which can be
enumerated as one of the largest data repositories in the world. A large
portion of this social media data is natural language text. However, the
natural language is highly ambiguous due to exposure to the frequent
occurrences of entities, which have polysemous words or phrases. Entity linking
is the task of linking the entity mentions in the text to their corresponding
entities in a knowledge base. Recently, FarsBase, a Persian knowledge graph,
has been introduced containing almost half a million entities. In this paper,
we propose an unsupervised Persian Entity Linking system, the first entity
linking system specially focused on the Persian language, which utilizes
context-dependent and context-independent features. For this purpose, we also
publish the first entity linking corpus of the Persian language containing
67,595 words that have been crawled from social media texts of some popular
channels in the Telegram messenger. The output of the proposed method is 86.94%
f-score for the Persian language, which is comparable with the similar
state-of-the-art methods in the English language.
| 2,020 | Computation and Language |
Syntactic Structure from Deep Learning | Modern deep neural networks achieve impressive performance in engineering
applications that require extensive linguistic skills, such as machine
translation. This success has sparked interest in probing whether these models
are inducing human-like grammatical knowledge from the raw data they are
exposed to, and, consequently, whether they can shed new light on long-standing
debates concerning the innate structure necessary for language acquisition. In
this article, we survey representative studies of the syntactic abilities of
deep networks, and discuss the broader implications that this work has for
theoretical linguistics.
| 2,020 | Computation and Language |
Dense Embeddings Preserving the Semantic Relationships in WordNet | In this paper, we provide a novel way to generate low dimensional vector
embeddings for the noun and verb synsets in WordNet, where the hypernym-hyponym
relationship is preserved in the embeddings. We call this embedding the Sense
Spectrum (and Sense Spectra for embeddings). In order to create suitable labels
for the training of sense spectra, we designed a new similarity measurement for
noun and verb synsets in WordNet. We call this similarity measurement the
Hypernym Intersection Similarity (HIS), since it compares the common and unique
hypernyms between two synsets. Our experiments show that on the noun and verb
pairs of the SimLex-999 dataset, HIS outperforms the three similarity
measurements in WordNet. Moreover, to the best of our knowledge, the sense
spectra provide the first dense synset embeddings that preserve the semantic
relationships in WordNet.
| 2,022 | Computation and Language |
What are We Depressed about When We Talk about COVID19: Mental Health
Analysis on Tweets Using Natural Language Processing | The outbreak of coronavirus disease 2019 (COVID-19) recently has affected
human life to a great extent. Besides direct physical and economic threats, the
pandemic also indirectly impact people's mental health conditions, which can be
overwhelming but difficult to measure. The problem may come from various
reasons such as unemployment status, stay-at-home policy, fear for the virus,
and so forth. In this work, we focus on applying natural language processing
(NLP) techniques to analyze tweets in terms of mental health. We trained deep
models that classify each tweet into the following emotions: anger,
anticipation, disgust, fear, joy, sadness, surprise and trust. We build the
EmoCT (Emotion-Covid19-Tweet) dataset for the training purpose by manually
labeling 1,000 English tweets. Furthermore, we propose and compare two methods
to find out the reasons that are causing sadness and fear.
| 2,020 | Computation and Language |
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks | Language models pretrained on text from a wide variety of sources form the
foundation of today's NLP. In light of the success of these broad-coverage
models, we investigate whether it is still helpful to tailor a pretrained model
to the domain of a target task. We present a study across four domains
(biomedical and computer science publications, news, and reviews) and eight
classification tasks, showing that a second phase of pretraining in-domain
(domain-adaptive pretraining) leads to performance gains, under both high- and
low-resource settings. Moreover, adapting to the task's unlabeled data
(task-adaptive pretraining) improves performance even after domain-adaptive
pretraining. Finally, we show that adapting to a task corpus augmented using
simple data selection strategies is an effective alternative, especially when
resources for domain-adaptive pretraining might be unavailable. Overall, we
consistently find that multi-phase adaptive pretraining offers large gains in
task performance.
| 2,020 | Computation and Language |
Visual Question Answering Using Semantic Information from Image
Descriptions | In this work, we propose a deep neural architecture that uses an attention
mechanism which utilizes region based image features, the natural language
question asked, and semantic knowledge extracted from the regions of an image
to produce open-ended answers for questions asked in a visual question
answering (VQA) task. The combination of both region based features and region
based textual information about the image bolsters a model to more accurately
respond to questions and potentially do so with less required training data. We
evaluate our proposed architecture on a VQA task against a strong baseline and
show that our method achieves excellent results on this task.
| 2,021 | Computation and Language |
Semi-Supervised Models via Data Augmentationfor Classifying Interactive
Affective Responses | We present semi-supervised models with data augmentation (SMDA), a
semi-supervised text classification system to classify interactive affective
responses. SMDA utilizes recent transformer-based models to encode each
sentence and employs back translation techniques to paraphrase given sentences
as augmented data. For labeled sentences, we performed data augmentations to
uniform the label distributions and computed supervised loss during training
process. For unlabeled sentences, we explored self-training by regarding
low-entropy predictions over unlabeled sentences as pseudo labels, assuming
high-confidence predictions as labeled data for training. We further introduced
consistency regularization as unsupervised loss after data augmentations on
unlabeled data, based on the assumption that the model should predict similar
class distributions with original unlabeled sentences as input and augmented
sentences as input. Via a set of experiments, we demonstrated that our system
outperformed baseline models in terms of F1-score and accuracy.
| 2,020 | Computation and Language |
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog | Recent studies have shown remarkable success in end-to-end task-oriented
dialog system. However, most neural models rely on large training data, which
are only available for a certain number of task domains, such as navigation and
scheduling.
This makes it difficult to scalable for a new domain with limited labeled
data. However, there has been relatively little research on how to effectively
use data from all domains to improve the performance of each domain and also
unseen domains. To this end, we investigate methods that can make explicit use
of domain knowledge and introduce a shared-private network to learn shared and
specific knowledge. In addition, we propose a novel Dynamic Fusion Network
(DF-Net) which automatically exploit the relevance between the target domain
and each domain. Results show that our model outperforms existing methods on
multi-domain dialogue, giving the state-of-the-art in the literature. Besides,
with little training data, we show its transferability by outperforming prior
best model by 13.9\% on average.
| 2,020 | Computation and Language |
QURIOUS: Question Generation Pretraining for Text Generation | Recent trends in natural language processing using pretraining have shifted
focus towards pretraining and fine-tuning approaches for text generation. Often
the focus has been on task-agnostic approaches that generalize the language
modeling objective. We propose question generation as a pretraining method,
which better aligns with the text generation objectives. Our text generation
models pretrained with this method are better at understanding the essence of
the input and are better language models for the target task. When evaluated on
two text generation tasks, abstractive summarization and answer-focused
question generation, our models result in state-of-the-art performances in
terms of automatic metrics. Human evaluators also found our summaries and
generated questions to be more natural, concise and informative.
| 2,020 | Computation and Language |
Learning Dialog Policies from Weak Demonstrations | Deep reinforcement learning is a promising approach to training a dialog
manager, but current methods struggle with the large state and action spaces of
multi-domain dialog systems. Building upon Deep Q-learning from Demonstrations
(DQfD), an algorithm that scores highly in difficult Atari games, we leverage
dialog data to guide the agent to successfully respond to a user's requests. We
make progressively fewer assumptions about the data needed, using labeled,
reduced-labeled, and even unlabeled data to train expert demonstrators. We
introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to
overcome the domain gap between the datasets and the environment. Experiments
in a challenging multi-domain dialog system framework validate our approaches,
and get high success rates even when trained on out-of-domain data.
| 2,020 | Computation and Language |
Coupling semantic and statistical techniques for dynamically enriching
web ontologies | With the development of the Semantic Web technology, the use of ontologies to
store and retrieve information covering several domains has increased. However,
very few ontologies are able to cope with the ever-growing need of frequently
updated semantic information or specific user requirements in specialized
domains. As a result, a critical issue is related to the unavailability of
relational information between concepts, also coined missing background
knowledge. One solution to address this issue relies on the manual enrichment
of ontologies by domain experts which is however a time consuming and costly
process, hence the need for dynamic ontology enrichment. In this paper we
present an automatic coupled statistical/semantic framework for dynamically
enriching large-scale generic ontologies from the World Wide Web. Using the
massive amount of information encoded in texts on the Web as a corpus, missing
background knowledge can therefore be discovered through a combination of
semantic relatedness measures and pattern acquisition techniques and
subsequently exploited. The benefits of our approach are: (i) proposing the
dynamic enrichment of large-scale generic ontologies with missing background
knowledge, and thus, enabling the reuse of such knowledge, (ii) dealing with
the issue of costly ontological manual enrichment by domain experts.
Experimental results in a precision-based evaluation setting demonstrate the
effectiveness of the proposed techniques.
| 2,013 | Computation and Language |
Coupled intrinsic and extrinsic human language resource-based query
expansion | Poor information retrieval performance has often been attributed to the
query-document vocabulary mismatch problem which is defined as the difficulty
for human users to formulate precise natural language queries that are in line
with the vocabulary of the documents deemed relevant to a specific search goal.
To alleviate this problem, query expansion processes are applied in order to
spawn and integrate additional terms to an initial query. This requires
accurate identification of main query concepts to ensure the intended search
goal is duly emphasized and relevant expansion concepts are extracted and
included in the enriched query. Natural language queries have intrinsic
linguistic properties such as parts-of-speech labels and grammatical relations
which can be utilized in determining the intended search goal. Additionally,
extrinsic language-based resources such as ontologies are needed to suggest
expansion concepts semantically coherent with the query content. We present
here a query expansion framework which capitalizes on both linguistic
characteristics of user queries and ontology resources for query constituent
encoding, expansion concept extraction and concept weighting. A thorough
empirical evaluation on real-world datasets validates our approach against
unigram language model, relevance model and a sequential dependence based
technique.
| 2,018 | Computation and Language |
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and
Generalization of Machine Reading Comprehension in Real-World Applications | Machine reading comprehension (MRC) is a crucial task in natural language
processing and has achieved remarkable advancements. However, most of the
neural MRC models are still far from robust and fail to generalize well in
real-world applications. In order to comprehensively verify the robustness and
generalization of MRC models, we introduce a real-world Chinese dataset --
DuReader_robust. It is designed to evaluate the MRC models from three aspects:
over-sensitivity, over-stability and generalization. Comparing to previous
work, the instances in DuReader_robust are natural texts, rather than the
altered unnatural texts. It presents the challenges when applying MRC models to
real-world applications. The experimental results show that MRC models do not
perform well on the challenge test set. Moreover, we analyze the behavior of
existing models on the challenge test set, which may provide suggestions for
future model development. The dataset and codes are publicly available at
https://github.com/baidu/DuReader.
| 2,021 | Computation and Language |
On Adversarial Examples for Biomedical NLP Tasks | The success of pre-trained word embeddings has motivated its use in tasks in
the biomedical domain. The BERT language model has shown remarkable results on
standard performance metrics in tasks such as Named Entity Recognition (NER)
and Semantic Textual Similarity (STS), which has brought significant progress
in the field of NLP. However, it is unclear whether these systems work
seemingly well in critical domains, such as legal or medical. For that reason,
in this work, we propose an adversarial evaluation scheme on two well-known
datasets for medical NER and STS. We propose two types of attacks inspired by
natural spelling errors and typos made by humans. We also propose another type
of attack that uses synonyms of medical terms. Under these adversarial
settings, the accuracy of the models drops significantly, and we quantify the
extent of this performance loss. We also show that we can significantly improve
the robustness of the models by training them with adversarial examples. We
hope our work will motivate the use of adversarial examples to evaluate and
develop models with increased robustness for medical tasks.
| 2,020 | Computation and Language |
Same Side Stance Classification Task: Facilitating Argument Stance
Classification by Fine-tuning a BERT Model | Research on computational argumentation is currently being intensively
investigated. The goal of this community is to find the best pro and con
arguments for a user given topic either to form an opinion for oneself, or to
persuade others to adopt a certain standpoint. While existing argument mining
methods can find appropriate arguments for a topic, a correct classification
into pro and con is not yet reliable. The same side stance classification task
provides a dataset of argument pairs classified by whether or not both
arguments share the same stance and does not need to distinguish between
topic-specific pro and con vocabulary but only the argument similarity within a
stance needs to be assessed. The results of our contribution to the task are
build on a setup based on the BERT architecture. We fine-tuned a pre-trained
BERT model for three epochs and used the first 512 tokens of each argument to
predict if two arguments share the same stance.
| 2,020 | Computation and Language |
Self-Attention Attribution: Interpreting Information Interactions Inside
Transformer | The great success of Transformer-based models benefits from the powerful
multi-head self-attention mechanism, which learns token dependencies and
encodes contextual information from the input. Prior work strives to attribute
model decisions to individual input features with different saliency measures,
but they fail to explain how these input features interact with each other to
reach predictions. In this paper, we propose a self-attention attribution
method to interpret the information interactions inside Transformer. We take
BERT as an example to conduct extensive studies. Firstly, we apply
self-attention attribution to identify the important attention heads, while
others can be pruned with marginal performance degradation. Furthermore, we
extract the most salient dependencies in each layer to construct an attribution
tree, which reveals the hierarchical interactions inside Transformer. Finally,
we show that the attribution results can be used as adversarial patterns to
implement non-targeted attacks towards BERT.
| 2,021 | Computation and Language |
Correct Me If You Can: Learning from Error Corrections and Markings | Sequence-to-sequence learning involves a trade-off between signal strength
and annotation cost of training data. For example, machine translation data
range from costly expert-generated translations that enable supervised
learning, to weak quality-judgment feedback that facilitate reinforcement
learning. We present the first user study on annotation cost and machine
learnability for the less popular annotation mode of error markings. We show
that error markings for translations of TED talks from English to German allow
precise credit assignment while requiring significantly less human effort than
correcting/post-editing, and that error-marked data can be used successfully to
fine-tune neural machine translation models.
| 2,020 | Computation and Language |
Adaptive Forgetting Curves for Spaced Repetition Language Learning | The forgetting curve has been extensively explored by psychologists,
educationalists and cognitive scientists alike. In the context of Intelligent
Tutoring Systems, modelling the forgetting curve for each user and knowledge
component (e.g. vocabulary word) should enable us to develop optimal revision
strategies that counteract memory decay and ensure long-term retention. In this
study we explore a variety of forgetting curve models incorporating
psychological and linguistic features, and we use these models to predict the
probability of word recall by learners of English as a second language. We
evaluate the impact of the models and their features using data from an online
vocabulary teaching platform and find that word complexity is a highly
informative feature which may be successfully learned by a neural network
model.
| 2,020 | Computation and Language |
Rapidly Bootstrapping a Question Answering Dataset for COVID-19 | We present CovidQA, the beginnings of a question answering dataset
specifically designed for COVID-19, built by hand from knowledge gathered from
Kaggle's COVID-19 Open Research Dataset Challenge. To our knowledge, this is
the first publicly available resource of its type, and intended as a stopgap
measure for guiding research until more substantial evaluation resources become
available. While this dataset, comprising 124 question-article pairs as of the
present version 0.1 release, does not have sufficient examples for supervised
machine learning, we believe that it can be helpful for evaluating the
zero-shot or transfer capabilities of existing models on topics specifically
related to COVID-19. This paper describes our methodology for constructing the
dataset and presents the effectiveness of a number of baselines, including
term-based techniques and various transformer-based models. The dataset is
available at http://covidqa.ai/
| 2,020 | Computation and Language |
Transliteration of Judeo-Arabic Texts into Arabic Script Using Recurrent
Neural Networks | We trained a model to automatically transliterate Judeo-Arabic texts into
Arabic script, enabling Arabic readers to access those writings. We employ a
recurrent neural network (RNN), combined with the connectionist temporal
classification (CTC) loss to deal with unequal input/output lengths. This
obligates adjustments in the training data to avoid input sequences that are
shorter than their corresponding outputs. We also utilize a pretraining stage
with a different loss function to improve network converge. Since only a single
source of parallel text was available for training, we take advantage of the
possibility of generating data synthetically. We train a model that has the
capability to memorize words in the output language, and that also utilizes
context for distinguishing ambiguities in the transliteration. We obtain an
improvement over the baseline 9.5% character error, achieving 2% error with our
best configuration. To measure the contribution of context to learning, we also
tested word-shuffled data, for which the error rises to 2.5%.
| 2,020 | Computation and Language |
A Gamma-Poisson Mixture Topic Model for Short Text | Most topic models are constructed under the assumption that documents follow
a multinomial distribution. The Poisson distribution is an alternative
distribution to describe the probability of count data. For topic modelling,
the Poisson distribution describes the number of occurrences of a word in
documents of fixed length. The Poisson distribution has been successfully
applied in text classification, but its application to topic modelling is not
well documented, specifically in the context of a generative probabilistic
model. Furthermore, the few Poisson topic models in literature are admixture
models, making the assumption that a document is generated from a mixture of
topics. In this study, we focus on short text. Many studies have shown that the
simpler assumption of a mixture model fits short text better. With mixture
models, as opposed to admixture models, the generative assumption is that a
document is generated from a single topic. One topic model, which makes this
one-topic-per-document assumption, is the Dirichlet-multinomial mixture model.
The main contributions of this work are a new Gamma-Poisson mixture model, as
well as a collapsed Gibbs sampler for the model. The benefit of the collapsed
Gibbs sampler derivation is that the model is able to automatically select the
number of topics contained in the corpus. The results show that the
Gamma-Poisson mixture model performs better than the Dirichlet-multinomial
mixture model at selecting the number of topics in labelled corpora.
Furthermore, the Gamma-Poisson mixture produces better topic coherence scores
than the Dirichlet-multinomial mixture model, thus making it a viable option
for the challenging task of topic modelling of short text.
| 2,020 | Computation and Language |
A Tool for Facilitating OCR Postediting in Historical Documents | Optical character recognition (OCR) for historical documents is a complex
procedure subject to a unique set of material issues, including inconsistencies
in typefaces and low quality scanning. Consequently, even the most
sophisticated OCR engines produce errors. This paper reports on a tool built
for postediting the output of Tesseract, more specifically for correcting
common errors in digitized historical documents. The proposed tool suggests
alternatives for word forms not found in a specified vocabulary. The assumed
error is replaced by a presumably correct alternative in the post-edition based
on the scores of a Language Model (LM). The tool is tested on a chapter of the
book An Essay Towards Regulating the Trade and Employing the Poor of this
Kingdom (Cary ,1719). As demonstrated below, the tool is successful in
correcting a number of common errors. If sometimes unreliable, it is also
transparent and subject to human intervention.
| 2,020 | Computation and Language |
Multiple Segmentations of Thai Sentences for Neural Machine Translation | Thai is a low-resource language, so it is often the case that data is not
available in sufficient quantities to train an Neural Machine Translation (NMT)
model which perform to a high level of quality. In addition, the Thai script
does not use white spaces to delimit the boundaries between words, which adds
more complexity when building sequence to sequence models. In this work, we
explore how to augment a set of English--Thai parallel data by replicating
sentence-pairs with different word segmentation methods on Thai, as training
data for NMT model training. Using different merge operations of Byte Pair
Encoding, different segmentations of Thai sentences can be obtained. The
experiments show that combining these datasets, performance is improved for NMT
models trained with a dataset that has been split using a supervised splitting
tool.
| 2,020 | Computation and Language |
UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer
Networks for Offensive Language Detection | Fine-tuning of pre-trained transformer networks such as BERT yield
state-of-the-art results for text classification tasks. Typically, fine-tuning
is performed on task-specific training datasets in a supervised manner. One can
also fine-tune in unsupervised manner beforehand by further pre-training the
masked language modeling (MLM) task. Hereby, in-domain data for unsupervised
MLM resembling the actual classification target dataset allows for domain
adaptation of the model. In this paper, we compare current pre-trained
transformer networks with and without MLM fine-tuning on their performance for
offensive language detection. Our MLM fine-tuned RoBERTa-based classifier
officially ranks 1st in the SemEval 2020 Shared Task~12 for the English
language. Further experiments with the ALBERT model even surpass this result.
| 2,020 | Computation and Language |
Generative Data Augmentation for Commonsense Reasoning | Recent advances in commonsense reasoning depend on large-scale
human-annotated training data to achieve peak performance. However, manual
curation of training examples is expensive and has been shown to introduce
annotation artifacts that neural models can readily exploit and overfit on. We
investigate G-DAUG^C, a novel generative data augmentation method that aims to
achieve more accurate and robust learning in the low-resource setting. Our
approach generates synthetic examples using pretrained language models, and
selects the most informative and diverse set of examples for data augmentation.
In experiments with multiple commonsense reasoning benchmarks, G-DAUG^C
consistently outperforms existing data augmentation methods based on
back-translation, and establishes a new state-of-the-art on WinoGrande, CODAH,
and CommonsenseQA. Further, in addition to improvements in in-distribution
accuracy, G-DAUG^C-augmented training also enhances out-of-distribution
generalization, showing greater robustness against adversarial or perturbed
examples. Our analysis demonstrates that G-DAUG^C produces a diverse set of
fluent training examples, and that its selection and training approaches are
important for performance. Our findings encourage future research toward
generative data augmentation to enhance both in-distribution learning and
out-of-distribution generalization.
| 2,022 | Computation and Language |
Probabilistically Masked Language Model Capable of Autoregressive
Generation in Arbitrary Word Order | Masked language model and autoregressive language model are two types of
language models. While pretrained masked language models such as BERT overwhelm
the line of natural language understanding (NLU) tasks, autoregressive language
models such as GPT are especially capable in natural language generation (NLG).
In this paper, we propose a probabilistic masking scheme for the masked
language model, which we call probabilistically masked language model (PMLM).
We implement a specific PMLM with a uniform prior distribution on the masking
ratio named u-PMLM. We prove that u-PMLM is equivalent to an autoregressive
permutated language model. One main advantage of the model is that it supports
text generation in arbitrary order with surprisingly good quality, which could
potentially enable new applications over traditional unidirectional generation.
Besides, the pretrained u-PMLM also outperforms BERT on a set of downstream NLU
tasks.
| 2,020 | Computation and Language |
Customization and modifications of SignWriting by LIS users | Historically, the various sign languages (SL) have not developed an own
writing system; nevertheless, some systems exist, among which the SignWriting
(SW) is a powerful and flexible one. In this paper, we present the mechanisms
adopted by signers of the Italian Sign Language (LIS), expert users of SW, to
modify the standard SW glyphs and increase their writing skills and/or
represent peculiar linguistic phenomena. We identify these glyphs and show
which characteristics make them "acceptable" by the expert community.
Eventually, we analyze the potentialities of these glyphs in hand writing and
in computer-assisted writing, focusing on SWift, a software designed to allow
the electronic writing-down of user-modified glyphs.
| 2,020 | Computation and Language |
Learning the grammar of drug prescription: recurrent neural network
grammars for medication information extraction in clinical texts | In this study, we evaluated the RNNG, a neural top-down transition based
parser, for medication information extraction in clinical texts. We evaluated
this model on a French clinical corpus. The task was to extract the name of a
drug (or a drug class), as well as attributes informing its administration:
frequency, dosage, duration, condition and route of administration. We compared
the RNNG model that jointly identifies entities, events and their relations
with separate BiLSTMs models for entities, events and relations as baselines.
We call seq-BiLSTMs the baseline models for relations extraction that takes as
extra-input the output of the BiLSTMs for entities and events. Similarly, we
evaluated seq-RNNG, a hybrid RNNG model that takes as extra-input the output of
the BiLSTMs for entities and events. RNNG outperforms seq-BiLSTM for
identifying complex relations, with on average 88.1 [84.4-91.6] % versus 69.9
[64.0-75.4] F-measure. However, RNNG tends to be weaker than the baseline
BiLSTM on detecting entities, with on average 82.4 [80.8-83.8] versus 84.1
[82.7-85.6] % F- measure. RNNG trained only for detecting relations tends to be
weaker than RNNG with the joint modelling objective, 87.4% [85.8-88.8] versus
88.5% [87.2-89.8]. Seq-RNNG is on par with BiLSTM for entities (84.0
[82.6-85.4] % F-measure) and with RNNG for relations (88.7 [87.4-90.0] %
F-measure). The performance of RNNG on relations can be explained both by the
model architecture, which provides inductive bias to capture the hierarchy in
the targets, and the joint modeling objective which allows the RNNG to learn
richer representations. RNNG is efficient for modeling relations between
entities or/and events in medical texts and its performances are close to those
of a BiLSTM for entity and event detection.
| 2,022 | Computation and Language |
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News
Detection on Social Media | This paper solves the fake news detection problem under a more realistic
scenario on social media. Given the source short-text tweet and the
corresponding sequence of retweet users without text comments, we aim at
predicting whether the source tweet is fake or not, and generating explanation
by highlighting the evidences on suspicious retweeters and the words they
concern. We develop a novel neural network-based model, Graph-aware
Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments
conducted on real tweet datasets exhibit that GCAN can significantly outperform
state-of-the-art methods by 16% in accuracy on average. In addition, the case
studies also show that GCAN can produce reasonable explanations.
| 2,020 | Computation and Language |
Residual Energy-Based Models for Text Generation | Text generation is ubiquitous in many NLP tasks, from summarization, to
dialogue and machine translation. The dominant parametric approach is based on
locally normalized models which predict one word at a time. While these work
remarkably well, they are plagued by exposure bias due to the greedy nature of
the generation process. In this work, we investigate un-normalized energy-based
models (EBMs) which operate not at the token but at the sequence level. In
order to make training tractable, we first work in the residual of a pretrained
locally normalized language model and second we train using noise contrastive
estimation. Furthermore, since the EBM works at the sequence level, we can
leverage pretrained bi-directional contextual representations, such as BERT and
RoBERTa. Our experiments on two large language modeling datasets show that
residual EBMs yield lower perplexity compared to locally normalized baselines.
Moreover, generation via importance sampling is very efficient and of higher
quality than the baseline models according to human evaluation.
| 2,020 | Computation and Language |
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling | As an essential task in task-oriented dialog systems, slot filling requires
extensive training data in a certain domain. However, such data are not always
available. Hence, cross-domain slot filling has naturally arisen to cope with
this data scarcity problem. In this paper, we propose a Coarse-to-fine approach
(Coach) for cross-domain slot filling. Our model first learns the general
pattern of slot entities by detecting whether the tokens are slot entities or
not. It then predicts the specific types for the slot entities. In addition, we
propose a template regularization approach to improve the adaptation robustness
by regularizing the representation of utterances based on utterance templates.
Experimental results show that our model significantly outperforms
state-of-the-art approaches in slot filling. Furthermore, our model can also be
applied to the cross-domain named entity recognition task, and it achieves
better adaptation performance than other existing baselines. The code is
available at https://github.com/zliucr/coach.
| 2,020 | Computation and Language |
ST$^2$: Small-data Text Style Transfer via Multi-task Meta-Learning | Text style transfer aims to paraphrase a sentence in one style into another
style while preserving content. Due to lack of parallel training data,
state-of-art methods are unsupervised and rely on large datasets that share
content. Furthermore, existing methods have been applied on very limited
categories of styles such as positive/negative and formal/informal. In this
work, we develop a meta-learning framework to transfer between any kind of text
styles, including personal writing styles that are more fine-grained, share
less content and have much smaller training data. While state-of-art models
fail in the few-shot style transfer task, our framework effectively utilizes
information from other styles to improve both language fluency and style
transfer accuracy.
| 2,020 | Computation and Language |
Exploring Explainable Selection to Control Abstractive Summarization | Like humans, document summarization models can interpret a document's
contents in a number of ways. Unfortunately, the neural models of today are
largely black boxes that provide little explanation of how or why they
generated a summary in the way they did. Therefore, to begin prying open the
black box and to inject a level of control into the substance of the final
summary, we developed a novel select-and-generate framework that focuses on
explainability. By revealing the latent centrality and interactions between
sentences, along with scores for sentence novelty and relevance, users are
given a window into the choices a model is making and an opportunity to guide
those choices in a more desirable direction. A novel pair-wise matrix captures
the sentence interactions, centrality, and attribute scores, and a mask with
tunable attribute thresholds allows the user to control which sentences are
likely to be included in the extraction. A sentence-deployed attention
mechanism in the abstractor ensures the final summary emphasizes the desired
content. Additionally, the encoder is adaptable, supporting both Transformer-
and BERT-based configurations. In a series of experiments assessed with ROUGE
metrics and two human evaluations, ESCA outperformed eight state-of-the-art
models on the CNN/DailyMail and NYT50 benchmark datasets.
| 2,020 | Computation and Language |
FLAT: Chinese NER Using Flat-Lattice Transformer | Recently, the character-word lattice structure has been proved to be
effective for Chinese named entity recognition (NER) by incorporating the word
information. However, since the lattice structure is complex and dynamic, most
existing lattice-based models are hard to fully utilize the parallel
computation of GPUs and usually have a low inference-speed. In this paper, we
propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the
lattice structure into a flat structure consisting of spans. Each span
corresponds to a character or latent word and its position in the original
lattice. With the power of Transformer and well-designed position encoding,
FLAT can fully leverage the lattice information and has an excellent
parallelization ability. Experiments on four datasets show FLAT outperforms
other lexicon-based models in performance and efficiency.
| 2,020 | Computation and Language |
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models | Sequence-to-sequence models usually transfer all encoder outputs to the
decoder for generation. In this work, by contrast, we hypothesize that these
encoder outputs can be compressed to shorten the sequence delivered for
decoding. We take Transformer as the testbed and introduce a layer of
stochastic gates in-between the encoder and the decoder. The gates are
regularized using the expected value of the sparsity-inducing L0penalty,
resulting in completely masking-out a subset of encoder outputs. In other
words, via joint training, the L0DROP layer forces Transformer to route
information through a subset of its encoder states. We investigate the effects
of this sparsification on two machine translation and two summarization tasks.
Experiments show that, depending on the task, around 40-70% of source encodings
can be pruned without significantly compromising quality. The decrease of the
output length endows L0DROP with the potential of improving decoding
efficiency, where it yields a speedup of up to 1.65x on document summarization
tasks against the standard Transformer. We analyze the L0DROP behaviour and
observe that it exhibits systematic preferences for pruning certain word types,
e.g., function words and punctuation get pruned most. Inspired by these
observations, we explore the feasibility of specifying rule-based patterns that
mask out encoder outputs based on information such as part-of-speech tags, word
frequency and word position.
| 2,020 | Computation and Language |
Event-QA: A Dataset for Event-Centric Question Answering over Knowledge
Graphs | Semantic Question Answering (QA) is a crucial technology to facilitate
intuitive user access to semantic information stored in knowledge graphs.
Whereas most of the existing QA systems and datasets focus on entity-centric
questions, very little is known about these systems' performance in the context
of events. As new event-centric knowledge graphs emerge, datasets for such
questions gain importance. In this paper, we present the Event-QA dataset for
answering event-centric questions over knowledge graphs. Event-QA contains 1000
semantic queries and the corresponding English, German and Portuguese
verbalizations for EventKG - an event-centric knowledge graph with more than
970 thousand events.
| 2,020 | Computation and Language |
Improving Massively Multilingual Neural Machine Translation and
Zero-Shot Translation | Massively multilingual models for neural machine translation (NMT) are
theoretically attractive, but often underperform bilingual models and deliver
poor zero-shot translations. In this paper, we explore ways to improve them. We
argue that multilingual NMT requires stronger modeling capacity to support
language pairs with varying typological characteristics, and overcome this
bottleneck via language-specific components and deepening NMT architectures. We
identify the off-target translation issue (i.e. translating into a wrong target
language) as the major source of the inferior zero-shot performance, and
propose random online backtranslation to enforce the translation of unseen
training language pairs. Experiments on OPUS-100 (a novel multilingual dataset
with 100 languages) show that our approach substantially narrows the
performance gap with bilingual models in both one-to-many and many-to-many
settings, and improves zero-shot performance by ~10 BLEU, approaching
conventional pivot-based methods.
| 2,020 | Computation and Language |
Lite Transformer with Long-Short Range Attention | Transformer has become ubiquitous in natural language processing (e.g.,
machine translation, question answering); however, it requires enormous amount
of computations to achieve high performance, which makes it not suitable for
mobile applications that are tightly constrained by the hardware resources and
battery. In this paper, we present an efficient mobile NLP architecture, Lite
Transformer to facilitate deploying mobile NLP applications on edge devices.
The key primitive is the Long-Short Range Attention (LSRA), where one group of
heads specializes in the local context modeling (by convolution) while another
group specializes in the long-distance relationship modeling (by attention).
Such specialization brings consistent improvement over the vanilla transformer
on three well-established language tasks: machine translation, abstractive
summarization, and language modeling. Under constrained resources (500M/100M
MACs), Lite Transformer outperforms transformer on WMT'14 English-French by
1.2/1.7 BLEU, respectively. Lite Transformer reduces the computation of
transformer base model by 2.5x with 0.3 BLEU score degradation. Combining with
pruning and quantization, we further compressed the model size of Lite
Transformer by 18.2x. For language modeling, Lite Transformer achieves 1.8
lower perplexity than the transformer at around 500M MACs. Notably, Lite
Transformer outperforms the AutoML-based Evolved Transformer by 0.5 higher BLEU
for the mobile NLP setting without the costly architecture search that requires
more than 250 GPU years. Code has been made available at
https://github.com/mit-han-lab/lite-transformer.
| 2,020 | Computation and Language |
Template-Based Question Generation from Retrieved Sentences for Improved
Unsupervised Question Answering | Question Answering (QA) is in increasing demand as the amount of information
available online and the desire for quick access to this content grows. A
common approach to QA has been to fine-tune a pretrained language model on a
task-specific labeled dataset. This paradigm, however, relies on scarce, and
costly to obtain, large-scale human-labeled data. We propose an unsupervised
approach to training QA models with generated pseudo-training data. We show
that generating questions for QA training by applying a simple template on a
related, retrieved sentence rather than the original context sentence improves
downstream QA performance by allowing the model to learn more complex
context-question relationships. Training a QA model on this data gives a
relative improvement over a previous unsupervised model in F1 score on the
SQuAD dataset by about 14%, and 20% when the answer is a named entity,
achieving state-of-the-art performance on SQuAD for unsupervised QA.
| 2,020 | Computation and Language |
Practical Comparable Data Collection for Low-Resource Languages via
Images | We propose a method of curating high-quality comparable training data for
low-resource languages with monolingual annotators. Our method involves using a
carefully selected set of images as a pivot between the source and target
languages by getting captions for such images in both languages independently.
Human evaluations on the English-Hindi comparable corpora created with our
method show that 81.1% of the pairs are acceptable translations, and only 2.47%
of the pairs are not translations at all. We further establish the potential of
the dataset collected through our approach by experimenting on two downstream
tasks - machine translation and dictionary extraction. All code and data are
available at https://github.com/madaan/PML4DC-Comparable-Data-Collection.
| 2,020 | Computation and Language |
The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in
Arabic | This paper describes our method for the task of Semantic Question Similarity
in Arabic in the workshop on NLP Solutions for Under-Resourced Languages
(NSURL). The aim is to build a model that is able to detect similar semantic
questions in the Arabic language for the provided dataset. Different methods of
determining questions similarity are explored in this work. The proposed models
achieved high F1-scores, which range from (88% to 96%). Our official best
result is produced from the ensemble model of using a pre-trained multilingual
BERT model with different random seeds with 95.924% F1-Score, which ranks the
first among nine participants teams.
| 2,020 | Computation and Language |
New Protocols and Negative Results for Textual Entailment Data
Collection | Natural language inference (NLI) data has proven useful in benchmarking and,
especially, as pretraining data for tasks requiring language understanding.
However, the crowdsourcing protocol that was used to collect this data has
known issues and was not explicitly optimized for either of these purposes, so
it is likely far from ideal. We propose four alternative protocols, each aimed
at improving either the ease with which annotators can produce sound training
examples or the quality and diversity of those examples. Using these
alternatives and a fifth baseline protocol, we collect and compare five new
8.5k-example training sets. In evaluations focused on transfer learning
applications, our results are solidly negative, with models trained on our
baseline dataset yielding good transfer performance to downstream tasks, but
none of our four new methods (nor the recent ANLI) showing any improvements
over that baseline. In a small silver lining, we observe that all four new
protocols, especially those where annotators edit pre-filled text boxes, reduce
previously observed issues with annotation artifacts.
| 2,020 | Computation and Language |
Syntactic Data Augmentation Increases Robustness to Inference Heuristics | Pretrained neural models such as BERT, when fine-tuned to perform natural
language inference (NLI), often show high accuracy on standard datasets, but
display a surprising lack of sensitivity to word order on controlled challenge
sets. We hypothesize that this issue is not primarily caused by the pretrained
model's limitations, but rather by the paucity of crowdsourced NLI examples
that might convey the importance of syntactic structure at the fine-tuning
stage. We explore several methods to augment standard training sets with
syntactically informative examples, generated by applying syntactic
transformations to sentences from the MNLI corpus. The best-performing
augmentation method, subject/object inversion, improved BERT's accuracy on
controlled examples that diagnose sensitivity to word order from 0.28 to 0.73,
without affecting performance on the MNLI test set. This improvement
generalized beyond the particular construction used for data augmentation,
suggesting that augmentation causes BERT to recruit abstract syntactic
representations.
| 2,020 | Computation and Language |
Contextualized Representations Using Textual Encyclopedic Knowledge | We present a method to represent input texts by contextualizing them jointly
with dynamically retrieved textual encyclopedic background knowledge from
multiple documents. We apply our method to reading comprehension tasks by
encoding questions and passages together with background sentences about the
entities they mention. We show that integrating background knowledge from text
is effective for tasks focusing on factual reasoning and allows direct reuse of
powerful pretrained BERT-style encoders. Moreover, knowledge integration can be
further improved with suitable pretraining via a self-supervised masked
language model objective over words in background-augmented input text. On
TriviaQA, our approach obtains improvements of 1.6 to 3.1 F1 over comparable
RoBERTa models which do not integrate background knowledge dynamically. On
MRQA, a large collection of diverse QA datasets, we see consistent gains
in-domain along with large improvements out-of-domain on BioASQ (2.1 to 4.2
F1), TextbookQA (1.6 to 2.0 F1), and DuoRC (1.1 to 2.0 F1).
| 2,021 | Computation and Language |
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About
People? | Social biases are encoded in word embeddings. This presents a unique
opportunity to study society historically and at scale, and a unique danger
when embeddings are used in downstream applications. Here, we investigate the
extent to which publicly-available word embeddings accurately reflect beliefs
about certain kinds of people as measured via traditional survey methods. We
find that biases found in word embeddings do, on average, closely mirror survey
data across seventeen dimensions of social meaning. However, we also find that
biases in embeddings are much more reflective of survey data for some
dimensions of meaning (e.g. gender) than others (e.g. race), and that we can be
highly confident that embedding-based measures reflect survey data only for the
most salient biases.
| 2,020 | Computation and Language |
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for
Question Answering Over Dynamic Contexts | We study question answering over a dynamic textual environment. Although
neural network models achieve impressive accuracy via learning from
input-output examples, they rarely leverage various types of knowledge and are
generally not interpretable. In this work, we propose a graph-based approach,
where a heterogeneous graph is automatically built with factual knowledge of
the context, temporal knowledge of the past states, and logical knowledge that
combines human-curated knowledge bases and rule bases. We develop a graph
neural network over the constructed graph, and train the model in an end-to-end
manner. Experimental results on a benchmark dataset show that the injection of
various types of knowledge improves a strong neural network baseline. An
additional benefit of our approach is that the graph itself naturally serves as
a rational behind the decision making.
| 2,020 | Computation and Language |
All Word Embeddings from One Embedding | In neural network-based models for natural language processing (NLP), the
largest part of the parameters often consists of word embeddings. Conventional
models prepare a large embedding matrix whose size depends on the vocabulary
size. Therefore, storing these models in memory and disk storage is costly. In
this study, to reduce the total number of parameters, the embeddings for all
words are represented by transforming a shared embedding. The proposed method,
ALONE (all word embeddings from one), constructs the embedding of a word by
modifying the shared embedding with a filter vector, which is word-specific but
non-trainable. Then, we input the constructed embedding into a feed-forward
neural network to increase its expressiveness. Naively, the filter vectors
occupy the same memory size as the conventional embedding matrix, which depends
on the vocabulary size. To solve this issue, we also introduce a
memory-efficient filter construction approach. We indicate our ALONE can be
used as word representation sufficiently through an experiment on the
reconstruction of pre-trained word embeddings. In addition, we also conduct
experiments on NLP application tasks: machine translation and summarization. We
combined ALONE with the current state-of-the-art encoder-decoder model, the
Transformer, and achieved comparable scores on WMT 2014 English-to-German
translation and DUC 2004 very short summarization with less parameters.
| 2,020 | Computation and Language |
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained
Model Lead to the Promised Land? | Fine-tuning pretrained model has achieved promising performance on standard
NER benchmarks. Generally, these benchmarks are blessed with strong name
regularity, high mention coverage and sufficient context diversity.
Unfortunately, when scaling NER to open situations, these advantages may no
longer exist. And therefore it raises a critical question of whether previous
creditable approaches can still work well when facing these challenges. As
there is no currently available dataset to investigate this problem, this paper
proposes to conduct randomization test on standard benchmarks. Specifically, we
erase name regularity, mention coverage and context diversity respectively from
the benchmarks, in order to explore their impact on the generalization ability
of models. To further verify our conclusions, we also construct a new open NER
dataset that focuses on entity types with weaker name regularity and lower
mention coverage to verify our conclusion. From both randomization test and
empirical experiments, we draw the conclusions that 1) name regularity is
critical for the models to generalize to unseen mentions; 2) high mention
coverage may undermine the model generalization ability and 3) context patterns
may not require enormous data to capture when using pretrained encoders.
| 2,020 | Computation and Language |
How Does NLP Benefit Legal System: A Summary of Legal Artificial
Intelligence | Legal Artificial Intelligence (LegalAI) focuses on applying the technology of
artificial intelligence, especially natural language processing, to benefit
tasks in the legal domain. In recent years, LegalAI has drawn increasing
attention rapidly from both AI researchers and legal professionals, as LegalAI
is beneficial to the legal system for liberating legal professionals from a
maze of paperwork. Legal professionals often think about how to solve tasks
from rule-based and symbol-based methods, while NLP researchers concentrate
more on data-driven and embedding methods. In this paper, we introduce the
history, the current state, and the future directions of research in LegalAI.
We illustrate the tasks from the perspectives of legal professionals and NLP
researchers and show several representative applications in LegalAI. We conduct
experiments and provide an in-depth analysis of the advantages and
disadvantages of existing works to explore possible future directions. You can
find the implementation of our work from https://github.com/thunlp/CLAIM.
| 2,020 | Computation and Language |
Learning to Update Natural Language Comments Based on Code Changes | We formulate the novel task of automatically updating an existing natural
language comment based on changes in the body of code it accompanies. We
propose an approach that learns to correlate changes across two distinct
language representations, to generate a sequence of edits that are applied to
the existing comment to reflect the source code modifications. We train and
evaluate our model using a dataset that we collected from commit histories of
open-source software projects, with each example consisting of a concurrent
update to a method and its corresponding comment. We compare our approach
against multiple baselines using both automatic metrics and human evaluation.
Results reflect the challenge of this task and that our model outperforms
baselines with respect to making edits.
| 2,020 | Computation and Language |
A Named Entity Based Approach to Model Recipes | Traditional cooking recipes follow a structure which can be modelled very
well if the rules and semantics of the different sections of the recipe text
are analyzed and represented accurately. We propose a structure that can
accurately represent the recipe as well as a pipeline to infer the best
representation of the recipe in this uniform structure. The Ingredients section
in a recipe typically lists down the ingredients required and corresponding
attributes such as quantity, temperature, and processing state. This can be
modelled by defining these attributes and their values. The physical entities
which make up a recipe can be broadly classified into utensils, ingredients and
their combinations that are related by cooking techniques. The instruction
section lists down a series of events in which a cooking technique or process
is applied upon these utensils and ingredients. We model these relationships in
the form of tuples. Thus, using a combination of these methods we model cooking
recipe in the dataset RecipeDB to show the efficacy of our method. This mined
information model can have several applications which include translating
recipes between languages, determining similarity between recipes, generation
of novel recipes and estimation of the nutritional profile of recipes. For the
purpose of recognition of ingredient attributes, we train the Named Entity
Relationship (NER) models and analyze the inferences with the help of K-Means
clustering. Our model presented with an F1 score of 0.95 across all datasets.
We use a similar NER tagging model for labelling cooking techniques (F1 score =
0.88) and utensils (F1 score = 0.90) within the instructions section. Finally,
we determine the temporal sequence of relationships between ingredients,
utensils and cooking techniques for modeling the instruction steps.
| 2,020 | Computation and Language |
Towards Discourse Parsing-inspired Semantic Storytelling | Previous work of ours on Semantic Storytelling uses text analytics procedures
including Named Entity Recognition and Event Detection. In this paper, we
outline our longer-term vision on Semantic Storytelling and describe the
current conceptual and technical approach. In the project that drives our
research we develop AI-based technologies that are verified by partners from
industry. One long-term goal is the development of an approach for Semantic
Storytelling that has broad coverage and that is, furthermore, robust. We
provide first results on experiments that involve discourse parsing, applied to
a concrete use case, "Explore the Neighbourhood!", which is based on a
semi-automatically collected data set with documents about noteworthy people in
one of Berlin's districts. Though automatically obtaining annotations for
coherence relations from plain text is a non-trivial challenge, our preliminary
results are promising. We envision our approach to be combined with additional
features (NER, coreference resolution, knowledge graphs
| 2,020 | Computation and Language |
Quantifying the Contextualization of Word Representations with Semantic
Class Probing | Pretrained language models have achieved a new state of the art on many NLP
tasks, but there are still many open questions about how and why they work so
well. We investigate the contextualization of words in BERT. We quantify the
amount of contextualization, i.e., how well words are interpreted in context,
by studying the extent to which semantic classes of a word can be inferred from
its contextualized embeddings. Quantifying contextualization helps in
understanding and utilizing pretrained language models. We show that top layer
representations achieve high accuracy inferring semantic classes; that the
strongest contextualization effects occur in the lower layers; that local
context is mostly sufficient for semantic class inference; and that top layer
representations are more task-specific after finetuning while lower layer
representations are more transferable. Finetuning uncovers task related
features, but pretrained knowledge is still largely preserved.
| 2,020 | Computation and Language |
MCQA: Multimodal Co-attention Based Network for Question Answering | We present MCQA, a learning-based algorithm for multimodal question
answering. MCQA explicitly fuses and aligns the multimodal input (i.e. text,
audio, and video), which forms the context for the query (question and answer).
Our approach fuses and aligns the question and the answer within this context.
Moreover, we use the notion of co-attention to perform cross-modal alignment
and multimodal context-query alignment. Our context-query alignment module
matches the relevant parts of the multimodal context and the query with each
other and aligns them to improve the overall performance. We evaluate the
performance of MCQA on Social-IQ, a benchmark dataset for multimodal question
answering. We compare the performance of our algorithm with prior methods and
observe an accuracy improvement of 4-7%.
| 2,020 | Computation and Language |
MixText: Linguistically-Informed Interpolation of Hidden Space for
Semi-Supervised Text Classification | This paper presents MixText, a semi-supervised learning method for text
classification, which uses our newly designed data augmentation method called
TMix. TMix creates a large amount of augmented training samples by
interpolating text in hidden space. Moreover, we leverage recent advances in
data augmentation to guess low-entropy labels for unlabeled data, hence making
them as easy to use as labeled data.By mixing labeled, unlabeled and augmented
data, MixText significantly outperformed current pre-trained and fined-tuned
models and other state-of-the-art semi-supervised learning methods on several
text classification benchmarks. The improvement is especially prominent when
supervision is extremely limited. We have publicly released our code at
https://github.com/GT-SALT/MixText.
| 2,020 | Computation and Language |
Hierarchical Multi Task Learning with Subword Contextual Embeddings for
Languages with Rich Morphology | Morphological information is important for many sequence labeling tasks in
Natural Language Processing (NLP). Yet, existing approaches rely heavily on
manual annotations or external software to capture this information. In this
study, we propose using subword contextual embeddings to capture the
morphological information for languages with rich morphology. In addition, we
incorporate these embeddings in a hierarchical multi-task setting which is not
employed before, to the best of our knowledge. Evaluated on Dependency Parsing
(DEP) and Named Entity Recognition (NER) tasks, which are shown to benefit
greatly from morphological information, our final model outperforms previous
state-of-the-art models on both tasks for the Turkish language. Besides, we
show a net improvement of 18.86% and 4.61% F-1 over the previously proposed
multi-task learner in the same setting for the DEP and the NER tasks,
respectively. Empirical results for five different MTL settings show that
incorporating subword contextual embeddings brings significant improvements for
both tasks. In addition, we observed that multi-task learning consistently
improves the performance of the DEP component.
| 2,020 | Computation and Language |
Causal Mediation Analysis for Interpreting Neural NLP: The Case of
Gender Bias | Common methods for interpreting neural models in natural language processing
typically examine either their structure or their behavior, but not both. We
propose a methodology grounded in the theory of causal mediation analysis for
interpreting which parts of a model are causally implicated in its behavior. It
enables us to analyze the mechanisms by which information flows from input to
output through various model components, known as mediators. We apply this
methodology to analyze gender bias in pre-trained Transformer language models.
We study the role of individual neurons and attention heads in mediating gender
bias across three datasets designed to gauge a model's sensitivity to gender
bias. Our mediation analysis reveals that gender bias effects are (i) sparse,
concentrated in a small part of the network; (ii) synergistic, amplified or
repressed by different components; and (iii) decomposable into effects flowing
directly from the input and indirectly through the mediators.
| 2,020 | Computation and Language |
Show, Describe and Conclude: On Exploiting the Structure Information of
Chest X-Ray Reports | Chest X-Ray (CXR) images are commonly used for clinical screening and
diagnosis. Automatically writing reports for these images can considerably
lighten the workload of radiologists for summarizing descriptive findings and
conclusive impressions. The complex structures between and within sections of
the reports pose a great challenge to the automatic report generation.
Specifically, the section Impression is a diagnostic summarization over the
section Findings; and the appearance of normality dominates each section over
that of abnormality. Existing studies rarely explore and consider this
fundamental structure information. In this work, we propose a novel framework
that exploits the structure information between and within report sections for
generating CXR imaging reports. First, we propose a two-stage strategy that
explicitly models the relationship between Findings and Impression. Second, we
design a novel cooperative multi-agent system that implicitly captures the
imbalanced distribution between abnormality and normality. Experiments on two
CXR report datasets show that our method achieves state-of-the-art performance
in terms of various evaluation metrics. Our results expose that the proposed
approach is able to generate high-quality medical reports through integrating
the structure information.
| 2,020 | Computation and Language |
Dual Learning for Semi-Supervised Natural Language Understanding | Natural language understanding (NLU) converts sentences into structured
semantic forms. The paucity of annotated training samples is still a
fundamental challenge of NLU. To solve this data sparsity problem, previous
work based on semi-supervised learning mainly focuses on exploiting unlabeled
sentences. In this work, we introduce a dual task of NLU, semantic-to-sentence
generation (SSG), and propose a new framework for semi-supervised NLU with the
corresponding dual model. The framework is composed of dual pseudo-labeling and
dual learning method, which enables an NLU model to make full use of data
(labeled and unlabeled) through a closed-loop of the primal and dual tasks. By
incorporating the dual task, the framework can exploit pure semantic forms as
well as unlabeled sentences, and further improve the NLU and SSG models
iteratively in the closed-loop. The proposed approaches are evaluated on two
public datasets (ATIS and SNIPS). Experiments in the semi-supervised setting
show that our methods can outperform various baselines significantly, and
extensive ablation studies are conducted to verify the effectiveness of our
framework. Finally, our method can also achieve the state-of-the-art
performance on the two datasets in the supervised setting. Our code is
available at \url{https://github.com/rhythmcao/slu-dual-learning.git}.
| 2,021 | Computation and Language |
MATINF: A Jointly Labeled Large-Scale Dataset for Classification,
Question Answering and Summarization | Recently, large-scale datasets have vastly facilitated the development in
nearly all domains of Natural Language Processing. However, there is currently
no cross-task dataset in NLP, which hinders the development of multi-task
learning. We propose MATINF, the first jointly labeled large-scale dataset for
classification, question answering and summarization. MATINF contains 1.07
million question-answer pairs with human-labeled categories and user-generated
question descriptions. Based on such rich information, MATINF is applicable for
three major NLP tasks, including classification, question answering, and
summarization. We benchmark existing methods and a novel multi-task baseline
over MATINF to inspire further research. Our comprehensive comparison and
experiments over MATINF and other datasets demonstrate the merits held by
MATINF.
| 2,020 | Computation and Language |
Towards Persona-Based Empathetic Conversational Models | Empathetic conversational models have been shown to improve user satisfaction
and task outcomes in numerous domains. In Psychology, persona has been shown to
be highly correlated to personality, which in turn influences empathy. In
addition, our empirical analysis also suggests that persona plays an important
role in empathetic conversations. To this end, we propose a new task towards
persona-based empathetic conversations and present the first empirical study on
the impact of persona on empathetic responding. Specifically, we first present
a novel large-scale multi-domain dataset for persona-based empathetic
conversations. We then propose CoBERT, an efficient BERT-based response
selection model that obtains the state-of-the-art performance on our dataset.
Finally, we conduct extensive experiments to investigate the impact of persona
on empathetic responding. Notably, our results show that persona improves
empathetic responding more when CoBERT is trained on empathetic conversations
than non-empathetic ones, establishing an empirical link between persona and
empathy in human conversations.
| 2,020 | Computation and Language |
Neural Topic Modeling with Bidirectional Adversarial Training | Recent years have witnessed a surge of interests of using neural topic models
for automatic topic extraction from text, since they avoid the complicated
mathematical derivations for model inference as in traditional topic models
such as Latent Dirichlet Allocation (LDA). However, these models either
typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent
topic space or could not infer topic distribution for a given document. To
address these limitations, we propose a neural topic modeling approach, called
Bidirectional Adversarial Topic (BAT) model, which represents the first attempt
of applying bidirectional adversarial training for neural topic modeling. The
proposed BAT builds a two-way projection between the document-topic
distribution and the document-word distribution. It uses a generator to capture
the semantic patterns from texts and an encoder for topic inference.
Furthermore, to incorporate word relatedness information, the Bidirectional
Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To
verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are
used in our experiments. The experimental results show that BAT and
Gaussian-BAT obtain more coherent topics, outperforming several competitive
baselines. Moreover, when performing text clustering based on the extracted
topics, our models outperform all the baselines, with more significant
improvements achieved by Gaussian-BAT where an increase of near 6\% is observed
in accuracy.
| 2,020 | Computation and Language |
Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty
with Bernstein Bounds | Most NLP datasets are not annotated with protected attributes such as gender,
making it difficult to measure classification bias using standard measures of
fairness (e.g., equal opportunity). However, manually annotating a large
dataset with a protected attribute is slow and expensive. Instead of annotating
all the examples, can we annotate a subset of them and use that sample to
estimate the bias? While it is possible to do so, the smaller this annotated
sample is, the less certain we are that the estimate is close to the true bias.
In this work, we propose using Bernstein bounds to represent this uncertainty
about the bias estimate as a confidence interval. We provide empirical evidence
that a 95% confidence interval derived this way consistently bounds the true
bias. In quantifying this uncertainty, our method, which we call
Bernstein-bounded unfairness, helps prevent classifiers from being deemed
biased or unbiased when there is insufficient evidence to make either claim.
Our findings suggest that the datasets currently used to measure specific
biases are too small to conclusively identify bias except in the most egregious
cases. For example, consider a co-reference resolution system that is 5% more
accurate on gender-stereotypical sentences -- to claim it is biased with 95%
confidence, we need a bias-specific dataset that is 3.8 times larger than
WinoBias, the largest available.
| 2,020 | Computation and Language |
Relational Graph Attention Network for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis aims to determine the sentiment polarity
towards a specific aspect in online reviews. Most recent efforts adopt
attention-based neural network models to implicitly connect aspects with
opinion words. However, due to the complexity of language and the existence of
multiple aspects in a single sentence, these models often confuse the
connections. In this paper, we address this problem by means of effective
encoding of syntax information. Firstly, we define a unified aspect-oriented
dependency tree structure rooted at a target aspect by reshaping and pruning an
ordinary dependency parse tree. Then, we propose a relational graph attention
network (R-GAT) to encode the new tree structure for sentiment prediction.
Extensive experiments are conducted on the SemEval 2014 and Twitter datasets,
and the experimental results confirm that the connections between aspects and
opinion words can be better established with our approach, and the performance
of the graph attention network (GAT) is significantly improved as a
consequence.
| 2,020 | Computation and Language |
Multi-Domain Dialogue Acts and Response Co-Generation | Generating fluent and informative responses is of critical importance for
task-oriented dialogue systems. Existing pipeline approaches generally predict
multiple dialogue acts first and use them to assist response generation. There
are at least two shortcomings with such approaches. First, the inherent
structures of multi-domain dialogue acts are neglected. Second, the semantic
associations between acts and responses are not taken into account for response
generation. To address these issues, we propose a neural co-generation model
that generates dialogue acts and responses concurrently. Unlike those pipeline
approaches, our act generation module preserves the semantic structures of
multi-domain dialogue acts and our response generation module dynamically
attends to different acts as needed. We train the two modules jointly using an
uncertainty loss to adjust their task weights adaptively. Extensive experiments
are conducted on the large-scale MultiWOZ dataset and the results show that our
model achieves very favorable improvement over several state-of-the-art models
in both automatic and human evaluations.
| 2,020 | Computation and Language |
GLUECoS : An Evaluation Benchmark for Code-Switched NLP | Code-switching is the use of more than one language in the same conversation
or utterance. Recently, multilingual contextual embedding models, trained on
multiple monolingual corpora, have shown promising results on cross-lingual and
multilingual tasks. We present an evaluation benchmark, GLUECoS, for
code-switched languages, that spans several NLP tasks in English-Hindi and
English-Spanish. Specifically, our evaluation benchmark includes Language
Identification from text, POS tagging, Named Entity Recognition, Sentiment
Analysis, Question Answering and a new task for code-switching, Natural
Language Inference. We present results on all these tasks using cross-lingual
word embedding models and multilingual models. In addition, we fine-tune
multilingual models on artificially generated code-switched data. Although
multilingual models perform significantly better than cross-lingual models, our
results show that in most tasks, across both language pairs, multilingual
models fine-tuned on code-switched data perform best, showing that multilingual
models can be further optimized for code-switching tasks.
| 2,020 | Computation and Language |
Heterogeneous Graph Neural Networks for Extractive Document
Summarization | As a crucial step in extractive document summarization, learning
cross-sentence relations has been explored by a plethora of approaches. An
intuitive way is to put them in the graph-based neural network, which has a
more complex structure for capturing inter-sentence relationships. In this
paper, we present a heterogeneous graph-based neural network for extractive
summarization (HeterSumGraph), which contains semantic nodes of different
granularity levels apart from sentences. These additional nodes act as the
intermediary between sentences and enrich the cross-sentence relations.
Besides, our graph structure is flexible in natural extension from a
single-document setting to multi-document via introducing document nodes. To
our knowledge, we are the first one to introduce different types of nodes into
graph-based neural networks for extractive document summarization and perform a
comprehensive qualitative analysis to investigate their benefits. The code will
be released on Github
| 2,020 | Computation and Language |
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