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Induction and Reference of Entities in a Visual Story | We are enveloped by stories of visual interpretations in our everyday lives.
The way we narrate a story often comprises of two stages, which are, forming a
central mind map of entities and then weaving a story around them. A
contributing factor to coherence is not just basing the story on these entities
but also, referring to them using appropriate terms to avoid repetition. In
this paper, we address these two stages of introducing the right entities at
seemingly reasonable junctures and also referring them coherently in the
context of visual storytelling. The building blocks of the central mind map,
also known as entity skeleton are entity chains including nominal and
coreference expressions. This entity skeleton is also represented in different
levels of abstractions to compose a generalized frame to weave the story. We
build upon an encoder-decoder framework to penalize the model when the decoded
story does not adhere to this entity skeleton. We establish a strong baseline
for skeleton informed generation and then extend this to have the capability of
multitasking by predicting the skeleton in addition to generating the story.
Finally, we build upon this model and propose a glocal hierarchical attention
model that attends to the skeleton both at the sentence (local) and the story
(global) levels. We observe that our proposed models outperform the baseline in
terms of automatic evaluation metric, METEOR. We perform various analysis
targeted to evaluate the performance of our task of enforcing the entity
skeleton such as the number and diversity of the entities generated. We also
conduct human evaluation from which it is concluded that the visual stories
generated by our model are preferred 82% of the times. In addition, we show
that our glocal hierarchical attention model improves coherence by introducing
more pronouns as required by the presence of nouns.
| 2,019 | Computation and Language |
Retrofitting Contextualized Word Embeddings with Paraphrases | Contextualized word embedding models, such as ELMo, generate meaningful
representations of words and their context. These models have been shown to
have a great impact on downstream applications. However, in many cases, the
contextualized embedding of a word changes drastically when the context is
paraphrased. As a result, the downstream model is not robust to paraphrasing
and other linguistic variations. To enhance the stability of contextualized
word embedding models, we propose an approach to retrofitting contextualized
embedding models with paraphrase contexts. Our method learns an orthogonal
transformation on the input space, which seeks to minimize the variance of word
representations on paraphrased contexts. Experiments show that the retrofitted
model significantly outperforms the original ELMo on various sentence
classification and language inference tasks.
| 2,019 | Computation and Language |
Using Clinical Notes with Time Series Data for ICU Management | Monitoring patients in ICU is a challenging and high-cost task. Hence,
predicting the condition of patients during their ICU stay can help provide
better acute care and plan the hospital's resources. There has been continuous
progress in machine learning research for ICU management, and most of this work
has focused on using time series signals recorded by ICU instruments. In our
work, we show that adding clinical notes as another modality improves the
performance of the model for three benchmark tasks: in-hospital mortality
prediction, modeling decompensation, and length of stay forecasting that play
an important role in ICU management. While the time-series data is measured at
regular intervals, doctor notes are charted at irregular times, making it
challenging to model them together. We propose a method to model them jointly,
achieving considerable improvement across benchmark tasks over baseline
time-series model. Our implementation can be found at
\url{https://github.com/kaggarwal/ClinicalNotesICU}.
| 2,020 | Computation and Language |
Measuring Domain Portability and ErrorPropagation in Biomedical QA | In this work we present Google's submission to the BioASQ 7 biomedical
question answering (QA) task (specifically Task 7b, Phase B). The core of our
systems are based on BERT QA models, specifically the model of
\cite{alberti2019bert}. In this report, and via our submissions, we aimed to
investigate two research questions. We start by studying how domain portable
are QA systems that have been pre-trained and fine-tuned on general texts,
e.g., Wikipedia. We measure this via two submissions. The first is a
non-adapted model that uses a public pre-trained BERT model and is fine-tuned
on the Natural Questions data set \cite{kwiatkowski2019natural}. The second
system takes this non-adapted model and fine-tunes it with the BioASQ training
data. Next, we study the impact of error propagation in end-to-end retrieval
and QA systems. Again we test this via two submissions. The first uses human
annotated relevant documents and snippets as input to the model and the second
predicted documents and snippets. Our main findings are that domain specific
fine-tuning can benefit Biomedical QA. However, the biggest quality bottleneck
is at the retrieval stage, where we see large drops in metrics -- over 10pts
absolute -- when using non gold inputs to the QA model.
| 2,019 | Computation and Language |
Measuring Conceptual Entanglement in Collections of Documents | Conceptual entanglement is a crucial phenomenon in quantum cognition because
it implies that classical probabilities cannot model non--compositional
conceptual phenomena. While several psychological experiments have been
developed to test conceptual entanglement, this has not been explored in the
context of Natural Language Processing. In this paper, we apply the hypothesis
that words of a document are traces of the concepts that a person has in mind
when writing the document. Therefore, if these concepts are entangled, we
should be able to observe traces of their entanglement in the documents. In
particular, we test conceptual entanglement by contrasting language simulations
with results obtained from a text corpus. Our analysis indicates that
conceptual entanglement is strongly linked to the way in which language is
structured. We discuss the implications of this finding in the context of
conceptual modeling and of Natural Language Processing.
| 2,013 | Computation and Language |
Self-attention based end-to-end Hindi-English Neural Machine Translation | Machine Translation (MT) is a zone of concentrate in Natural Language
processing which manages the programmed interpretation of human language,
starting with one language then onto the next by the PC. Having a rich research
history spreading over about three decades, Machine interpretation is a
standout amongst the most looked for after region of research in the
computational linguistics network. As a piece of this current ace's proposal,
the fundamental center examines the Deep-learning based strategies that have
gained critical ground as of late and turning into the de facto strategy in MT.
We would like to point out the recent advances that have been put forward in
the field of Neural Translation models, different domains under which NMT has
replaced conventional SMT models and would also like to mention future avenues
in the field. Consequently, we propose an end-to-end self-attention transformer
network for Neural Machine Translation, trained on Hindi-English parallel
corpus and compare the model's efficiency with other state of art models like
encoder-decoder and attention-based encoder-decoder neural models on the basis
of BLEU. We conclude this paper with a comparative analysis of the three
proposed models.
| 2,019 | Computation and Language |
Visuallly Grounded Generation of Entailments from Premises | Natural Language Inference (NLI) is the task of determining the semantic
relationship between a premise and a hypothesis. In this paper, we focus on the
{\em generation} of hypotheses from premises in a multimodal setting, to
generate a sentence (hypothesis) given an image and/or its description
(premise) as the input. The main goals of this paper are (a) to investigate
whether it is reasonable to frame NLI as a generation task; and (b) to consider
the degree to which grounding textual premises in visual information is
beneficial to generation. We compare different neural architectures, showing
through automatic and human evaluation that entailments can indeed be generated
successfully. We also show that multimodal models outperform unimodal models in
this task, albeit marginally.
| 2,019 | Computation and Language |
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role
Labeling | Semantic role labeling (SRL) is the task of identifying predicates and
labeling argument spans with semantic roles. Even though most semantic-role
formalisms are built upon constituent syntax and only syntactic constituents
can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work
on syntax-aware SRL relies on dependency representations of syntax. In
contrast, we show how graph convolutional networks (GCNs) can be used to encode
constituent structures and inform an SRL system. Nodes in our SpanGCN
correspond to constituents. The computation is done in 3 stages. First, initial
node representations are produced by `composing' word representations of the
first and the last word in the constituent. Second, graph convolutions relying
on the constituent tree are performed, yielding syntactically-informed
constituent representations. Finally, the constituent representations are
`decomposed' back into word representations which in turn are used as input to
the SRL classifier. We evaluate SpanGCN against alternatives, including a model
using GCNs over dependency trees, and show its effectiveness on standard
CoNLL-2005, CoNLL-2012, and FrameNet benchmarks.
| 2,020 | Computation and Language |
Low-Rank Approximation of Matrices for PMI-based Word Embeddings | We perform an empirical evaluation of several methods of low-rank
approximation in the problem of obtaining PMI-based word embeddings. All word
vectors were trained on parts of a large corpus extracted from English
Wikipedia (enwik9) which was divided into two equal-sized datasets, from which
PMI matrices were obtained. A repeated measures design was used in assigning a
method of low-rank approximation (SVD, NMF, QR) and dimensionality of the
vectors (250, 500) to each of the PMI matrix replicates. Our experiments show
that word vectors obtained from the truncated SVD achieve the best performance
on two downstream tasks, similarity and analogy, compare to the other two
low-rank approximation methods.
| 2,019 | Computation and Language |
Generating Timelines by Modeling Semantic Change | Though languages can evolve slowly, they can also react strongly to dramatic
world events. By studying the connection between words and events, it is
possible to identify which events change our vocabulary and in what way. In
this work, we tackle the task of creating timelines - records of historical
"turning points", represented by either words or events, to understand the
dynamics of a target word. Our approach identifies these points by leveraging
both static and time-varying word embeddings to measure the influence of words
and events. In addition to quantifying changes, we show how our technique can
help isolate semantic changes. Our qualitative and quantitative evaluations
show that we are able to capture this semantic change and event influence.
| 2,019 | Computation and Language |
Using Chinese Glyphs for Named Entity Recognition | Most Named Entity Recognition (NER) systems use additional features like
part-of-speech (POS) tags, shallow parsing, gazetteers, etc. Such kind of
information requires external knowledge like unlabeled texts and trained
taggers. Adding these features to NER systems have been shown to have a
positive impact. However, sometimes creating gazetteers or taggers can take a
lot of time and may require extensive data cleaning. In this paper for Chinese
NER systems, we do not use these traditional features but we use lexicographic
features of Chinese characters. Chinese characters are composed of graphical
components called radicals and these components often have some semantic
indicators. We propose CNN based models that incorporate this semantic
information and use them for NER. Our models show an improvement over the
baseline BERT-BiLSTM-CRF model. We set a new baseline score for Chinese
OntoNotes v5.0 and show an improvement of +.64 F1 score. We present a
state-of-the-art F1 score on Weibo dataset of 71.81 and show a competitive
improvement of +0.72 over baseline on ResumeNER dataset.
| 2,020 | Computation and Language |
Adapting Language Models for Non-Parallel Author-Stylized Rewriting | Given the recent progress in language modeling using Transformer-based neural
models and an active interest in generating stylized text, we present an
approach to leverage the generalization capabilities of a language model to
rewrite an input text in a target author's style. Our proposed approach adapts
a pre-trained language model to generate author-stylized text by fine-tuning on
the author-specific corpus using a denoising autoencoder (DAE) loss in a
cascaded encoder-decoder framework. Optimizing over DAE loss allows our model
to learn the nuances of an author's style without relying on parallel data,
which has been a severe limitation of the previous related works in this space.
To evaluate the efficacy of our approach, we propose a linguistically-motivated
framework to quantify stylistic alignment of the generated text to the target
author at lexical, syntactic and surface levels. The evaluation framework is
both interpretable as it leads to several insights about the model, and
self-contained as it does not rely on external classifiers, e.g. sentiment or
formality classifiers. Qualitative and quantitative assessment indicates that
the proposed approach rewrites the input text with better alignment to the
target style while preserving the original content better than state-of-the-art
baselines.
| 2,020 | Computation and Language |
Improving Quality and Efficiency in Plan-based Neural Data-to-Text
Generation | We follow the step-by-step approach to neural data-to-text generation we
proposed in Moryossef et al (2019), in which the generation process is divided
into a text-planning stage followed by a plan-realization stage. We suggest
four extensions to that framework: (1) we introduce a trainable neural planning
component that can generate effective plans several orders of magnitude faster
than the original planner; (2) we incorporate typing hints that improve the
model's ability to deal with unseen relations and entities; (3) we introduce a
verification-by-reranking stage that substantially improves the faithfulness of
the resulting texts; (4) we incorporate a simple but effective referring
expression generation module. These extensions result in a generation process
that is faster, more fluent, and more accurate.
| 2,019 | Computation and Language |
Improving OOV Detection and Resolution with External Language Models in
Acoustic-to-Word ASR | Acoustic-to-word (A2W) end-to-end automatic speech recognition (ASR) systems
have attracted attention because of an extremely simplified architecture and
fast decoding. To alleviate data sparseness issues due to infrequent words, the
combination with an acoustic-to-character (A2C) model is investigated.
Moreover, the A2C model can be used to recover out-of-vocabulary (OOV) words
that are not covered by the A2W model, but this requires accurate detection of
OOV words. A2W models learn contexts with both acoustic and transcripts;
therefore they tend to falsely recognize OOV words as words in the vocabulary.
In this paper, we tackle this problem by using external language models (LM),
which are trained only with transcriptions and have better linguistic
information to detect OOV words. The A2C model is used to resolve these OOV
words. Experimental evaluations show that external LMs have the effects of not
only reducing errors but also increasing the number of detected OOV words, and
the proposed method significantly improves performances in English
conversational and Japanese lecture corpora, especially for out-of-domain
scenario. We also investigate the impact of the vocabulary size of A2W models
and the data size for training LMs. Moreover, our approach can reduce the
vocabulary size several times with marginal performance degradation.
| 2,019 | Computation and Language |
Is change the only constant? Profile change perspective on
#LokSabhaElections2019 | Users on Twitter are identified with the help of their profile attributes
that consists of username, display name, profile image, to name a few. The
profile attributes that users adopt can reflect their interests, belief, or
thematic inclinations. Literature has proposed the implications and
significance of profile attribute change for a random population of users.
However, the use of profile attribute for endorsements and to start a movement
have been under-explored. In this work, we consider #LokSabhaElections2019 as a
movement and perform a large-scale study of the profile of users who actively
made changes to profile attributes centered around #LokSabhaElections2019. We
collect the profile metadata for 49.4M users for a period of 2 months from
April 5, 2019 to June 5, 2019 amid #LokSabhaElections2019. We investigate how
the profile changes vary for the influential leaders and their followers over
the social movement. We further differentiate the organic and inorganic ways to
show the political inclination from the prism of profile changes. We report how
the addition of election campaign related keywords lead to spread of behavior
contagion and further investigate it with respect to "Chowkidar Movement" in
detail.
| 2,019 | Computation and Language |
Inducing Constituency Trees through Neural Machine Translation | Latent tree learning(LTL) methods learn to parse sentences using only
indirect supervision from a downstream task. Recent advances in latent tree
learning have made it possible to recover moderately high quality tree
structures by training with language modeling or auto-encoding objectives. In
this work, we explore the hypothesis that decoding in machine translation, as a
conditional language modeling task, will produce better tree structures since
it offers a similar training signal as language modeling, but with more
semantic signal. We adapt two existing latent-tree language models--PRPN
andON-LSTM--for use in translation. We find that they indeed recover trees that
are better in F1 score than those seen in language modeling on WSJ test set,
while maintaining strong translation quality. We observe that translation is a
better objective than language modeling for inducing trees, marking the first
success at latent tree learning using a machine translation objective.
Additionally, our findings suggest that, although translation provides better
signal for inducing trees than language modeling, translation models can
perform well without exploiting the latent tree structure.
| 2,019 | Computation and Language |
Algorithms for certain classes of Tamil Spelling correction | Tamil language has an agglutinative, diglossic, alpha-syllabary structure
which provides a significant combinatorial explosion of morphological forms all
of which are effectively used in Tamil prose, poetry from antiquity to the
modern age in an unbroken chain of continuity. However, for the language
understanding, spelling correction purposes some of these present challenges as
out-of-dictionary words. In this paper the authors propose algorithmic
techniques to handle specific problems of conjoined-words (out-of-dictionary)
(transliteration)[thendRalkattRu] = [thendRal]+[kattRu] when parts are alone
present in word-list in efficient way. Morphological structure of Tamil makes
it necessary to depend on synthesis-analysis approach and dictionary lists will
never be sufficient to truly capture the language. In this paper we have
attempted to make a summary of various known algorithms for specific classes of
Tamil spelling errors. We believe this collection of suggestions to improve
future spelling checkers. We also note do not cover many important techniques
like affix removal and other such techniques of key importance in rule-based
spell checkers.
| 2,019 | Computation and Language |
Deep Structured Neural Network for Event Temporal Relation Extraction | We propose a novel deep structured learning framework for event temporal
relation extraction. The model consists of 1) a recurrent neural network (RNN)
to learn scoring functions for pair-wise relations, and 2) a structured support
vector machine (SSVM) to make joint predictions. The neural network
automatically learns representations that account for long-term contexts to
provide robust features for the structured model, while the SSVM incorporates
domain knowledge such as transitive closure of temporal relations as
constraints to make better globally consistent decisions. By jointly training
the two components, our model combines the benefits of both data-driven
learning and knowledge exploitation. Experimental results on three high-quality
event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that
incorporated with pre-trained contextualized embeddings, the proposed model
achieves significantly better performances than the state-of-the-art methods on
all three datasets. We also provide thorough ablation studies to investigate
our model.
| 2,019 | Computation and Language |
Towards Best Experiment Design for Evaluating Dialogue System Output | To overcome the limitations of automated metrics (e.g. BLEU, METEOR) for
evaluating dialogue systems, researchers typically use human judgments to
provide convergent evidence. While it has been demonstrated that human
judgments can suffer from the inconsistency of ratings, extant research has
also found that the design of the evaluation task affects the consistency and
quality of human judgments. We conduct a between-subjects study to understand
the impact of four experiment conditions on human ratings of dialogue system
output. In addition to discrete and continuous scale ratings, we also
experiment with a novel application of Best-Worst scaling to dialogue
evaluation. Through our systematic study with 40 crowdsourced workers in each
task, we find that using continuous scales achieves more consistent ratings
than Likert scale or ranking-based experiment design. Additionally, we find
that factors such as time taken to complete the task and no prior experience of
participating in similar studies of rating dialogue system output positively
impact consistency and agreement amongst raters
| 2,019 | Computation and Language |
Dependency-Guided LSTM-CRF for Named Entity Recognition | Dependency tree structures capture long-distance and syntactic relationships
between words in a sentence. The syntactic relations (e.g., nominal subject,
object) can potentially infer the existence of certain named entities. In
addition, the performance of a named entity recognizer could benefit from the
long-distance dependencies between the words in dependency trees. In this work,
we propose a simple yet effective dependency-guided LSTM-CRF model to encode
the complete dependency trees and capture the above properties for the task of
named entity recognition (NER). The data statistics show strong correlations
between the entity types and dependency relations. We conduct extensive
experiments on several standard datasets and demonstrate the effectiveness of
the proposed model in improving NER and achieving state-of-the-art performance.
Our analysis reveals that the significant improvements mainly result from the
dependency relations and long-distance interactions provided by dependency
trees.
| 2,019 | Computation and Language |
Syntax-Aware Aspect-Level Sentiment Classification with
Proximity-Weighted Convolution Network | It has been widely accepted that Long Short-Term Memory (LSTM) network,
coupled with attention mechanism and memory module, is useful for aspect-level
sentiment classification. However, existing approaches largely rely on the
modelling of semantic relatedness of an aspect with its context words, while to
some extent ignore their syntactic dependencies within sentences. Consequently,
this may lead to an undesirable result that the aspect attends on contextual
words that are descriptive of other aspects. In this paper, we propose a
proximity-weighted convolution network to offer an aspect-specific syntax-aware
representation of contexts. In particular, two ways of determining proximity
weight are explored, namely position proximity and dependency proximity. The
representation is primarily abstracted by a bidirectional LSTM architecture and
further enhanced by a proximity-weighted convolution. Experiments conducted on
the SemEval 2014 benchmark demonstrate the effectiveness of our proposed
approach compared with a range of state-of-the-art models.
| 2,019 | Computation and Language |
Speech Replay Detection with x-Vector Attack Embeddings and Spectral
Features | We present our system submission to the ASVspoof 2019 Challenge Physical
Access (PA) task. The objective for this challenge was to develop a
countermeasure that identifies speech audio as either bona fide or intercepted
and replayed. The target prediction was a value indicating that a speech
segment was bona fide (positive values) or "spoofed" (negative values). Our
system used convolutional neural networks (CNNs) and a representation of the
speech audio that combined x-vector attack embeddings with signal processing
features. The x-vector attack embeddings were created from mel-frequency
cepstral coefficients (MFCCs) using a time-delay neural network (TDNN). These
embeddings jointly modeled 27 different environments and 9 types of attacks
from the labeled data. We also used sub-band spectral centroid magnitude
coefficients (SCMCs) as features. We included an additive Gaussian noise layer
during training as a way to augment the data to make our system more robust to
previously unseen attack examples. We report system performance using the
tandem detection cost function (tDCF) and equal error rate (EER). Our approach
performed better that both of the challenge baselines. Our technique suggests
that our x-vector attack embeddings can help regularize the CNN predictions
even when environments or attacks are more challenging.
| 2,019 | Computation and Language |
TinyBERT: Distilling BERT for Natural Language Understanding | Language model pre-training, such as BERT, has significantly improved the
performances of many natural language processing tasks. However, pre-trained
language models are usually computationally expensive, so it is difficult to
efficiently execute them on resource-restricted devices. To accelerate
inference and reduce model size while maintaining accuracy, we first propose a
novel Transformer distillation method that is specially designed for knowledge
distillation (KD) of the Transformer-based models. By leveraging this new KD
method, the plenty of knowledge encoded in a large teacher BERT can be
effectively transferred to a small student Tiny-BERT. Then, we introduce a new
two-stage learning framework for TinyBERT, which performs Transformer
distillation at both the pretraining and task-specific learning stages. This
framework ensures that TinyBERT can capture he general-domain as well as the
task-specific knowledge in BERT.
TinyBERT with 4 layers is empirically effective and achieves more than 96.8%
the performance of its teacher BERTBASE on GLUE benchmark, while being 7.5x
smaller and 9.4x faster on inference. TinyBERT with 4 layers is also
significantly better than 4-layer state-of-the-art baselines on BERT
distillation, with only about 28% parameters and about 31% inference time of
them. Moreover, TinyBERT with 6 layers performs on-par with its teacher
BERTBASE.
| 2,020 | Computation and Language |
A Consolidated System for Robust Multi-Document Entity Risk Extraction
and Taxonomy Augmentation | We introduce a hybrid human-automated system that provides scalable
entity-risk relation extractions across large data sets. Given an
expert-defined keyword taxonomy, entities, and data sources, the system returns
text extractions based on bidirectional token distances between entities and
keywords and expands taxonomy coverage with word vector encodings. Our system
represents a more simplified architecture compared to alerting focused systems
- motivated by high coverage use cases in the risk mining space such as due
diligence activities and intelligence gathering. We provide an overview of the
system and expert evaluations for a range of token distances. We demonstrate
that single and multi-sentence distance groups significantly outperform
baseline extractions with shorter, single sentences being preferred by
analysts. As the taxonomy expands, the amount of relevant information increases
and multi-sentence extractions become more preferred, but this is tempered
against entity-risk relations become more indirect. We discuss the implications
of these observations on users, management of ambiguity and taxonomy expansion,
and future system modifications.
| 2,019 | Computation and Language |
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related
entity recognition in hospital discharge summaries | Monitoring the administration of drugs and adverse drug reactions are key
parts of pharmacovigilance. In this paper, we explore the extraction of drug
mentions and drug-related information (reason for taking a drug, route,
frequency, dosage, strength, form, duration, and adverse events) from hospital
discharge summaries through deep learning that relies on various
representations for clinical named entity recognition. This work was officially
part of the 2018 n2c2 shared task, and we use the data supplied as part of the
task. We developed two deep learning architecture based on recurrent neural
networks and pre-trained language models. We also explore the effect of
augmenting word representations with semantic features for clinical named
entity recognition. Our feature-augmented BiLSTM-CRF model performed with
F1-score of 92.67% and ranked 4th for entity extraction sub-task among
submitted systems to n2c2 challenge. The recurrent neural networks that use the
pre-trained domain-specific word embeddings and a CRF layer for label
optimization perform drug, adverse event and related entities extraction with
micro-averaged F1-score of over 91%. The augmentation of word vectors with
semantic features extracted using available clinical NLP toolkits can further
improve the performance. Word embeddings that are pre-trained on a large
unannotated corpus of relevant documents and further fine-tuned to the task
perform rather well. However, the augmentation of word embeddings with semantic
features can help improve the performance (primarily by boosting precision) of
drug-related named entity recognition from electronic health records.
| 2,019 | Computation and Language |
Specificity-Based Sentence Ordering for Multi-Document Extractive Risk
Summarization | Risk mining technologies seek to find relevant textual extractions that
capture entity-risk relationships. However, when high volume data sets are
processed, a multitude of relevant extractions can be returned, shifting the
focus to how best to present the results. We provide the details of a risk
mining multi-document extractive summarization system that produces high
quality output by modeling shifts in specificity that are characteristic of
well-formed discourses. In particular, we propose a novel selection algorithm
that alternates between extracts based on human curated or expanded autoencoded
key terms, which exhibit greater specificity or generality as it relates to an
entity-risk relationship. Through this extract ordering, and without the need
for more complex discourse-aware NLP, we induce felicitous shifts in
specificity in the alternating summaries that outperform non-alternating
summaries on automatic ROUGE and BLEU scores, and manual understandability and
preferences evaluations - achieving no statistically significant difference
when compared to human authored summaries.
| 2,019 | Computation and Language |
NLVR2 Visual Bias Analysis | NLVR2 (Suhr et al., 2019) was designed to be robust for language bias through
a data collection process that resulted in each natural language sentence
appearing with both true and false labels. The process did not provide a
similar measure of control for visual bias. This technical report analyzes the
potential for visual bias in NLVR2. We show that some amount of visual bias
likely exists. Finally, we identify a subset of the test data that allows to
test for model performance in a way that is robust to such potential biases. We
show that the performance of existing models (Li et al., 2019; Tan and Bansal
2019) is relatively robust to this potential bias. We propose to add the
evaluation on this subset of the data to the NLVR2 evaluation protocol, and
update the official release to include it. A notebook including an
implementation of the code used to replicate this analysis is available at
http://nlvr.ai/NLVR2BiasAnalysis.html.
| 2,019 | Computation and Language |
Automated Chess Commentator Powered by Neural Chess Engine | In this paper, we explore a new approach for automated chess commentary
generation, which aims to generate chess commentary texts in different
categories (e.g., description, comparison, planning, etc.). We introduce a
neural chess engine into text generation models to help with encoding boards,
predicting moves, and analyzing situations. By jointly training the neural
chess engine and the generation models for different categories, the models
become more effective. We conduct experiments on 5 categories in a benchmark
Chess Commentary dataset and achieve inspiring results in both automatic and
human evaluations.
| 2,019 | Computation and Language |
Biomedical Mention Disambiguation using a Deep Learning Approach | Automatically locating named entities in natural language text - named entity
recognition - is an important task in the biomedical domain. Many named entity
mentions are ambiguous between several bioconcept types, however, causing text
spans to be annotated as more than one type when simultaneously recognizing
multiple entity types. The straightforward solution is a rule-based approach
applying a priority order based on the precision of each entity tagger (from
highest to lowest). While this method is straightforward and useful, imprecise
disambiguation remains a significant source of error. We address this issue by
generating a partially labeled corpus of ambiguous concept mentions. We first
collect named entity mentions from multiple human-curated databases (e.g.
CTDbase, gene2pubmed), then correlate them with the text mined span from
PubTator to provide the context where the mention appears. Our corpus contains
more than 3 million concept mentions that ambiguous between one or more concept
types in PubTator (about 3% of all mentions). We approached this task as a
classification problem and developed a deep learning-based method which uses
the semantics of the span being classified and the surrounding words to
identify the most likely bioconcept type. More specifically, we develop a
convolutional neural network (CNN) and along short-term memory (LSTM) network
to respectively handle the semantic syntax features, then concatenate these
within a fully connected layer for final classification. The priority ordering
rule-based approach demonstrated F1-scores of 71.29% (micro-averaged) and
41.19% (macro-averaged), while the new disambiguation method demonstrated
F1-scores of 91.94% (micro-averaged) and 85.42% (macro-averaged), a very
substantial increase.
| 2,019 | Computation and Language |
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with
Contextualized Embeddings | Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al.,
2018), Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a
major recent innovation in NLP. CWEs provide semantic vector representations of
words depending on their respective context. Their advantage over static word
embeddings has been shown for a number of tasks, such as text classification,
sequence tagging, or machine translation. Since vectors of the same word type
can vary depending on the respective context, they implicitly provide a model
for word sense disambiguation (WSD). We introduce a simple but effective
approach to WSD using a nearest neighbor classification on CWEs. We compare the
performance of different CWE models for the task and can report improvements
above the current state of the art for two standard WSD benchmark datasets. We
further show that the pre-trained BERT model is able to place polysemic words
into distinct 'sense' regions of the embedding space, while ELMo and Flair NLP
do not seem to possess this ability.
| 2,019 | Computation and Language |
Cross-Lingual Natural Language Generation via Pre-Training | In this work we focus on transferring supervision signals of natural language
generation (NLG) tasks between multiple languages. We propose to pretrain the
encoder and the decoder of a sequence-to-sequence model under both monolingual
and cross-lingual settings. The pre-training objective encourages the model to
represent different languages in the shared space, so that we can conduct
zero-shot cross-lingual transfer. After the pre-training procedure, we use
monolingual data to fine-tune the pre-trained model on downstream NLG tasks.
Then the sequence-to-sequence model trained in a single language can be
directly evaluated beyond that language (i.e., accepting multi-lingual input
and producing multi-lingual output). Experimental results on question
generation and abstractive summarization show that our model outperforms the
machine-translation-based pipeline methods for zero-shot cross-lingual
generation. Moreover, cross-lingual transfer improves NLG performance of
low-resource languages by leveraging rich-resource language data. Our
implementation and data are available at https://github.com/CZWin32768/xnlg.
| 2,019 | Computation and Language |
Learning Dense Representations for Entity Retrieval | We show that it is feasible to perform entity linking by training a dual
encoder (two-tower) model that encodes mentions and entities in the same dense
vector space, where candidate entities are retrieved by approximate nearest
neighbor search. Unlike prior work, this setup does not rely on an alias table
followed by a re-ranker, and is thus the first fully learned entity retrieval
model. We show that our dual encoder, trained using only anchor-text links in
Wikipedia, outperforms discrete alias table and BM25 baselines, and is
competitive with the best comparable results on the standard TACKBP-2010
dataset. In addition, it can retrieve candidates extremely fast, and
generalizes well to a new dataset derived from Wikinews. On the modeling side,
we demonstrate the dramatic value of an unsupervised negative mining algorithm
for this task.
| 2,019 | Computation and Language |
Using Priming to Uncover the Organization of Syntactic Representations
in Neural Language Models | Neural language models (LMs) perform well on tasks that require sensitivity
to syntactic structure. Drawing on the syntactic priming paradigm from
psycholinguistics, we propose a novel technique to analyze the representations
that enable such success. By establishing a gradient similarity metric between
structures, this technique allows us to reconstruct the organization of the
LMs' syntactic representational space. We use this technique to demonstrate
that LSTM LMs' representations of different types of sentences with relative
clauses are organized hierarchically in a linguistically interpretable manner,
suggesting that the LMs track abstract properties of the sentence.
| 2,019 | Computation and Language |
Multi-stage Pretraining for Abstractive Summarization | Neural models for abstractive summarization tend to achieve the best
performance in the presence of highly specialized, summarization specific
modeling add-ons such as pointer-generator, coverage-modeling, and
inferencetime heuristics. We show here that pretraining can complement such
modeling advancements to yield improved results in both short-form and
long-form abstractive summarization using two key concepts: full-network
initialization and multi-stage pretraining. Our method allows the model to
transitively benefit from multiple pretraining tasks, from generic language
tasks to a specialized summarization task to an even more specialized one such
as bullet-based summarization. Using this approach, we demonstrate improvements
of 1.05 ROUGE-L points on the Gigaword benchmark and 1.78 ROUGE-L points on the
CNN/DailyMail benchmark, compared to a randomly-initialized baseline.
| 2,019 | Computation and Language |
Data Ordering Patterns for Neural Machine Translation: An Empirical
Study | Recent works show that ordering of the training data affects the model
performance for Neural Machine Translation. Several approaches involving
dynamic data ordering and data sharding based on curriculum learning have been
analysed for the their performance gains and faster convergence. In this work
we propose to empirically study several ordering approaches for the training
data based on different metrics and evaluate their impact on the model
performance. Results from our study show that pre-fixing the ordering of the
training data based on perplexity scores from a pre-trained model performs the
best and outperforms the default approach of randomly shuffling the training
data every epoch.
| 2,019 | Computation and Language |
Portuguese Named Entity Recognition using BERT-CRF | Recent advances in language representation using neural networks have made it
viable to transfer the learned internal states of a trained model to downstream
natural language processing tasks, such as named entity recognition (NER) and
question answering. It has been shown that the leverage of pre-trained language
models improves the overall performance on many tasks and is highly beneficial
when labeled data is scarce. In this work, we train Portuguese BERT models and
employ a BERT-CRF architecture to the NER task on the Portuguese language,
combining the transfer capabilities of BERT with the structured predictions of
CRF. We explore feature-based and fine-tuning training strategies for the BERT
model. Our fine-tuning approach obtains new state-of-the-art results on the
HAREM I dataset, improving the F1-score by 1 point on the selective scenario (5
NE classes) and by 4 points on the total scenario (10 NE classes).
| 2,020 | Computation and Language |
TripleNet: Triple Attention Network for Multi-Turn Response Selection in
Retrieval-based Chatbots | We consider the importance of different utterances in the context for
selecting the response usually depends on the current query. In this paper, we
propose the model TripleNet to fully model the task with the triple <context,
query, response> instead of <context, response> in previous works. The heart of
TripleNet is a novel attention mechanism named triple attention to model the
relationships within the triple at four levels. The new mechanism updates the
representation for each element based on the attention with the other two
concurrently and symmetrically. We match the triple <C, Q, R> centered on the
response from char to context level for prediction. Experimental results on two
large-scale multi-turn response selection datasets show that the proposed model
can significantly outperform the state-of-the-art methods. TripleNet source
code is available at https://github.com/wtma/TripleNet
| 2,019 | Computation and Language |
Knowledge-Enriched Transformer for Emotion Detection in Textual
Conversations | Messages in human conversations inherently convey emotions. The task of
detecting emotions in textual conversations leads to a wide range of
applications such as opinion mining in social networks. However, enabling
machines to analyze emotions in conversations is challenging, partly because
humans often rely on the context and commonsense knowledge to express emotions.
In this paper, we address these challenges by proposing a Knowledge-Enriched
Transformer (KET), where contextual utterances are interpreted using
hierarchical self-attention and external commonsense knowledge is dynamically
leveraged using a context-aware affective graph attention mechanism.
Experiments on multiple textual conversation datasets demonstrate that both
context and commonsense knowledge are consistently beneficial to the emotion
detection performance. In addition, the experimental results show that our KET
model outperforms the state-of-the-art models on most of the tested datasets in
F1 score.
| 2,019 | Computation and Language |
LitGen: Genetic Literature Recommendation Guided by Human Explanations | As genetic sequencing costs decrease, the lack of clinical interpretation of
variants has become the bottleneck in using genetics data. A major rate
limiting step in clinical interpretation is the manual curation of evidence in
the genetic literature by highly trained biocurators. What makes curation
particularly time-consuming is that the curator needs to identify papers that
study variant pathogenicity using different types of approaches and
evidences---e.g. biochemical assays or case control analysis. In collaboration
with the Clinical Genomic Resource (ClinGen)---the flagship NIH program for
clinical curation---we propose the first machine learning system, LitGen, that
can retrieve papers for a particular variant and filter them by specific
evidence types used by curators to assess for pathogenicity. LitGen uses
semi-supervised deep learning to predict the type of evidence provided by each
paper. It is trained on papers annotated by ClinGen curators and systematically
evaluated on new test data collected by ClinGen. LitGen further leverages rich
human explanations and unlabeled data to gain 7.9%-12.6% relative performance
improvement over models learned only on the annotated papers. It is a useful
framework to improve clinical variant curation.
| 2,019 | Computation and Language |
Do Massively Pretrained Language Models Make Better Storytellers? | Large neural language models trained on massive amounts of text have emerged
as a formidable strategy for Natural Language Understanding tasks. However, the
strength of these models as Natural Language Generators is less clear. Though
anecdotal evidence suggests that these models generate better quality text,
there has been no detailed study characterizing their generation abilities. In
this work, we compare the performance of an extensively pretrained model,
OpenAI GPT2-117 (Radford et al., 2019), to a state-of-the-art neural story
generation model (Fan et al., 2018). By evaluating the generated text across a
wide variety of automatic metrics, we characterize the ways in which pretrained
models do, and do not, make better storytellers. We find that although GPT2-117
conditions more strongly on context, is more sensitive to ordering of events,
and uses more unusual words, it is just as likely to produce repetitive and
under-diverse text when using likelihood-maximizing decoding algorithms.
| 2,019 | Computation and Language |
Situating Sentence Embedders with Nearest Neighbor Overlap | As distributed approaches to natural language semantics have developed and
diversified, embedders for linguistic units larger than words have come to play
an increasingly important role. To date, such embedders have been evaluated
using benchmark tasks (e.g., GLUE) and linguistic probes. We propose a
comparative approach, nearest neighbor overlap (N2O), that quantifies
similarity between embedders in a task-agnostic manner. N2O requires only a
collection of examples and is simple to understand: two embedders are more
similar if, for the same set of inputs, there is greater overlap between the
inputs' nearest neighbors. Though applicable to embedders of texts of any size,
we focus on sentence embedders and use N2O to show the effects of different
design choices and architectures.
| 2,019 | Computation and Language |
An Empirical Study of Content Understanding in Conversational Question
Answering | With a lot of work about context-free question answering systems, there is an
emerging trend of conversational question answering models in the natural
language processing field. Thanks to the recently collected datasets, including
QuAC and CoQA, there has been more work on conversational question answering,
and recent work has achieved competitive performance on both datasets. However,
to best of our knowledge, two important questions for conversational
comprehension research have not been well studied: 1) How well can the
benchmark dataset reflect models' content understanding? 2) Do the models well
utilize the conversation content when answering questions? To investigate these
questions, we design different training settings, testing settings, as well as
an attack to verify the models' capability of content understanding on QuAC and
CoQA. The experimental results indicate some potential hazards in the benchmark
datasets, QuAC and CoQA, for conversational comprehension research. Our
analysis also sheds light on both what models may learn and how datasets may
bias the models. With deep investigation of the task, it is believed that this
work can benefit the future progress of conversation comprehension. The source
code is available at https://github.com/MiuLab/CQA-Study.
| 2,019 | Computation and Language |
Technical report on Conversational Question Answering | Conversational Question Answering is a challenging task since it requires
understanding of conversational history. In this project, we propose a new
system RoBERTa + AT +KD, which involves rationale tagging multi-task,
adversarial training, knowledge distillation and a linguistic post-process
strategy. Our single model achieves 90.4(F1) on the CoQA test set without data
augmentation, outperforming the current state-of-the-art single model by 2.6%
F1.
| 2,019 | Computation and Language |
Deep Text Mining of Instagram Data Without Strong Supervision | With the advent of social media, our online feeds increasingly consist of
short, informal, and unstructured text. This textual data can be analyzed for
the purpose of improving user recommendations and detecting trends. Instagram
is one of the largest social media platforms, containing both text and images.
However, most of the prior research on text processing in social media is
focused on analyzing Twitter data, and little attention has been paid to text
mining of Instagram data. Moreover, many text mining methods rely on annotated
training data, which in practice is both difficult and expensive to obtain. In
this paper, we present methods for unsupervised mining of fashion attributes
from Instagram text, which can enable a new kind of user recommendation in the
fashion domain. In this context, we analyze a corpora of Instagram posts from
the fashion domain, introduce a system for extracting fashion attributes from
Instagram, and train a deep clothing classifier with weak supervision to
classify Instagram posts based on the associated text.
With our experiments, we confirm that word embeddings are a useful asset for
information extraction. Experimental results show that information extraction
using word embeddings outperforms a baseline that uses Levenshtein distance.
The results also show the benefit of combining weak supervision signals using
generative models instead of majority voting. Using weak supervision and
generative modeling, an F1 score of 0.61 is achieved on the task of classifying
the image contents of Instagram posts based solely on the associated text,
which is on level with human performance. Finally, our empirical study provides
one of the few available studies on Instagram text and shows that the text is
noisy, that the text distribution exhibits the long-tail phenomenon, and that
comment sections on Instagram are multi-lingual.
| 2,019 | Computation and Language |
In Conclusion Not Repetition: Comprehensive Abstractive Summarization
With Diversified Attention Based On Determinantal Point Processes | Various Seq2Seq learning models designed for machine translation were applied
for abstractive summarization task recently. Despite these models provide high
ROUGE scores, they are limited to generate comprehensive summaries with a high
level of abstraction due to its degenerated attention distribution. We
introduce Diverse Convolutional Seq2Seq Model(DivCNN Seq2Seq) using
Determinantal Point Processes methods(Micro DPPs and Macro DPPs) to produce
attention distribution considering both quality and diversity. Without breaking
the end to end architecture, DivCNN Seq2Seq achieves a higher level of
comprehensiveness compared to vanilla models and strong baselines. All the
reproducible codes and datasets are available online.
| 2,020 | Computation and Language |
Learning ASR-Robust Contextualized Embeddings for Spoken Language
Understanding | Employing pre-trained language models (LM) to extract contextualized word
representations has achieved state-of-the-art performance on various NLP tasks.
However, applying this technique to noisy transcripts generated by automatic
speech recognizer (ASR) is concerned. Therefore, this paper focuses on making
contextualized representations more ASR-robust. We propose a novel
confusion-aware fine-tuning method to mitigate the impact of ASR errors to
pre-trained LMs. Specifically, we fine-tune LMs to produce similar
representations for acoustically confusable words that are obtained from word
confusion networks (WCNs) produced by ASR. Experiments on the benchmark ATIS
dataset show that the proposed method significantly improves the performance of
spoken language understanding when performing on ASR transcripts. Our source
code is available at https://github.com/MiuLab/SpokenVec
| 2,020 | Computation and Language |
Application of Fuzzy Clustering for Text Data Dimensionality Reduction | Large textual corpora are often represented by the document-term frequency
matrix whose elements are the frequency of terms; however, this matrix has two
problems: sparsity and high dimensionality. Four dimension reduction strategies
are used to address these problems. Of the four strategies, unsupervised
feature transformation (UFT) is a popular and efficient strategy to map the
terms to a new basis in the document-term frequency matrix. Although several
UFT-based methods have been developed, fuzzy clustering has not been considered
for dimensionality reduction. This research explores fuzzy clustering as a new
UFT-based approach to create a lower-dimensional representation of documents.
Performance of fuzzy clustering with and without using global term weighting
methods is shown to exceed principal component analysis and singular value
decomposition. This study also explores the effect of applying different
fuzzifier values on fuzzy clustering for dimensionality reduction purpose.
| 2,019 | Computation and Language |
Code-switching Language Modeling With Bilingual Word Embeddings: A Case
Study for Egyptian Arabic-English | Code-switching (CS) is a widespread phenomenon among bilingual and
multilingual societies. The lack of CS resources hinders the performance of
many NLP tasks. In this work, we explore the potential use of bilingual word
embeddings for code-switching (CS) language modeling (LM) in the low resource
Egyptian Arabic-English language. We evaluate different state-of-the-art
bilingual word embeddings approaches that require cross-lingual resources at
different levels and propose an innovative but simple approach that jointly
learns bilingual word representations without the use of any parallel data,
relying only on monolingual and a small amount of CS data. While all
representations improve CS LM, ours performs the best and improves perplexity
33.5% relative over the baseline.
| 2,019 | Computation and Language |
Layerwise Relevance Visualization in Convolutional Text Graph
Classifiers | Representations in the hidden layers of Deep Neural Networks (DNN) are often
hard to interpret since it is difficult to project them into an interpretable
domain. Graph Convolutional Networks (GCN) allow this projection, but existing
explainability methods do not exploit this fact, i.e. do not focus their
explanations on intermediate states. In this work, we present a novel method
that traces and visualizes features that contribute to a classification
decision in the visible and hidden layers of a GCN. Our method exposes hidden
cross-layer dynamics in the input graph structure. We experimentally
demonstrate that it yields meaningful layerwise explanations for a GCN sentence
classifier.
| 2,019 | Computation and Language |
Efficiently Reusing Old Models Across Languages via Transfer Learning | Recent progress in neural machine translation is directed towards larger
neural networks trained on an increasing amount of hardware resources. As a
result, NMT models are costly to train, both financially, due to the
electricity and hardware cost, and environmentally, due to the carbon
footprint. It is especially true in transfer learning for its additional cost
of training the "parent" model before transferring knowledge and training the
desired "child" model. In this paper, we propose a simple method of re-using an
already trained model for different language pairs where there is no need for
modifications in model architecture. Our approach does not need a separate
parent model for each investigated language pair, as it is typical in NMT
transfer learning. To show the applicability of our method, we recycle a
Transformer model trained by different researchers and use it to seed models
for different language pairs. We achieve better translation quality and shorter
convergence times than when training from random initialization.
| 2,020 | Computation and Language |
Assessing the Lexico-Semantic Relational Knowledge Captured by Word and
Concept Embeddings | Deep learning currently dominates the benchmarks for various NLP tasks and,
at the basis of such systems, words are frequently represented as embeddings
--vectors in a low dimensional space-- learned from large text corpora and
various algorithms have been proposed to learn both word and concept
embeddings. One of the claimed benefits of such embeddings is that they capture
knowledge about semantic relations. Such embeddings are most often evaluated
through tasks such as predicting human-rated similarity and analogy which only
test a few, often ill-defined, relations. In this paper, we propose a method
for (i) reliably generating word and concept pair datasets for a wide number of
relations by using a knowledge graph and (ii) evaluating to what extent
pre-trained embeddings capture those relations. We evaluate the approach
against a proprietary and a public knowledge graph and analyze the results,
showing which lexico-semantic relational knowledge is captured by current
embedding learning approaches.
| 2,019 | Computation and Language |
Neural Generative Rhetorical Structure Parsing | Rhetorical structure trees have been shown to be useful for several
document-level tasks including summarization and document classification.
Previous approaches to RST parsing have used discriminative models; however,
these are less sample efficient than generative models, and RST parsing
datasets are typically small. In this paper, we present the first generative
model for RST parsing. Our model is a document-level RNN grammar (RNNG) with a
bottom-up traversal order. We show that, for our parser's traversal order,
previous beam search algorithms for RNNGs have a left-branching bias which is
ill-suited for RST parsing. We develop a novel beam search algorithm that keeps
track of both structure- and word-generating actions without exhibiting this
branching bias and results in absolute improvements of 6.8 and 2.9 on
unlabelled and labelled F1 over previous algorithms. Overall, our generative
model outperforms a discriminative model with the same features by 2.6 F1
points and achieves performance comparable to the state-of-the-art,
outperforming all published parsers from a recent replication study that do not
use additional training data.
| 2,019 | Computation and Language |
Paying Attention to Function Words | All natural languages exhibit a distinction between content words (like nouns
and adjectives) and function words (like determiners, auxiliaries,
prepositions). Yet surprisingly little has been said about the emergence of
this universal architectural feature of natural languages. Why have human
languages evolved to exhibit this division of labor between content and
function words? How could such a distinction have emerged in the first place?
This paper takes steps towards answering these questions by showing how the
distinction can emerge through reinforcement learning in agents playing a
signaling game across contexts which contain multiple objects that possess
multiple perceptually salient gradable properties.
| 2,019 | Computation and Language |
Diachronic Topics in New High German Poetry | Statistical topic models are increasingly and popularly used by Digital
Humanities scholars to perform distant reading tasks on literary data. It
allows us to estimate what people talk about. Especially Latent Dirichlet
Allocation (LDA) has shown its usefulness, as it is unsupervised, robust, easy
to use, scalable, and it offers interpretable results. In a preliminary study,
we apply LDA to a corpus of New High German poetry (textgrid, with 51k poems,
8m token), and use the distribution of topics over documents for a
classification of poems into time periods and for authorship attribution.
| 2,019 | Computation and Language |
Attention Interpretability Across NLP Tasks | The attention layer in a neural network model provides insights into the
model's reasoning behind its prediction, which are usually criticized for being
opaque. Recently, seemingly contradictory viewpoints have emerged about the
interpretability of attention weights (Jain & Wallace, 2019; Vig & Belinkov,
2019). Amid such confusion arises the need to understand attention mechanism
more systematically. In this work, we attempt to fill this gap by giving a
comprehensive explanation which justifies both kinds of observations (i.e.,
when is attention interpretable and when it is not). Through a series of
experiments on diverse NLP tasks, we validate our observations and reinforce
our claim of interpretability of attention through manual evaluation.
| 2,019 | Computation and Language |
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for
Sentiment Analysis | In this article we describe our participation in TASS 2019, a shared task
aimed at the detection of sentiment polarity of Spanish tweets. We combined
different representations such as bag-of-words, bag-of-characters, and tweet
embeddings. In particular, we trained robust subword-aware word embeddings and
computed tweet representations using a weighted-averaging strategy. We also
used two data augmentation techniques to deal with data scarcity: two-way
translation augmentation, and instance crossover augmentation, a novel
technique that generates new instances by combining halves of tweets. In
experiments, we trained linear classifiers and ensemble models, obtaining
highly competitive results despite the simplicity of our approaches.
| 2,019 | Computation and Language |
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space | Finding the right reviewers to assess the quality of conference submissions
is a time consuming process for conference organizers. Given the importance of
this step, various automated reviewer-paper matching solutions have been
proposed to alleviate the burden. Prior approaches, including bag-of-words
models and probabilistic topic models have been inadequate to deal with the
vocabulary mismatch and partial topic overlap between a paper submission and
the reviewer's expertise. Our approach, the common topic model, jointly models
the topics common to the submission and the reviewer's profile while relying on
abstract topic vectors. Experiments and insightful evaluations on two datasets
demonstrate that the proposed method achieves consistent improvements compared
to available state-of-the-art implementations of paper-reviewer matching.
| 2,019 | Computation and Language |
TalkDown: A Corpus for Condescension Detection in Context | Condescending language use is caustic; it can bring dialogues to an end and
bifurcate communities. Thus, systems for condescension detection could have a
large positive impact. A challenge here is that condescension is often
impossible to detect from isolated utterances, as it depends on the discourse
and social context. To address this, we present TalkDown, a new labeled dataset
of condescending linguistic acts in context. We show that extending a
language-only model with representations of the discourse improves performance,
and we motivate techniques for dealing with the low rates of condescension
overall. We also use our model to estimate condescension rates in various
online communities and relate these differences to differing community norms.
| 2,019 | Computation and Language |
Task-Oriented Conversation Generation Using Heterogeneous Memory
Networks | How to incorporate external knowledge into a neural dialogue model is
critically important for dialogue systems to behave like real humans. To handle
this problem, memory networks are usually a great choice and a promising way.
However, existing memory networks do not perform well when leveraging
heterogeneous information from different sources. In this paper, we propose a
novel and versatile external memory networks called Heterogeneous Memory
Networks (HMNs), to simultaneously utilize user utterances, dialogue history
and background knowledge tuples. In our method, historical sequential dialogues
are encoded and stored into the context-aware memory enhanced by gating
mechanism while grounding knowledge tuples are encoded and stored into the
context-free memory. During decoding, the decoder augmented with HMNs
recurrently selects each word in one response utterance from these two memories
and a general vocabulary. Experimental results on multiple real-world datasets
show that HMNs significantly outperform the state-of-the-art data-driven
task-oriented dialogue models in most domains.
| 2,019 | Computation and Language |
Annotated Guidelines and Building Reference Corpus for Myanmar-English
Word Alignment | Reference corpus for word alignment is an important resource for developing
and evaluating word alignment methods. For Myanmar-English language pairs,
there is no reference corpus to evaluate the word alignment tasks. Therefore,
we created the guidelines for Myanmar-English word alignment annotation between
two languages over contrastive learning and built the Myanmar-English reference
corpus consisting of verified alignments from Myanmar ALT of the Asian Language
Treebank (ALT). This reference corpus contains confident labels sure (S) and
possible (P) for word alignments which are used to test for the purpose of
evaluation of the word alignments tasks. We discuss the most linking
ambiguities to define consistent and systematic instructions to align manual
words. We evaluated the results of annotators agreement using our reference
corpus in terms of alignment error rate (AER) in word alignment tasks and
discuss the words relationships in terms of BLEU scores.
| 2,019 | Computation and Language |
Question Answering is a Format; When is it Useful? | Recent years have seen a dramatic expansion of tasks and datasets posed as
question answering, from reading comprehension, semantic role labeling, and
even machine translation, to image and video understanding. With this
expansion, there are many differing views on the utility and definition of
"question answering" itself. Some argue that its scope should be narrow, or
broad, or that it is overused in datasets today. In this opinion piece, we
argue that question answering should be considered a format which is sometimes
useful for studying particular phenomena, not a phenomenon or task in itself.
We discuss when a task is correctly described as question answering, and when a
task is usefully posed as question answering, instead of using some other
format.
| 2,019 | Computation and Language |
Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity
towards a particular aspect in a sentence. Recently, this task has been widely
addressed by the neural attention mechanism, which computes attention weights
to softly select words for generating aspect-specific sentence representations.
The attention is expected to concentrate on opinion words for accurate
sentiment prediction. However, attention is prone to be distracted by noisy or
misleading words, or opinion words from other aspects. In this paper, we
propose an alternative hard-selection approach, which determines the start and
end positions of the opinion snippet, and selects the words between these two
positions for sentiment prediction. Specifically, we learn deep associations
between the sentence and aspect, and the long-term dependencies within the
sentence by leveraging the pre-trained BERT model. We further detect the
opinion snippet by self-critical reinforcement learning. Especially,
experimental results demonstrate the effectiveness of our method and prove that
our hard-selection approach outperforms soft-selection approaches when handling
multi-aspect sentences.
| 2,019 | Computation and Language |
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph
Completion | For large-scale knowledge graphs (KGs), recent research has been focusing on
the large proportion of infrequent relations which have been ignored by
previous studies. For example few-shot learning paradigm for relations has been
investigated. In this work, we further advocate that handling uncommon entities
is inevitable when dealing with infrequent relations. Therefore, we propose a
meta-learning framework that aims at handling infrequent relations with
few-shot learning and uncommon entities by using textual descriptions. We
design a novel model to better extract key information from textual
descriptions. Besides, we also develop a novel generative model in our
framework to enhance the performance by generating extra triplets during the
training stage. Experiments are conducted on two datasets from real-world KGs,
and the results show that our framework outperforms previous methods when
dealing with infrequent relations and their accompanying uncommon entities.
| 2,019 | Computation and Language |
Multi-Dimensional Explanation of Target Variables from Documents | Automated predictions require explanations to be interpretable by humans.
Past work used attention and rationale mechanisms to find words that predict
the target variable of a document. Often though, they result in a tradeoff
between noisy explanations or a drop in accuracy. Furthermore, rationale
methods cannot capture the multi-faceted nature of justifications for multiple
targets, because of the non-probabilistic nature of the mask. In this paper, we
propose the Multi-Target Masker (MTM) to address these shortcomings. The
novelty lies in the soft multi-dimensional mask that models a relevance
probability distribution over the set of target variables to handle
ambiguities. Additionally, two regularizers guide MTM to induce long,
meaningful explanations. We evaluate MTM on two datasets and show, using
standard metrics and human annotations, that the resulting masks are more
accurate and coherent than those generated by the state-of-the-art methods.
Moreover, MTM is the first to also achieve the highest F1 scores for all the
target variables simultaneously.
| 2,020 | Computation and Language |
Breaking the Data Barrier: Towards Robust Speech Translation via
Adversarial Stability Training | In a pipeline speech translation system, automatic speech recognition (ASR)
system will transmit errors in recognition to the downstream machine
translation (MT) system. A standard machine translation system is usually
trained on parallel corpus composed of clean text and will perform poorly on
text with recognition noise, a gap well known in speech translation community.
In this paper, we propose a training architecture which aims at making a neural
machine translation model more robust against speech recognition errors. Our
approach addresses the encoder and the decoder simultaneously using adversarial
learning and data augmentation, respectively. Experimental results on IWSLT2018
speech translation task show that our approach can bridge the gap between the
ASR output and the MT input, outperforms the baseline by up to 2.83 BLEU on
noisy ASR output, while maintaining close performance on clean text.
| 2,019 | Computation and Language |
Developing a Fine-Grained Corpus for a Less-resourced Language: the case
of Kurdish | Kurdish is a less-resourced language consisting of different dialects written
in various scripts. Approximately 30 million people in different countries
speak the language. The lack of corpora is one of the main obstacles in Kurdish
language processing. In this paper, we present KTC-the Kurdish Textbooks
Corpus, which is composed of 31 K-12 textbooks in Sorani dialect. The corpus is
normalized and categorized into 12 educational subjects containing 693,800
tokens (110,297 types). Our resource is publicly available for non-commercial
use under the CC BY-NC-SA 4.0 license.
| 2,019 | Computation and Language |
Semi-supervised Text Style Transfer: Cross Projection in Latent Space | Text style transfer task requires the model to transfer a sentence of one
style to another style while retaining its original content meaning, which is a
challenging problem that has long suffered from the shortage of parallel data.
In this paper, we first propose a semi-supervised text style transfer model
that combines the small-scale parallel data with the large-scale nonparallel
data. With these two types of training data, we introduce a projection function
between the latent space of different styles and design two constraints to
train it. We also introduce two other simple but effective semi-supervised
methods to compare with. To evaluate the performance of the proposed methods,
we build and release a novel style transfer dataset that alters sentences
between the style of ancient Chinese poem and the modern Chinese.
| 2,019 | Computation and Language |
Learning A Unified Named Entity Tagger From Multiple Partially Annotated
Corpora For Efficient Adaptation | Named entity recognition (NER) identifies typed entity mentions in raw text.
While the task is well-established, there is no universally used tagset: often,
datasets are annotated for use in downstream applications and accordingly only
cover a small set of entity types relevant to a particular task. For instance,
in the biomedical domain, one corpus might annotate genes, another chemicals,
and another diseases---despite the texts in each corpus containing references
to all three types of entities. In this paper, we propose a deep structured
model to integrate these "partially annotated" datasets to jointly identify all
entity types appearing in the training corpora. By leveraging multiple
datasets, the model can learn robust input representations; by building a joint
structured model, it avoids potential conflicts caused by combining several
models' predictions at test time. Experiments show that the proposed model
significantly outperforms strong multi-task learning baselines when training on
multiple, partially annotated datasets and testing on datasets that contain
tags from more than one of the training corpora.
| 2,019 | Computation and Language |
Extremely Small BERT Models from Mixed-Vocabulary Training | Pretrained language models like BERT have achieved good results on NLP tasks,
but are impractical on resource-limited devices due to memory footprint. A
large fraction of this footprint comes from the input embeddings with large
input vocabulary and embedding dimensions. Existing knowledge distillation
methods used for model compression cannot be directly applied to train student
models with reduced vocabulary sizes. To this end, we propose a distillation
method to align the teacher and student embeddings via mixed-vocabulary
training. Our method compresses BERT-LARGE to a task-agnostic model with
smaller vocabulary and hidden dimensions, which is an order of magnitude
smaller than other distilled BERT models and offers a better size-accuracy
trade-off on language understanding benchmarks as well as a practical dialogue
task.
| 2,021 | Computation and Language |
Speech Recognition with Augmented Synthesized Speech | Recent success of the Tacotron speech synthesis architecture and its variants
in producing natural sounding multi-speaker synthesized speech has raised the
exciting possibility of replacing expensive, manually transcribed,
domain-specific, human speech that is used to train speech recognizers. The
multi-speaker speech synthesis architecture can learn latent embedding spaces
of prosody, speaker and style variations derived from input acoustic
representations thereby allowing for manipulation of the synthesized speech. In
this paper, we evaluate the feasibility of enhancing speech recognition
performance using speech synthesis using two corpora from different domains. We
explore algorithms to provide the necessary acoustic and lexical diversity
needed for robust speech recognition. Finally, we demonstrate the feasibility
of this approach as a data augmentation strategy for domain-transfer.
We find that improvements to speech recognition performance is achievable by
augmenting training data with synthesized material. However, there remains a
substantial gap in performance between recognizers trained on human speech
those trained on synthesized speech.
| 2,019 | Computation and Language |
The Power of Communities: A Text Classification Model with Automated
Labeling Process Using Network Community Detection | Text classification is one of the most critical areas in machine learning and
artificial intelligence research. It has been actively adopted in many business
applications such as conversational intelligence systems, news articles
categorizations, sentiment analysis, emotion detection systems, and many other
recommendation systems in our daily life. One of the problems in supervised
text classification models is that the models' performance depends heavily on
the quality of data labeling that is typically done by humans. In this study,
we propose a new network community detection-based approach to automatically
label and classify text data into multiclass value spaces. Specifically, we
build networks with sentences as the network nodes and pairwise cosine
similarities between the Term Frequency-Inversed Document Frequency (TFIDF)
vector representations of the sentences as the network link weights. We use the
Louvain method to detect the communities in the sentence networks. We train and
test the Support Vector Machine and the Random Forest models on both the
human-labeled data and network community detection labeled data. Results showed
that models with the data labeled by the network community detection
outperformed the models with the human-labeled data by 2.68-3.75% of
classification accuracy. Our method may help developments of more accurate
conversational intelligence and other text classification systems.
| 2,019 | Computation and Language |
FreeLB: Enhanced Adversarial Training for Natural Language Understanding | Adversarial training, which minimizes the maximal risk for label-preserving
input perturbations, has proved to be effective for improving the
generalization of language models. In this work, we propose a novel adversarial
training algorithm, FreeLB, that promotes higher invariance in the embedding
space, by adding adversarial perturbations to word embeddings and minimizing
the resultant adversarial risk inside different regions around input samples.
To validate the effectiveness of the proposed approach, we apply it to
Transformer-based models for natural language understanding and commonsense
reasoning tasks. Experiments on the GLUE benchmark show that when applied only
to the finetuning stage, it is able to improve the overall test scores of
BERT-base model from 78.3 to 79.4, and RoBERTa-large model from 88.5 to 88.8.
In addition, the proposed approach achieves state-of-the-art single-model test
accuracies of 85.44\% and 67.75\% on ARC-Easy and ARC-Challenge. Experiments on
CommonsenseQA benchmark further demonstrate that FreeLB can be generalized and
boost the performance of RoBERTa-large model on other tasks as well. Code is
available at \url{https://github.com/zhuchen03/FreeLB .
| 2,020 | Computation and Language |
SIM: A Slot-Independent Neural Model for Dialogue State Tracking | Dialogue state tracking is an important component in task-oriented dialogue
systems to identify users' goals and requests as a dialogue proceeds. However,
as most previous models are dependent on dialogue slots, the model complexity
soars when the number of slots increases. In this paper, we put forward a
slot-independent neural model (SIM) to track dialogue states while keeping the
model complexity invariant to the number of dialogue slots. The model utilizes
attention mechanisms between user utterance and system actions. SIM achieves
state-of-the-art results on WoZ and DSTC2 tasks, with only 20% of the model
size of previous models.
| 2,019 | Computation and Language |
Large-scale Pretraining for Neural Machine Translation with Tens of
Billions of Sentence Pairs | In this paper, we investigate the problem of training neural machine
translation (NMT) systems with a dataset of more than 40 billion bilingual
sentence pairs, which is larger than the largest dataset to date by orders of
magnitude. Unprecedented challenges emerge in this situation compared to
previous NMT work, including severe noise in the data and prohibitively long
training time. We propose practical solutions to handle these issues and
demonstrate that large-scale pretraining significantly improves NMT
performance. We are able to push the BLEU score of WMT17 Chinese-English
dataset to 32.3, with a significant performance boost of +3.2 over existing
state-of-the-art results.
| 2,019 | Computation and Language |
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer
Learning and Auxiliary Labels | Aspect and opinion term extraction is a critical step in Aspect-Based
Sentiment Analysis (ABSA). Our study focuses on evaluating transfer learning
using pre-trained BERT (Devlin et al., 2018) to classify tokens from hotel
reviews in bahasa Indonesia. The primary challenge is the language informality
of the review texts. By utilizing transfer learning from a multilingual model,
we achieved up to 2% difference on token level F1-score compared to the
state-of-the-art Bi-LSTM model with fewer training epochs (3 vs. 200 epochs).
The fine-tuned model clearly outperforms the Bi-LSTM model on the entity level.
Furthermore, we propose a method to include CRF with auxiliary labels as an
output layer for the BERT-based models. The CRF addition further improves the
F1-score for both token and entity level.
| 2,020 | Computation and Language |
Fine-tune Bert for DocRED with Two-step Process | Modelling relations between multiple entities has attracted increasing
attention recently, and a new dataset called DocRED has been collected in order
to accelerate the research on the document-level relation extraction. Current
baselines for this task uses BiLSTM to encode the whole document and are
trained from scratch. We argue that such simple baselines are not strong enough
to model to complex interaction between entities. In this paper, we further
apply a pre-trained language model (BERT) to provide a stronger baseline for
this task. We also find that solving this task in phases can further improve
the performance. The first step is to predict whether or not two entities have
a relation, the second step is to predict the specific relation.
| 2,019 | Computation and Language |
ALBERT: A Lite BERT for Self-supervised Learning of Language
Representations | Increasing model size when pretraining natural language representations often
results in improved performance on downstream tasks. However, at some point
further model increases become harder due to GPU/TPU memory limitations and
longer training times. To address these problems, we present two
parameter-reduction techniques to lower memory consumption and increase the
training speed of BERT. Comprehensive empirical evidence shows that our
proposed methods lead to models that scale much better compared to the original
BERT. We also use a self-supervised loss that focuses on modeling
inter-sentence coherence, and show it consistently helps downstream tasks with
multi-sentence inputs. As a result, our best model establishes new
state-of-the-art results on the GLUE, RACE, and \squad benchmarks while having
fewer parameters compared to BERT-large. The code and the pretrained models are
available at https://github.com/google-research/ALBERT.
| 2,020 | Computation and Language |
Low-Resource Response Generation with Template Prior | We study open domain response generation with limited message-response pairs.
The problem exists in real-world applications but is less explored by the
existing work. Since the paired data now is no longer enough to train a neural
generation model, we consider leveraging the large scale of unpaired data that
are much easier to obtain, and propose response generation with both paired and
unpaired data. The generation model is defined by an encoder-decoder
architecture with templates as prior, where the templates are estimated from
the unpaired data as a neural hidden semi-markov model. By this means, response
generation learned from the small paired data can be aided by the semantic and
syntactic knowledge in the large unpaired data. To balance the effect of the
prior and the input message to response generation, we propose learning the
whole generation model with an adversarial approach. Empirical studies on
question response generation and sentiment response generation indicate that
when only a few pairs are available, our model can significantly outperform
several state-of-the-art response generation models in terms of both automatic
and human evaluation.
| 2,020 | Computation and Language |
Read, Attend and Comment: A Deep Architecture for Automatic News Comment
Generation | Automatic news comment generation is a new testbed for techniques of natural
language generation. In this paper, we propose a "read-attend-comment"
procedure for news comment generation and formalize the procedure with a
reading network and a generation network. The reading network comprehends a
news article and distills some important points from it, then the generation
network creates a comment by attending to the extracted discrete points and the
news title. We optimize the model in an end-to-end manner by maximizing a
variational lower bound of the true objective using the back-propagation
algorithm. Experimental results on two datasets indicate that our model can
significantly outperform existing methods in terms of both automatic evaluation
and human judgment.
| 2,019 | Computation and Language |
Selecting Artificially-Generated Sentences for Fine-Tuning Neural
Machine Translation | Neural Machine Translation (NMT) models tend to achieve best performance when
larger sets of parallel sentences are provided for training. For this reason,
augmenting the training set with artificially-generated sentence pairs can
boost performance.
Nonetheless, the performance can also be improved with a small number of
sentences if they are in the same domain as the test set. Accordingly, we want
to explore the use of artificially-generated sentences along with
data-selection algorithms to improve German-to-English NMT models trained
solely with authentic data.
In this work, we show how artificially-generated sentences can be more
beneficial than authentic pairs, and demonstrate their advantages when used in
combination with data-selection algorithms.
| 2,019 | Computation and Language |
Improving Fine-grained Entity Typing with Entity Linking | Fine-grained entity typing is a challenging problem since it usually involves
a relatively large tag set and may require to understand the context of the
entity mention. In this paper, we use entity linking to help with the
fine-grained entity type classification process. We propose a deep neural model
that makes predictions based on both the context and the information obtained
from entity linking results. Experimental results on two commonly used datasets
demonstrates the effectiveness of our approach. On both datasets, it achieves
more than 5\% absolute strict accuracy improvement over the state of the art.
| 2,019 | Computation and Language |
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution
Model for Task-Oriented Dialogue | Ellipsis and co-reference are common and ubiquitous especially in multi-turn
dialogues. In this paper, we treat the resolution of ellipsis and co-reference
in dialogue as a problem of generating omitted or referred expressions from the
dialogue context. We therefore propose a unified end-to-end Generative Ellipsis
and CO-reference Resolution model (GECOR) in the context of dialogue. The model
can generate a new pragmatically complete user utterance by alternating the
generation and copy mode for each user utterance. A multi-task learning
framework is further proposed to integrate the GECOR into an end-to-end
task-oriented dialogue. In order to train both the GECOR and the multi-task
learning framework, we manually construct a new dataset on the basis of the
public dataset CamRest676 with both ellipsis and co-reference annotation. On
this dataset, intrinsic evaluations on the resolution of ellipsis and
co-reference show that the GECOR model significantly outperforms the
sequence-to-sequence (seq2seq) baseline model in terms of EM, BLEU and F1 while
extrinsic evaluations on the downstream dialogue task demonstrate that our
multi-task learning framework with GECOR achieves a higher success rate of task
completion than TSCP, a state-of-the-art end-to-end task-oriented dialogue
model.
| 2,019 | Computation and Language |
MinWikiSplit: A Sentence Splitting Corpus with Minimal Propositions | We compiled a new sentence splitting corpus that is composed of 203K pairs of
aligned complex source and simplified target sentences. Contrary to previously
proposed text simplification corpora, which contain only a small number of
split examples, we present a dataset where each input sentence is broken down
into a set of minimal propositions, i.e. a sequence of sound, self-contained
utterances with each of them presenting a minimal semantic unit that cannot be
further decomposed into meaningful propositions. This corpus is useful for
developing sentence splitting approaches that learn how to transform sentences
with a complex linguistic structure into a fine-grained representation of short
sentences that present a simple and more regular structure which is easier to
process for downstream applications and thus facilitates and improves their
performance.
| 2,019 | Computation and Language |
Semantic Change and Emerging Tropes In a Large Corpus of New High German
Poetry | Due to its semantic succinctness and novelty of expression, poetry is a great
test bed for semantic change analysis. However, so far there is a scarcity of
large diachronic corpora. Here, we provide a large corpus of German poetry
which consists of about 75k poems with more than 11 million tokens, with poems
ranging from the 16th to early 20th century. We then track semantic change in
this corpus by investigating the rise of tropes (`love is magic') over time and
detecting change points of meaning, which we find to occur particularly within
the German Romantic period. Additionally, through self-similarity, we
reconstruct literary periods and find evidence that the law of linear semantic
change also applies to poetry.
| 2,019 | Computation and Language |
DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor
English and German | We introduce DisSim, a discourse-aware sentence splitting framework for
English and German whose goal is to transform syntactically complex sentences
into an intermediate representation that presents a simple and more regular
structure which is easier to process for downstream semantic applications. For
this purpose, we turn input sentences into a two-layered semantic hierarchy in
the form of core facts and accompanying contexts, while identifying the
rhetorical relations that hold between them. In that way, we preserve the
coherence structure of the input and, hence, its interpretability for
downstream tasks.
| 2,019 | Computation and Language |
DARTS: Dialectal Arabic Transcription System | We present the speech to text transcription system, called DARTS, for low
resource Egyptian Arabic dialect. We analyze the following; transfer learning
from high resource broadcast domain to low-resource dialectal domain and
semi-supervised learning where we use in-domain unlabeled audio data collected
from YouTube. Key features of our system are: A deep neural network acoustic
model that consists of a front end Convolutional Neural Network (CNN) followed
by several layers of Time Delayed Neural Network (TDNN) and Long-Short Term
Memory Recurrent Neural Network (LSTM); sequence discriminative training of the
acoustic model; n-gram and recurrent neural network language model for decoding
and N-best list rescoring. We show that a simple transfer learning method can
achieve good results. The results are further improved by using unlabeled data
from YouTube in a semi-supervised setup. Various systems are combined to give
the final system that achieves the lowest word error on on the community
standard Egyptian-Arabic speech dataset (MGB-3).
| 2,019 | Computation and Language |
An Investigation into the Effectiveness of Enhancement in ASR Training
and Test for CHiME-5 Dinner Party Transcription | Despite the strong modeling power of neural network acoustic models, speech
enhancement has been shown to deliver additional word error rate improvements
if multi-channel data is available. However, there has been a longstanding
debate whether enhancement should also be carried out on the ASR training data.
In an extensive experimental evaluation on the acoustically very challenging
CHiME-5 dinner party data we show that: (i) cleaning up the training data can
lead to substantial error rate reductions, and (ii) enhancement in training is
advisable as long as enhancement in test is at least as strong as in training.
This approach stands in contrast and delivers larger gains than the common
strategy reported in the literature to augment the training database with
additional artificially degraded speech. Together with an acoustic model
topology consisting of initial CNN layers followed by factorized TDNN layers we
achieve with 41.6% and 43.2% WER on the DEV and EVAL test sets, respectively, a
new single-system state-of-the-art result on the CHiME-5 data. This is a 8%
relative improvement compared to the best word error rate published so far for
a speech recognizer without system combination.
| 2,019 | Computation and Language |
Keyphrase Generation for Scientific Articles using GANs | In this paper, we present a keyphrase generation approach using conditional
Generative Adversarial Networks (GAN). In our GAN model, the generator outputs
a sequence of keyphrases based on the title and abstract of a scientific
article. The discriminator learns to distinguish between machine-generated and
human-curated keyphrases. We evaluate this approach on standard benchmark
datasets. Our model achieves state-of-the-art performance in generation of
abstractive keyphrases and is also comparable to the best performing extractive
techniques. We also demonstrate that our method generates more diverse
keyphrases and make our implementation publicly available.
| 2,019 | Computation and Language |
KnowBias: Detecting Political Polarity in Long Text Content | We introduce a classification scheme for detecting political bias in long
text content such as newspaper opinion articles. Obtaining long text data and
annotations at sufficient scale for training is difficult, but it is relatively
easy to extract political polarity from tweets through their authorship. We
train on tweets and perform inference on articles. Universal sentence encoders
and other existing methods that aim to address this domain-adaptation scenario
deliver inaccurate and inconsistent predictions on articles, which we show is
due to a difference in opinion concentration between tweets and articles. We
propose a two-step classification scheme that uses a neutral detector trained
on tweets to remove neutral sentences from articles in order to align opinion
concentration and therefore improve accuracy on that domain. Our implementation
is available for public use at https://knowbias.ml.
| 2,019 | Computation and Language |
Learning to Create Sentence Semantic Relation Graphs for Multi-Document
Summarization | Linking facts across documents is a challenging task, as the language used to
express the same information in a sentence can vary significantly, which
complicates the task of multi-document summarization. Consequently, existing
approaches heavily rely on hand-crafted features, which are domain-dependent
and hard to craft, or additional annotated data, which is costly to gather. To
overcome these limitations, we present a novel method, which makes use of two
types of sentence embeddings: universal embeddings, which are trained on a
large unrelated corpus, and domain-specific embeddings, which are learned
during training.
To this end, we develop SemSentSum, a fully data-driven model able to
leverage both types of sentence embeddings by building a sentence semantic
relation graph. SemSentSum achieves competitive results on two types of
summary, consisting of 665 bytes and 100 words. Unlike other state-of-the-art
models, neither hand-crafted features nor additional annotated data are
necessary, and the method is easily adaptable for other tasks. To our
knowledge, we are the first to use multiple sentence embeddings for the task of
multi-document summarization.
| 2,019 | Computation and Language |
A Comparison of Hybrid and End-to-End Models for Syllable Recognition | This paper presents a comparison of a traditional hybrid speech recognition
system (kaldi using WFST and TDNN with lattice-free MMI) and a lexicon-free
end-to-end (TensorFlow implementation of multi-layer LSTM with CTC training)
models for German syllable recognition on the Verbmobil corpus. The results
show that explicitly modeling prior knowledge is still valuable in building
recognition systems. With a strong language model (LM) based on syllables, the
structured approach significantly outperforms the end-to-end model. The best
word error rate (WER) regarding syllables was achieved using kaldi with a
4-gram LM, modeling all syllables observed in the training set. It achieved
10.0% WER w.r.t. the syllables, compared to the end-to-end approach where the
best WER was 27.53%. The work presented here has implications for building
future recognition systems that operate independent of a large vocabulary, as
typically used in a tasks such as recognition of syllabic or agglutinative
languages, out-of-vocabulary techniques, keyword search indexing and medical
speech processing.
| 2,019 | Computation and Language |
Deep Ensemble Learning for News Stance Detection | Stance detection in fake news is an important component in news veracity
assessment because this process helps fact-checking by understanding stance to
a central claim from different information sources. The Fake News Challenge
Stage 1 (FNC-1) held in 2017 was setup for this purpose, which involves
estimating the stance of a news article body relative to a given headline. This
thesis starts from the error analysis for the three top-performing systems in
FNC-1. Based on the analysis, a simple but tough-to-beat Multilayer Perceptron
system is chosen as the baseline. Afterwards, three approaches are explored to
improve baseline.The first approach explores the possibility of improving the
prediction accuracy by adding extra keywords features when training a model,
where keywords are converted to an indicator vector and then concatenated to
the baseline features. A list of keywords is manually selected based on the
error analysis, which may best reflect some characteristics of fake news titles
and bodies. To make this selection process automatically, three algorithms are
created based on Mutual Information (MI) theory: keywords generator based on MI
stance class, MI customised class, and Pointwise MI algorithm. The second
approach is based on word embedding, where word2vec model is introduced and two
document similarities calculation algorithms are implemented: wor2vec cosine
similarity and WMD distance. The third approach is ensemble learning. Different
models are configured together with two continuous outputs combining
algorithms. The 10-fold cross validation reveals that the ensemble of three
neural network models trained from simple bag-of-words features gives the best
performance. It is therefore selected to compete in FNC-1. After
hyperparameters fine tuning, the selected deep ensemble model beats the FNC-1
winner team by a remarkable 34.25 marks under FNC-1's evaluation metric.
| 2,019 | Computation and Language |
Rethinking Text Attribute Transfer: A Lexical Analysis | Text attribute transfer is modifying certain linguistic attributes (e.g.
sentiment, style, authorship, etc.) of a sentence and transforming them from
one type to another. In this paper, we aim to analyze and interpret what is
changed during the transfer process. We start from the observation that in many
existing models and datasets, certain words within a sentence play important
roles in determining the sentence attribute class. These words are referred to
as \textit{the Pivot Words}. Based on these pivot words, we propose a lexical
analysis framework, \textit{the Pivot Analysis}, to quantitatively analyze the
effects of these words in text attribute classification and transfer. We apply
this framework to existing datasets and models and show that: (1) the pivot
words are strong features for the classification of sentence attributes; (2) to
change the attribute of a sentence, many datasets only requires to change
certain pivot words; (3) consequently, many transfer models only perform the
lexical-level modification, while leaving higher-level sentence structures
unchanged. Our work provides an in-depth understanding of linguistic attribute
transfer and further identifies the future requirements and challenges of this
task\footnote{Our code can be found at
https://github.com/FranxYao/pivot_analysis}.
| 2,019 | Computation and Language |
Coin_flipper at eHealth-KD Challenge 2019: Voting LSTMs for Key Phrases
and Semantic Relation Identification Applied to Spanish eHealth Texts | This paper describes our approach presented for the eHealth-KD 2019
challenge. Our participation was aimed at testing how far we could go using
generic tools for Text-Processing but, at the same time, using common
optimization techniques in the field of Data Mining. The architecture proposed
for both tasks of the challenge is a standard stacked 2-layer bi-LSTM. The main
particularities of our approach are: (a) The use of a surrogate function of F1
as loss function to close the gap between the minimization function and the
evaluation metric, and (b) The generation of an ensemble of models for
generating predictions by majority vote. Our system ranked second with an F1
score of 62.18% in the main task by a narrow margin with the winner that scored
63.94%.
| 2,019 | Computation and Language |
On the Importance of Subword Information for Morphological Tasks in
Truly Low-Resource Languages | Recent work has validated the importance of subword information for word
representation learning. Since subwords increase parameter sharing ability in
neural models, their value should be even more pronounced in low-data regimes.
In this work, we therefore provide a comprehensive analysis focused on the
usefulness of subwords for word representation learning in truly low-resource
scenarios and for three representative morphological tasks: fine-grained entity
typing, morphological tagging, and named entity recognition. We conduct a
systematic study that spans several dimensions of comparison: 1) type of data
scarcity which can stem from the lack of task-specific training data, or even
from the lack of unannotated data required to train word embeddings, or both;
2) language type by working with a sample of 16 typologically diverse languages
including some truly low-resource ones (e.g. Rusyn, Buryat, and Zulu); 3) the
choice of the subword-informed word representation method. Our main results
show that subword-informed models are universally useful across all language
types, with large gains over subword-agnostic embeddings. They also suggest
that the effective use of subwords largely depends on the language (type) and
the task at hand, as well as on the amount of available data for training the
embeddings and task-based models, where having sufficient in-task data is a
more critical requirement.
| 2,019 | Computation and Language |
Monotonic Multihead Attention | Simultaneous machine translation models start generating a target sequence
before they have encoded or read the source sequence. Recent approaches for
this task either apply a fixed policy on a state-of-the art Transformer model,
or a learnable monotonic attention on a weaker recurrent neural network-based
structure. In this paper, we propose a new attention mechanism, Monotonic
Multihead Attention (MMA), which extends the monotonic attention mechanism to
multihead attention. We also introduce two novel and interpretable approaches
for latency control that are specifically designed for multiple attentions
heads. We apply MMA to the simultaneous machine translation task and
demonstrate better latency-quality tradeoffs compared to MILk, the previous
state-of-the-art approach. We also analyze how the latency controls affect the
attention span and we motivate the introduction of our model by analyzing the
effect of the number of decoder layers and heads on quality and latency.
| 2,019 | Computation and Language |
Optimizing Speech Recognition For The Edge | While most deployed speech recognition systems today still run on servers, we
are in the midst of a transition towards deployments on edge devices. This leap
to the edge is powered by the progression from traditional speech recognition
pipelines to end-to-end (E2E) neural architectures, and the parallel
development of more efficient neural network topologies and optimization
techniques. Thus, we are now able to create highly accurate speech recognizers
that are both small and fast enough to execute on typical mobile devices. In
this paper, we begin with a baseline RNN-Transducer architecture comprised of
Long Short-Term Memory (LSTM) layers. We then experiment with a variety of more
computationally efficient layer types, as well as apply optimization techniques
like neural connection pruning and parameter quantization to construct a small,
high quality, on-device speech recognizer that is an order of magnitude smaller
than the baseline system without any optimizations.
| 2,020 | Computation and Language |
Biomedical relation extraction with pre-trained language representations
and minimal task-specific architecture | This paper presents our participation in the AGAC Track from the 2019 BioNLP
Open Shared Tasks. We provide a solution for Task 3, which aims to extract
"gene - function change - disease" triples, where "gene" and "disease" are
mentions of particular genes and diseases respectively and "function change" is
one of four pre-defined relationship types. Our system extends BERT (Devlin et
al., 2018), a state-of-the-art language model, which learns contextual language
representations from a large unlabelled corpus and whose parameters can be
fine-tuned to solve specific tasks with minimal additional architecture. We
encode the pair of mentions and their textual context as two consecutive
sequences in BERT, separated by a special symbol. We then use a single linear
layer to classify their relationship into five classes (four pre-defined, as
well as 'no relation'). Despite considerable class imbalance, our system
significantly outperforms a random baseline while relying on an extremely
simple setup with no specially engineered features.
| 2,019 | Computation and Language |
Improving RNN Transducer Modeling for End-to-End Speech Recognition | In the last few years, an emerging trend in automatic speech recognition
research is the study of end-to-end (E2E) systems. Connectionist Temporal
Classification (CTC), Attention Encoder-Decoder (AED), and RNN Transducer
(RNN-T) are the most popular three methods. Among these three methods, RNN-T
has the advantages to do online streaming which is challenging to AED and it
doesn't have CTC's frame-independence assumption. In this paper, we improve the
RNN-T training in two aspects. First, we optimize the training algorithm of
RNN-T to reduce the memory consumption so that we can have larger training
minibatch for faster training speed. Second, we propose better model structures
so that we obtain RNN-T models with the very good accuracy but small footprint.
Trained with 30 thousand hours anonymized and transcribed Microsoft production
data, the best RNN-T model with even smaller model size (216 Megabytes)
achieves up-to 11.8% relative word error rate (WER) reduction from the baseline
RNN-T model. This best RNN-T model is significantly better than the device
hybrid model with similar size by achieving up-to 15.0% relative WER reduction,
and obtains similar WERs as the server hybrid model of 5120 Megabytes in size.
| 2,019 | Computation and Language |
Learning the Difference that Makes a Difference with
Counterfactually-Augmented Data | Despite alarm over the reliance of machine learning systems on so-called
spurious patterns, the term lacks coherent meaning in standard statistical
frameworks. However, the language of causality offers clarity: spurious
associations are due to confounding (e.g., a common cause), but not direct or
indirect causal effects. In this paper, we focus on natural language
processing, introducing methods and resources for training models less
sensitive to spurious patterns. Given documents and their initial labels, we
task humans with revising each document so that it (i) accords with a
counterfactual target label; (ii) retains internal coherence; and (iii) avoids
unnecessary changes. Interestingly, on sentiment analysis and natural language
inference tasks, classifiers trained on original data fail on their
counterfactually-revised counterparts and vice versa. Classifiers trained on
combined datasets perform remarkably well, just shy of those specialized to
either domain. While classifiers trained on either original or manipulated data
alone are sensitive to spurious features (e.g., mentions of genre), models
trained on the combined data are less sensitive to this signal. Both datasets
are publicly available.
| 2,020 | Computation and Language |
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