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Global Encoding for Abstractive Summarization | In neural abstractive summarization, the conventional sequence-to-sequence
(seq2seq) model often suffers from repetition and semantic irrelevance. To
tackle the problem, we propose a global encoding framework, which controls the
information flow from the encoder to the decoder based on the global
information of the source context. It consists of a convolutional gated unit to
perform global encoding to improve the representations of the source-side
information. Evaluations on the LCSTS and the English Gigaword both demonstrate
that our model outperforms the baseline models, and the analysis shows that our
model is capable of reducing repetition.
| 2,018 | Computation and Language |
Automatic Estimation of Simultaneous Interpreter Performance | Simultaneous interpretation, translation of the spoken word in real-time, is
both highly challenging and physically demanding. Methods to predict
interpreter confidence and the adequacy of the interpreted message have a
number of potential applications, such as in computer-assisted interpretation
interfaces or pedagogical tools. We propose the task of predicting simultaneous
interpreter performance by building on existing methodology for quality
estimation (QE) of machine translation output. In experiments over five
settings in three language pairs, we extend a QE pipeline to estimate
interpreter performance (as approximated by the METEOR evaluation metric) and
propose novel features reflecting interpretation strategy and evaluation
measures that further improve prediction accuracy.
| 2,018 | Computation and Language |
From Word to Sense Embeddings: A Survey on Vector Representations of
Meaning | Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.
| 2,018 | Computation and Language |
Regularizing Output Distribution of Abstractive Chinese Social Media
Text Summarization for Improved Semantic Consistency | Abstractive text summarization is a highly difficult problem, and the
sequence-to-sequence model has shown success in improving the performance on
the task. However, the generated summaries are often inconsistent with the
source content in semantics. In such cases, when generating summaries, the
model selects semantically unrelated words with respect to the source content
as the most probable output. The problem can be attributed to heuristically
constructed training data, where summaries can be unrelated to the source
content, thus containing semantically unrelated words and spurious word
correspondence. In this paper, we propose a regularization approach for the
sequence-to-sequence model and make use of what the model has learned to
regularize the learning objective to alleviate the effect of the problem. In
addition, we propose a practical human evaluation method to address the problem
that the existing automatic evaluation method does not evaluate the semantic
consistency with the source content properly. Experimental results demonstrate
the effectiveness of the proposed approach, which outperforms almost all the
existing models. Especially, the proposed approach improves the semantic
consistency by 4\% in terms of human evaluation.
| 2,018 | Computation and Language |
End-to-End Reinforcement Learning for Automatic Taxonomy Induction | We present a novel end-to-end reinforcement learning approach to automatic
taxonomy induction from a set of terms. While prior methods treat the problem
as a two-phase task (i.e., detecting hypernymy pairs followed by organizing
these pairs into a tree-structured hierarchy), we argue that such two-phase
methods may suffer from error propagation, and cannot effectively optimize
metrics that capture the holistic structure of a taxonomy. In our approach, the
representations of term pairs are learned using multiple sources of information
and used to determine \textit{which} term to select and \textit{where} to place
it on the taxonomy via a policy network. All components are trained in an
end-to-end manner with cumulative rewards, measured by a holistic tree metric
over the training taxonomies. Experiments on two public datasets of different
domains show that our approach outperforms prior state-of-the-art taxonomy
induction methods up to 19.6\% on ancestor F1.
| 2,018 | Computation and Language |
Joint Embedding of Words and Labels for Text Classification | Word embeddings are effective intermediate representations for capturing
semantic regularities between words, when learning the representations of text
sequences. We propose to view text classification as a label-word joint
embedding problem: each label is embedded in the same space with the word
vectors. We introduce an attention framework that measures the compatibility of
embeddings between text sequences and labels. The attention is learned on a
training set of labeled samples to ensure that, given a text sequence, the
relevant words are weighted higher than the irrelevant ones. Our method
maintains the interpretability of word embeddings, and enjoys a built-in
ability to leverage alternative sources of information, in addition to input
text sequences. Extensive results on the several large text datasets show that
the proposed framework outperforms the state-of-the-art methods by a large
margin, in terms of both accuracy and speed.
| 2,018 | Computation and Language |
Deep Neural Machine Translation with Weakly-Recurrent Units | Recurrent neural networks (RNNs) have represented for years the state of the
art in neural machine translation. Recently, new architectures have been
proposed, which can leverage parallel computation on GPUs better than classical
RNNs. Faster training and inference combined with different
sequence-to-sequence modeling also lead to performance improvements. While the
new models completely depart from the original recurrent architecture, we
decided to investigate how to make RNNs more efficient. In this work, we
propose a new recurrent NMT architecture, called Simple Recurrent NMT, built on
a class of fast and weakly-recurrent units that use layer normalization and
multiple attentions. Our experiments on the WMT14 English-to-German and WMT16
English-Romanian benchmarks show that our model represents a valid alternative
to LSTMs, as it can achieve better results at a significantly lower
computational cost.
| 2,018 | Computation and Language |
Behavior Analysis of NLI Models: Uncovering the Influence of Three
Factors on Robustness | Natural Language Inference is a challenging task that has received
substantial attention, and state-of-the-art models now achieve impressive test
set performance in the form of accuracy scores. Here, we go beyond this single
evaluation metric to examine robustness to semantically-valid alterations to
the input data. We identify three factors - insensitivity, polarity and unseen
pairs - and compare their impact on three SNLI models under a variety of
conditions. Our results demonstrate a number of strengths and weaknesses in the
models' ability to generalise to new in-domain instances. In particular, while
strong performance is possible on unseen hypernyms, unseen antonyms are more
challenging for all the models. More generally, the models suffer from an
insensitivity to certain small but semantically significant alterations, and
are also often influenced by simple statistical correlations between words and
training labels. Overall, we show that evaluations of NLI models can benefit
from studying the influence of factors intrinsic to the models or found in the
dataset used.
| 2,018 | Computation and Language |
Deep RNNs Encode Soft Hierarchical Syntax | We present a set of experiments to demonstrate that deep recurrent neural
networks (RNNs) learn internal representations that capture soft hierarchical
notions of syntax from highly varied supervision. We consider four syntax tasks
at different depths of the parse tree; for each word, we predict its part of
speech as well as the first (parent), second (grandparent) and third level
(great-grandparent) constituent labels that appear above it. These predictions
are made from representations produced at different depths in networks that are
pretrained with one of four objectives: dependency parsing, semantic role
labeling, machine translation, or language modeling. In every case, we find a
correspondence between network depth and syntactic depth, suggesting that a
soft syntactic hierarchy emerges. This effect is robust across all conditions,
indicating that the models encode significant amounts of syntax even in the
absence of an explicit syntactic training supervision.
| 2,018 | Computation and Language |
Neural Machine Translation for Bilingually Scarce Scenarios: A Deep
Multi-task Learning Approach | Neural machine translation requires large amounts of parallel training text
to learn a reasonable-quality translation model. This is particularly
inconvenient for language pairs for which enough parallel text is not
available. In this paper, we use monolingual linguistic resources in the source
side to address this challenging problem based on a multi-task learning
approach. More specifically, we scaffold the machine translation task on
auxiliary tasks including semantic parsing, syntactic parsing, and named-entity
recognition. This effectively injects semantic and/or syntactic knowledge into
the translation model, which would otherwise require a large amount of training
bitext. We empirically evaluate and show the effectiveness of our multi-task
learning approach on three translation tasks: English-to-French,
English-to-Farsi, and English-to-Vietnamese.
| 2,018 | Computation and Language |
State Gradients for RNN Memory Analysis | We present a framework for analyzing what the state in RNNs remembers from
its input embeddings. Our approach is inspired by backpropagation, in the sense
that we compute the gradients of the states with respect to the input
embeddings. The gradient matrix is decomposed with Singular Value Decomposition
to analyze which directions in the embedding space are best transferred to the
hidden state space, characterized by the largest singular values. We apply our
approach to LSTM language models and investigate to what extent and for how
long certain classes of words are remembered on average for a certain corpus.
Additionally, the extent to which a specific property or relationship is
remembered by the RNN can be tracked by comparing a vector characterizing that
property with the direction(s) in embedding space that are best preserved in
hidden state space.
| 2,018 | Computation and Language |
Neural Open Information Extraction | Conventional Open Information Extraction (Open IE) systems are usually built
on hand-crafted patterns from other NLP tools such as syntactic parsing, yet
they face problems of error propagation. In this paper, we propose a neural
Open IE approach with an encoder-decoder framework. Distinct from existing
methods, the neural Open IE approach learns highly confident arguments and
relation tuples bootstrapped from a state-of-the-art Open IE system. An
empirical study on a large benchmark dataset shows that the neural Open IE
system significantly outperforms several baselines, while maintaining
comparable computational efficiency.
| 2,018 | Computation and Language |
The risk of sub-optimal use of Open Source NLP Software: UKB is
inadvertently state-of-the-art in knowledge-based WSD | UKB is an open source collection of programs for performing, among other
tasks, knowledge-based Word Sense Disambiguation (WSD). Since it was released
in 2009 it has been often used out-of-the-box in sub-optimal settings. We show
that nine years later it is the state-of-the-art on knowledge-based WSD. This
case shows the pitfalls of releasing open source NLP software without optimal
default settings and precise instructions for reproducibility.
| 2,018 | Computation and Language |
Decision problems for Clark-congruential languages | A common question when studying a class of context-free grammars is whether
equivalence is decidable within this class. We answer this question positively
for the class of Clark-congruential grammars, which are of interest to
grammatical inference. We also consider the problem of checking whether a given
CFG is Clark-congruential, and show that it is decidable given that the CFG is
a DCFG.
| 2,018 | Computation and Language |
Cross-lingual Document Retrieval using Regularized Wasserstein Distance | Many information retrieval algorithms rely on the notion of a good distance
that allows to efficiently compare objects of different nature. Recently, a new
promising metric called Word Mover's Distance was proposed to measure the
divergence between text passages. In this paper, we demonstrate that this
metric can be extended to incorporate term-weighting schemes and provide more
accurate and computationally efficient matching between documents using
entropic regularization. We evaluate the benefits of both extensions in the
task of cross-lingual document retrieval (CLDR). Our experimental results on
eight CLDR problems suggest that the proposed methods achieve remarkable
improvements in terms of Mean Reciprocal Rank compared to several baselines.
| 2,018 | Computation and Language |
Bootstrapping Multilingual Intent Models via Machine Translation for
Dialog Automation | With the resurgence of chat-based dialog systems in consumer and enterprise
applications, there has been much success in developing data-driven and
rule-based natural language models to understand human intent. Since these
models require large amounts of data and in-domain knowledge, expanding an
equivalent service into new markets is disrupted by language barriers that
inhibit dialog automation.
This paper presents a user study to evaluate the utility of out-of-the-box
machine translation technology to (1) rapidly bootstrap multilingual spoken
dialog systems and (2) enable existing human analysts to understand foreign
language utterances. We additionally evaluate the utility of machine
translation in human assisted environments, where a portion of the traffic is
processed by analysts. In English->Spanish experiments, we observe a high
potential for dialog automation, as well as the potential for human analysts to
process foreign language utterances with high accuracy.
| 2,018 | Computation and Language |
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems | Automatic machine learning systems can inadvertently accentuate and
perpetuate inappropriate human biases. Past work on examining inappropriate
biases has largely focused on just individual systems. Further, there is no
benchmark dataset for examining inappropriate biases in systems. Here for the
first time, we present the Equity Evaluation Corpus (EEC), which consists of
8,640 English sentences carefully chosen to tease out biases towards certain
races and genders. We use the dataset to examine 219 automatic sentiment
analysis systems that took part in a recent shared task, SemEval-2018 Task 1
'Affect in Tweets'. We find that several of the systems show statistically
significant bias; that is, they consistently provide slightly higher sentiment
intensity predictions for one race or one gender. We make the EEC freely
available.
| 2,018 | Computation and Language |
Sentiment Composition of Words with Opposing Polarities | In this paper, we explore sentiment composition in phrases that have at least
one positive and at least one negative word---phrases like 'happy accident' and
'best winter break'. We compiled a dataset of such opposing polarity phrases
and manually annotated them with real-valued scores of sentiment association.
Using this dataset, we analyze the linguistic patterns present in opposing
polarity phrases. Finally, we apply several unsupervised and supervised
techniques of sentiment composition to determine their efficacy on this
dataset. Our best system, which incorporates information from the phrase's
constituents, their parts of speech, their sentiment association scores, and
their embedding vectors, obtains an accuracy of over 80% on the opposing
polarity phrases.
| 2,018 | Computation and Language |
NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse
Drug Reactions and Medication Intake | Our team, NRC-Canada, participated in two shared tasks at the AMIA-2017
Workshop on Social Media Mining for Health Applications (SMM4H): Task 1 -
classification of tweets mentioning adverse drug reactions, and Task 2 -
classification of tweets describing personal medication intake. For both tasks,
we trained Support Vector Machine classifiers using a variety of surface-form,
sentiment, and domain-specific features. With nine teams participating in each
task, our submissions ranked first on Task 1 and third on Task 2. Handling
considerable class imbalance proved crucial for Task 1. We applied an
under-sampling technique to reduce class imbalance (from about 1:10 to 1:2).
Standard n-gram features, n-grams generalized over domain terms, as well as
general-domain and domain-specific word embeddings had a substantial impact on
the overall performance in both tasks. On the other hand, including sentiment
lexicon features did not result in any improvement.
| 2,018 | Computation and Language |
Neural Factor Graph Models for Cross-lingual Morphological Tagging | Morphological analysis involves predicting the syntactic traits of a word
(e.g. {POS: Noun, Case: Acc, Gender: Fem}). Previous work in morphological
tagging improves performance for low-resource languages (LRLs) through
cross-lingual training with a high-resource language (HRL) from the same
family, but is limited by the strict, often false, assumption that tag sets
exactly overlap between the HRL and LRL. In this paper we propose a method for
cross-lingual morphological tagging that aims to improve information sharing
between languages by relaxing this assumption. The proposed model uses
factorial conditional random fields with neural network potentials, making it
possible to (1) utilize the expressive power of neural network representations
to smooth over superficial differences in the surface forms, (2) model pairwise
and transitive relationships between tags, and (3) accurately generate tag sets
that are unseen or rare in the training data. Experiments on four languages
from the Universal Dependencies Treebank demonstrate superior tagging
accuracies over existing cross-lingual approaches.
| 2,018 | Computation and Language |
Domain Adapted Word Embeddings for Improved Sentiment Classification | Generic word embeddings are trained on large-scale generic corpora; Domain
Specific (DS) word embeddings are trained only on data from a domain of
interest. This paper proposes a method to combine the breadth of generic
embeddings with the specificity of domain specific embeddings. The resulting
embeddings, called Domain Adapted (DA) word embeddings, are formed by aligning
corresponding word vectors using Canonical Correlation Analysis (CCA) or the
related nonlinear Kernel CCA. Evaluation results on sentiment classification
tasks show that the DA embeddings substantially outperform both generic and DS
embeddings when used as input features to standard or state-of-the-art sentence
encoding algorithms for classification.
| 2,018 | Computation and Language |
Using Statistical and Semantic Models for Multi-Document Summarization | We report a series of experiments with different semantic models on top of
various statistical models for extractive text summarization. Though
statistical models may better capture word co-occurrences and distribution
around the text, they fail to detect the context and the sense of sentences
/words as a whole. Semantic models help us gain better insight into the context
of sentences. We show that how tuning weights between different models can help
us achieve significant results on various benchmarks. Learning pre-trained
vectors used in semantic models further, on given corpus, can give addition
spike in performance. Using weighing techniques in between different
statistical models too further refines our result. For Statistical models, we
have used TF/IDF, TextRAnk, Jaccard/Cosine Similarities. For Semantic Models,
we have used WordNet-based Model and proposed two models based on Glove Vectors
and Facebook's InferSent. We tested our approach on DUC 2004 dataset,
generating 100-word summaries. We have discussed the system, algorithms,
analysis and also proposed and tested possible improvements. ROUGE scores were
used to compare to other summarizers.
| 2,018 | Computation and Language |
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction | One key task of fine-grained sentiment analysis of product reviews is to
extract product aspects or features that users have expressed opinions on. This
paper focuses on supervised aspect extraction using deep learning. Unlike other
highly sophisticated supervised deep learning models, this paper proposes a
novel and yet simple CNN model employing two types of pre-trained embeddings
for aspect extraction: general-purpose embeddings and domain-specific
embeddings. Without using any additional supervision, this model achieves
surprisingly good results, outperforming state-of-the-art sophisticated
existing methods. To our knowledge, this paper is the first to report such
double embeddings based CNN model for aspect extraction and achieve very good
results.
| 2,018 | Computation and Language |
Confidence Modeling for Neural Semantic Parsing | In this work we focus on confidence modeling for neural semantic parsers
which are built upon sequence-to-sequence models. We outline three major causes
of uncertainty, and design various metrics to quantify these factors. These
metrics are then used to estimate confidence scores that indicate whether model
predictions are likely to be correct. Beyond confidence estimation, we identify
which parts of the input contribute to uncertain predictions allowing users to
interpret their model, and verify or refine its input. Experimental results
show that our confidence model significantly outperforms a widely used method
that relies on posterior probability, and improves the quality of
interpretation compared to simply relying on attention scores.
| 2,018 | Computation and Language |
TutorialBank: A Manually-Collected Corpus for Prerequisite Chains,
Survey Extraction and Resource Recommendation | The field of Natural Language Processing (NLP) is growing rapidly, with new
research published daily along with an abundance of tutorials, codebases and
other online resources. In order to learn this dynamic field or stay up-to-date
on the latest research, students as well as educators and researchers must
constantly sift through multiple sources to find valuable, relevant
information. To address this situation, we introduce TutorialBank, a new,
publicly available dataset which aims to facilitate NLP education and research.
We have manually collected and categorized over 6,300 resources on NLP as well
as the related fields of Artificial Intelligence (AI), Machine Learning (ML)
and Information Retrieval (IR). Our dataset is notably the largest
manually-picked corpus of resources intended for NLP education which does not
include only academic papers. Additionally, we have created both a search
engine and a command-line tool for the resources and have annotated the corpus
to include lists of research topics, relevant resources for each topic,
prerequisite relations among topics, relevant sub-parts of individual
resources, among other annotations. We are releasing the dataset and present
several avenues for further research.
| 2,018 | Computation and Language |
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context | We know very little about how neural language models (LM) use prior
linguistic context. In this paper, we investigate the role of context in an
LSTM LM, through ablation studies. Specifically, we analyze the increase in
perplexity when prior context words are shuffled, replaced, or dropped. On two
standard datasets, Penn Treebank and WikiText-2, we find that the model is
capable of using about 200 tokens of context on average, but sharply
distinguishes nearby context (recent 50 tokens) from the distant history. The
model is highly sensitive to the order of words within the most recent
sentence, but ignores word order in the long-range context (beyond 50 tokens),
suggesting the distant past is modeled only as a rough semantic field or topic.
We further find that the neural caching model (Grave et al., 2017b) especially
helps the LSTM to copy words from within this distant context. Overall, our
analysis not only provides a better understanding of how neural LMs use their
context, but also sheds light on recent success from cache-based models.
| 2,018 | Computation and Language |
Learning to Ask Good Questions: Ranking Clarification Questions using
Neural Expected Value of Perfect Information | Inquiry is fundamental to communication, and machines cannot effectively
collaborate with humans unless they can ask questions. In this work, we build a
neural network model for the task of ranking clarification questions. Our model
is inspired by the idea of expected value of perfect information: a good
question is one whose expected answer will be useful. We study this problem
using data from StackExchange, a plentiful online resource in which people
routinely ask clarifying questions to posts so that they can better offer
assistance to the original poster. We create a dataset of clarification
questions consisting of ~77K posts paired with a clarification question (and
answer) from three domains of StackExchange: askubuntu, unix and superuser. We
evaluate our model on 500 samples of this dataset against expert human
judgments and demonstrate significant improvements over controlled baselines.
| 2,018 | Computation and Language |
Backpropagating through Structured Argmax using a SPIGOT | We introduce the structured projection of intermediate gradients optimization
technique (SPIGOT), a new method for backpropagating through neural networks
that include hard-decision structured predictions (e.g., parsing) in
intermediate layers. SPIGOT requires no marginal inference, unlike structured
attention networks (Kim et al., 2017) and some reinforcement learning-inspired
solutions (Yogatama et al., 2017). Like so-called straight-through estimators
(Hinton, 2012), SPIGOT defines gradient-like quantities associated with
intermediate nondifferentiable operations, allowing backpropagation before and
after them; SPIGOT's proxy aims to ensure that, after a parameter update, the
intermediate structure will remain well-formed.
We experiment on two structured NLP pipelines: syntactic-then-semantic
dependency parsing, and semantic parsing followed by sentiment classification.
We show that training with SPIGOT leads to a larger improvement on the
downstream task than a modularly-trained pipeline, the straight-through
estimator, and structured attention, reaching a new state of the art on
semantic dependency parsing.
| 2,018 | Computation and Language |
Examining a hate speech corpus for hate speech detection and popularity
prediction | As research on hate speech becomes more and more relevant every day, most of
it is still focused on hate speech detection. By attempting to replicate a hate
speech detection experiment performed on an existing Twitter corpus annotated
for hate speech, we highlight some issues that arise from doing research in the
field of hate speech, which is essentially still in its infancy. We take a
critical look at the training corpus in order to understand its biases, while
also using it to venture beyond hate speech detection and investigate whether
it can be used to shed light on other facets of research, such as popularity of
hate tweets.
| 2,018 | Computation and Language |
AdvEntuRe: Adversarial Training for Textual Entailment with
Knowledge-Guided Examples | We consider the problem of learning textual entailment models with limited
supervision (5K-10K training examples), and present two complementary
approaches for it. First, we propose knowledge-guided adversarial example
generators for incorporating large lexical resources in entailment models via
only a handful of rule templates. Second, to make the entailment model - a
discriminator - more robust, we propose the first GAN-style approach for
training it using a natural language example generator that iteratively adjusts
based on the discriminator's performance. We demonstrate effectiveness using
two entailment datasets, where the proposed methods increase accuracy by 4.7%
on SciTail and by 2.8% on a 1% training sub-sample of SNLI. Notably, even a
single hand-written rule, negate, improves the accuracy on the negation
examples in SNLI by 6.1%.
| 2,018 | Computation and Language |
Huge Automatically Extracted Training Sets for Multilingual Word Sense
Disambiguation | We release to the community six large-scale sense-annotated datasets in
multiple language to pave the way for supervised multilingual Word Sense
Disambiguation. Our datasets cover all the nouns in the English WordNet and
their translations in other languages for a total of millions of sense-tagged
sentences. Experiments prove that these corpora can be effectively used as
training sets for supervised WSD systems, surpassing the state of the art for
low-resourced languages and providing competitive results for English, where
manually annotated training sets are accessible. The data is available at
trainomatic.org.
| 2,018 | Computation and Language |
Gaussian Mixture Latent Vector Grammars | We introduce Latent Vector Grammars (LVeGs), a new framework that extends
latent variable grammars such that each nonterminal symbol is associated with a
continuous vector space representing the set of (infinitely many) subtypes of
the nonterminal. We show that previous models such as latent variable grammars
and compositional vector grammars can be interpreted as special cases of LVeGs.
We then present Gaussian Mixture LVeGs (GM-LVeGs), a new special case of LVeGs
that uses Gaussian mixtures to formulate the weights of production rules over
subtypes of nonterminals. A major advantage of using Gaussian mixtures is that
the partition function and the expectations of subtype rules can be computed
using an extension of the inside-outside algorithm, which enables efficient
inference and learning. We apply GM-LVeGs to part-of-speech tagging and
constituency parsing and show that GM-LVeGs can achieve competitive accuracies.
Our code is available at https://github.com/zhaoyanpeng/lveg.
| 2,018 | Computation and Language |
TED-LIUM 3: twice as much data and corpus repartition for experiments on
speaker adaptation | In this paper, we present TED-LIUM release 3 corpus dedicated to speech
recognition in English, that multiplies by more than two the available data to
train acoustic models in comparison with TED-LIUM 2. We present the recent
development on Automatic Speech Recognition (ASR) systems in comparison with
the two previous releases of the TED-LIUM Corpus from 2012 and 2014. We
demonstrate that, passing from 207 to 452 hours of transcribed speech training
data is really more useful for end-to-end ASR systems than for HMM-based
state-of-the-art ones, even if the HMM-based ASR system still outperforms
end-to-end ASR system when the size of audio training data is 452 hours, with
respectively a Word Error Rate (WER) of 6.6% and 13.7%. Last, we propose two
repartitions of the TED-LIUM release 3 corpus: the legacy one that is the same
as the one existing in release 2, and a new one, calibrated and designed to
make experiments on speaker adaptation. Like the two first releases, TED-LIUM 3
corpus will be freely available for the research community.
| 2,018 | Computation and Language |
Unsupervised Semantic Frame Induction using Triclustering | We use dependency triples automatically extracted from a Web-scale corpus to
perform unsupervised semantic frame induction. We cast the frame induction
problem as a triclustering problem that is a generalization of clustering for
triadic data. Our replicable benchmarks demonstrate that the proposed
graph-based approach, Triframes, shows state-of-the art results on this task on
a FrameNet-derived dataset and performing on par with competitive methods on a
verb class clustering task.
| 2,019 | Computation and Language |
Jointly Predicting Predicates and Arguments in Neural Semantic Role
Labeling | Recent BIO-tagging-based neural semantic role labeling models are very high
performing, but assume gold predicates as part of the input and cannot
incorporate span-level features. We propose an end-to-end approach for jointly
predicting all predicates, arguments spans, and the relations between them. The
model makes independent decisions about what relationship, if any, holds
between every possible word-span pair, and learns contextualized span
representations that provide rich, shared input features for each decision.
Experiments demonstrate that this approach sets a new state of the art on
PropBank SRL without gold predicates.
| 2,018 | Computation and Language |
Coarse-to-Fine Decoding for Neural Semantic Parsing | Semantic parsing aims at mapping natural language utterances into structured
meaning representations. In this work, we propose a structure-aware neural
architecture which decomposes the semantic parsing process into two stages.
Given an input utterance, we first generate a rough sketch of its meaning,
where low-level information (such as variable names and arguments) is glossed
over. Then, we fill in missing details by taking into account the natural
language input and the sketch itself. Experimental results on four datasets
characteristic of different domains and meaning representations show that our
approach consistently improves performance, achieving competitive results
despite the use of relatively simple decoders.
| 2,018 | Computation and Language |
Zero-Shot Dialog Generation with Cross-Domain Latent Actions | This paper introduces zero-shot dialog generation (ZSDG), as a step towards
neural dialog systems that can instantly generalize to new situations with
minimal data. ZSDG enables an end-to-end generative dialog system to generalize
to a new domain for which only a domain description is provided and no training
dialogs are available. Then a novel learning framework, Action Matching, is
proposed. This algorithm can learn a cross-domain embedding space that models
the semantics of dialog responses which, in turn, lets a neural dialog
generation model generalize to new domains. We evaluate our methods on a new
synthetic dialog dataset, and an existing human-human dialog dataset. Results
show that our method has superior performance in learning dialog models that
rapidly adapt their behavior to new domains and suggests promising future
research.
| 2,018 | Computation and Language |
Triangular Architecture for Rare Language Translation | Neural Machine Translation (NMT) performs poor on the low-resource language
pair $(X,Z)$, especially when $Z$ is a rare language. By introducing another
rich language $Y$, we propose a novel triangular training architecture (TA-NMT)
to leverage bilingual data $(Y,Z)$ (may be small) and $(X,Y)$ (can be rich) to
improve the translation performance of low-resource pairs. In this triangular
architecture, $Z$ is taken as the intermediate latent variable, and translation
models of $Z$ are jointly optimized with a unified bidirectional EM algorithm
under the goal of maximizing the translation likelihood of $(X,Y)$. Empirical
results demonstrate that our method significantly improves the translation
quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even
better performance combining back-translation methods.
| 2,018 | Computation and Language |
An attention-based Bi-GRU-CapsNet model for hypernymy detection between
compound entities | Named entities are usually composable and extensible. Typical examples are
names of symptoms and diseases in medical areas. To distinguish these entities
from general entities, we name them \textit{compound entities}. In this paper,
we present an attention-based Bi-GRU-CapsNet model to detect hypernymy
relationship between compound entities. Our model consists of several important
components. To avoid the out-of-vocabulary problem, English words or Chinese
characters in compound entities are fed into the bidirectional gated recurrent
units. An attention mechanism is designed to focus on the differences between
the two compound entities. Since there are some different cases in hypernymy
relationship between compound entities, capsule network is finally employed to
decide whether the hypernymy relationship exists or not. Experimental results
demonstrate
| 2,018 | Computation and Language |
Hierarchical Neural Story Generation | We explore story generation: creative systems that can build coherent and
fluent passages of text about a topic. We collect a large dataset of 300K
human-written stories paired with writing prompts from an online forum. Our
dataset enables hierarchical story generation, where the model first generates
a premise, and then transforms it into a passage of text. We gain further
improvements with a novel form of model fusion that improves the relevance of
the story to the prompt, and adding a new gated multi-scale self-attention
mechanism to model long-range context. Experiments show large improvements over
strong baselines on both automated and human evaluations. Human judges prefer
stories generated by our approach to those from a strong non-hierarchical model
by a factor of two to one.
| 2,018 | Computation and Language |
Building Language Models for Text with Named Entities | Text in many domains involves a significant amount of named entities.
Predict- ing the entity names is often challenging for a language model as they
appear less frequent on the training corpus. In this paper, we propose a novel
and effective approach to building a discriminative language model which can
learn the entity names by leveraging their entity type information. We also
introduce two benchmark datasets based on recipes and Java programming codes,
on which we evalu- ate the proposed model. Experimental re- sults show that our
model achieves 52.2% better perplexity in recipe generation and 22.06% on code
generation than the state-of-the-art language models.
| 2,018 | Computation and Language |
Learning to Ask Questions in Open-domain Conversational Systems with
Typed Decoders | Asking good questions in large-scale, open-domain conversational systems is
quite significant yet rather untouched. This task, substantially different from
traditional question generation, requires to question not only with various
patterns but also on diverse and relevant topics. We observe that a good
question is a natural composition of {\it interrogatives}, {\it topic words},
and {\it ordinary words}. Interrogatives lexicalize the pattern of questioning,
topic words address the key information for topic transition in dialogue, and
ordinary words play syntactical and grammatical roles in making a natural
sentence. We devise two typed decoders (\textit{soft typed decoder} and
\textit{hard typed decoder}) in which a type distribution over the three types
is estimated and used to modulate the final generation distribution. Extensive
experiments show that the typed decoders outperform state-of-the-art baselines
and can generate more meaningful questions.
| 2,018 | Computation and Language |
Autoencoder as Assistant Supervisor: Improving Text Representation for
Chinese Social Media Text Summarization | Most of the current abstractive text summarization models are based on the
sequence-to-sequence model (Seq2Seq). The source content of social media is
long and noisy, so it is difficult for Seq2Seq to learn an accurate semantic
representation. Compared with the source content, the annotated summary is
short and well written. Moreover, it shares the same meaning as the source
content. In this work, we supervise the learning of the representation of the
source content with that of the summary. In implementation, we regard a summary
autoencoder as an assistant supervisor of Seq2Seq. Following previous work, we
evaluate our model on a popular Chinese social media dataset. Experimental
results show that our model achieves the state-of-the-art performances on the
benchmark dataset.
| 2,018 | Computation and Language |
Bag-of-Words as Target for Neural Machine Translation | A sentence can be translated into more than one correct sentences. However,
most of the existing neural machine translation models only use one of the
correct translations as the targets, and the other correct sentences are
punished as the incorrect sentences in the training stage. Since most of the
correct translations for one sentence share the similar bag-of-words, it is
possible to distinguish the correct translations from the incorrect ones by the
bag-of-words. In this paper, we propose an approach that uses both the
sentences and the bag-of-words as targets in the training stage, in order to
encourage the model to generate the potentially correct sentences that are not
appeared in the training set. We evaluate our model on a Chinese-English
translation dataset, and experiments show our model outperforms the strong
baselines by the BLEU score of 4.55.
| 2,018 | Computation and Language |
UnibucKernel Reloaded: First Place in Arabic Dialect Identification for
the Second Year in a Row | We present a machine learning approach that ranked on the first place in the
Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial
Evaluation Campaign. The proposed approach combines several kernels using
multiple kernel learning. While most of our kernels are based on character
p-grams (also known as n-grams) extracted from speech or phonetic transcripts,
we also use a kernel based on dialectal embeddings generated from audio
recordings by the organizers. In the learning stage, we independently employ
Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR).
Preliminary experiments indicate that KRR provides better classification
results. Our approach is shallow and simple, but the empirical results obtained
in the 2018 ADI Closed Shared Task prove that it achieves the best performance.
Furthermore, our top macro-F1 score (58.92%) is significantly better than the
second best score (57.59%) in the 2018 ADI Shared Task, according to the
statistical significance test performed by the organizers. Nevertheless, we
obtain even better post-competition results (a macro-F1 score of 62.28%) using
the audio embeddings released by the organizers after the competition. With a
very similar approach (that did not include phonetic features), we also ranked
first in the ADI Closed Shared Tasks of the 2017 VarDial Evaluation Campaign,
surpassing the second best method by 4.62%. We therefore conclude that our
multiple kernel learning method is the best approach to date for Arabic dialect
identification.
| 2,018 | Computation and Language |
Neural Coreference Resolution with Deep Biaffine Attention by Joint
Mention Detection and Mention Clustering | Coreference resolution aims to identify in a text all mentions that refer to
the same real-world entity. The state-of-the-art end-to-end neural coreference
model considers all text spans in a document as potential mentions and learns
to link an antecedent for each possible mention. In this paper, we propose to
improve the end-to-end coreference resolution system by (1) using a biaffine
attention model to get antecedent scores for each possible mention, and (2)
jointly optimizing the mention detection accuracy and the mention clustering
log-likelihood given the mention cluster labels. Our model achieves the
state-of-the-art performance on the CoNLL-2012 Shared Task English test set.
| 2,018 | Computation and Language |
Comprehensive Supersense Disambiguation of English Prepositions and
Possessives | Semantic relations are often signaled with prepositional or possessive
marking--but extreme polysemy bedevils their analysis and automatic
interpretation. We introduce a new annotation scheme, corpus, and task for the
disambiguation of prepositions and possessives in English. Unlike previous
approaches, our annotations are comprehensive with respect to types and tokens
of these markers; use broadly applicable supersense classes rather than
fine-grained dictionary definitions; unite prepositions and possessives under
the same class inventory; and distinguish between a marker's lexical
contribution and the role it marks in the context of a predicate or scene.
Strong interannotator agreement rates, as well as encouraging disambiguation
results with established supervised methods, speak to the viability of the
scheme and task.
| 2,018 | Computation and Language |
Word learning and the acquisition of syntactic--semantic overhypotheses | Children learning their first language face multiple problems of induction:
how to learn the meanings of words, and how to build meaningful phrases from
those words according to syntactic rules. We consider how children might solve
these problems efficiently by solving them jointly, via a computational model
that learns the syntax and semantics of multi-word utterances in a grounded
reference game. We select a well-studied empirical case in which children are
aware of patterns linking the syntactic and semantic properties of words ---
that the properties picked out by base nouns tend to be related to shape, while
prenominal adjectives tend to refer to other properties such as color. We show
that children applying such inductive biases are accurately reflecting the
statistics of child-directed speech, and that inducing similar biases in our
computational model captures children's behavior in a classic adjective
learning experiment. Our model incorporating such biases also demonstrates a
clear data efficiency in learning, relative to a baseline model that learns
without forming syntax-sensitive overhypotheses of word meaning. Thus solving a
more complex joint inference problem may make the full problem of language
acquisition easier, not harder.
| 2,018 | Computation and Language |
Discourse Coherence in the Wild: A Dataset, Evaluation and Methods | To date there has been very little work on assessing discourse coherence
methods on real-world data. To address this, we present a new corpus of
real-world texts (GCDC) as well as the first large-scale evaluation of leading
discourse coherence algorithms. We show that neural models, including two that
we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these
performance differences and discuss patterns we observed in low coherence texts
in four domains.
| 2,018 | Computation and Language |
Token-level and sequence-level loss smoothing for RNN language models | Despite the effectiveness of recurrent neural network language models, their
maximum likelihood estimation suffers from two limitations. It treats all
sentences that do not match the ground truth as equally poor, ignoring the
structure of the output space. Second, it suffers from "exposure bias": during
training tokens are predicted given ground-truth sequences, while at test time
prediction is conditioned on generated output sequences. To overcome these
limitations we build upon the recent reward augmented maximum likelihood
approach \ie sequence-level smoothing that encourages the model to predict
sentences close to the ground truth according to a given performance metric. We
extend this approach to token-level loss smoothing, and propose improvements to
the sequence-level smoothing approach. Our experiments on two different tasks,
image captioning and machine translation, show that token-level and
sequence-level loss smoothing are complementary, and significantly improve
results.
| 2,018 | Computation and Language |
Parser Training with Heterogeneous Treebanks | How to make the most of multiple heterogeneous treebanks when training a
monolingual dependency parser is an open question. We start by investigating
previously suggested, but little evaluated, strategies for exploiting multiple
treebanks based on concatenating training sets, with or without fine-tuning. We
go on to propose a new method based on treebank embeddings. We perform
experiments for several languages and show that in many cases fine-tuning and
treebank embeddings lead to substantial improvements over single treebanks or
concatenation, with average gains of 2.0--3.5 LAS points. We argue that
treebank embeddings should be preferred due to their conceptual simplicity,
flexibility and extensibility.
| 2,018 | Computation and Language |
The Spot the Difference corpus: a multi-modal corpus of spontaneous task
oriented spoken interactions | This paper describes the Spot the Difference Corpus which contains 54
interactions between pairs of subjects interacting to find differences in two
very similar scenes. The setup used, the participants' metadata and details
about collection are described. We are releasing this corpus of task-oriented
spontaneous dialogues. This release includes rich transcriptions, annotations,
audio and video. We believe that this dataset constitutes a valuable resource
to study several dimensions of human communication that go from turn-taking to
the study of referring expressions. In our preliminary analyses we have looked
at task success (how many differences were found out of the total number of
differences) and how it evolves over time. In addition we have looked at scene
complexity provided by the RGB components' entropy and how it could relate to
speech overlaps, interruptions and the expression of uncertainty. We found
there is a tendency that more complex scenes have more competitive
interruptions.
| 2,018 | Computation and Language |
Bianet: A Parallel News Corpus in Turkish, Kurdish and English | We present a new open-source parallel corpus consisting of news articles
collected from the Bianet magazine, an online newspaper that publishes Turkish
news, often along with their translations in English and Kurdish. In this
paper, we describe the collection process of the corpus and its statistical
properties. We validate the benefit of using the Bianet corpus by evaluating
bilingual and multilingual neural machine translation models in English-Turkish
and English-Kurdish directions.
| 2,018 | Computation and Language |
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement
Learning Approach | The goal of sentiment-to-sentiment "translation" is to change the underlying
sentiment of a sentence while keeping its content. The main challenge is the
lack of parallel data. To solve this problem, we propose a cycled reinforcement
learning method that enables training on unpaired data by collaboration between
a neutralization module and an emotionalization module. We evaluate our
approach on two review datasets, Yelp and Amazon. Experimental results show
that our approach significantly outperforms the state-of-the-art systems.
Especially, the proposed method substantially improves the content preservation
performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to
14.06 on the two datasets, respectively.
| 2,018 | Computation and Language |
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing | We propose an efficient dynamic oracle for training the 2-Planar
transition-based parser, a linear-time parser with over 99% coverage on
non-projective syntactic corpora. This novel approach outperforms the static
training strategy in the vast majority of languages tested and scored better on
most datasets than the arc-hybrid parser enhanced with the SWAP transition,
which can handle unrestricted non-projectivity.
| 2,018 | Computation and Language |
Effects of Word Embeddings on Neural Network-based Pitch Accent
Detection | Pitch accent detection often makes use of both acoustic and lexical features
based on the fact that pitch accents tend to correlate with certain words. In
this paper, we extend a pitch accent detector that involves a convolutional
neural network to include word embeddings, which are state-of-the-art vector
representations of words. We examine the effect these features have on
within-corpus and cross-corpus experiments on three English datasets. The
results show that while word embeddings can improve the performance in
corpus-dependent experiments, they also have the potential to make
generalization to unseen data more challenging.
| 2,018 | Computation and Language |
Unsupervised Abstractive Meeting Summarization with Multi-Sentence
Compression and Budgeted Submodular Maximization | We introduce a novel graph-based framework for abstractive meeting speech
summarization that is fully unsupervised and does not rely on any annotations.
Our work combines the strengths of multiple recent approaches while addressing
their weaknesses. Moreover, we leverage recent advances in word embeddings and
graph degeneracy applied to NLP to take exterior semantic knowledge into
account, and to design custom diversity and informativeness measures.
Experiments on the AMI and ICSI corpus show that our system improves on the
state-of-the-art. Code and data are publicly available, and our system can be
interactively tested.
| 2,018 | Computation and Language |
AMR Parsing as Graph Prediction with Latent Alignment | Abstract meaning representations (AMRs) are broad-coverage sentence-level
semantic representations. AMRs represent sentences as rooted labeled directed
acyclic graphs. AMR parsing is challenging partly due to the lack of annotated
alignments between nodes in the graphs and words in the corresponding
sentences. We introduce a neural parser which treats alignments as latent
variables within a joint probabilistic model of concepts, relations and
alignments. As exact inference requires marginalizing over alignments and is
infeasible, we use the variational auto-encoding framework and a continuous
relaxation of the discrete alignments. We show that joint modeling is
preferable to using a pipeline of align and parse. The parser achieves the best
reported results on the standard benchmark (74.4% on LDC2016E25).
| 2,018 | Computation and Language |
Conversations Gone Awry: Detecting Early Signs of Conversational Failure | One of the main challenges online social systems face is the prevalence of
antisocial behavior, such as harassment and personal attacks. In this work, we
introduce the task of predicting from the very start of a conversation whether
it will get out of hand. As opposed to detecting undesirable behavior after the
fact, this task aims to enable early, actionable prediction at a time when the
conversation might still be salvaged.
To this end, we develop a framework for capturing pragmatic devices---such as
politeness strategies and rhetorical prompts---used to start a conversation,
and analyze their relation to its future trajectory. Applying this framework in
a controlled setting, we demonstrate the feasibility of detecting early warning
signs of antisocial behavior in online discussions.
| 2,018 | Computation and Language |
NASH: Toward End-to-End Neural Architecture for Generative Semantic
Hashing | Semantic hashing has become a powerful paradigm for fast similarity search in
many information retrieval systems. While fairly successful, previous
techniques generally require two-stage training, and the binary constraints are
handled ad-hoc. In this paper, we present an end-to-end Neural Architecture for
Semantic Hashing (NASH), where the binary hashing codes are treated as
Bernoulli latent variables. A neural variational inference framework is
proposed for training, where gradients are directly back-propagated through the
discrete latent variable to optimize the hash function. We also draw
connections between proposed method and rate-distortion theory, which provides
a theoretical foundation for the effectiveness of the proposed framework.
Experimental results on three public datasets demonstrate that our method
significantly outperforms several state-of-the-art models on both unsupervised
and supervised scenarios.
| 2,018 | Computation and Language |
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library | This paper describes our winning contribution to SemEval 2018 Task 4:
Character Identification on Multiparty Dialogues. It is a simple, standard
model with one key innovation, an entity library. Our results show that this
innovation greatly facilitates the identification of infrequent characters.
Because of the generic nature of our model, this finding is potentially
relevant to any task that requires effective learning from sparse or unbalanced
data.
| 2,018 | Computation and Language |
Large-Scale QA-SRL Parsing | We present a new large-scale corpus of Question-Answer driven Semantic Role
Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our
corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for
over 64,000 sentences across 3 domains and was gathered with a new
crowd-sourcing scheme that we show has high precision and good recall at modest
cost. We also present neural models for two QA-SRL subtasks: detecting argument
spans for a predicate and generating questions to label the semantic
relationship. The best models achieve question accuracy of 82.6% and span-level
accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL
prediction task. They can also, as we show, be used to gather additional
annotations at low cost.
| 2,018 | Computation and Language |
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature
Vectors | Motivations like domain adaptation, transfer learning, and feature learning
have fueled interest in inducing embeddings for rare or unseen words, n-grams,
synsets, and other textual features. This paper introduces a la carte
embedding, a simple and general alternative to the usual word2vec-based
approaches for building such representations that is based upon recent
theoretical results for GloVe-like embeddings. Our method relies mainly on a
linear transformation that is efficiently learnable using pretrained word
vectors and linear regression. This transform is applicable on the fly in the
future when a new text feature or rare word is encountered, even if only a
single usage example is available. We introduce a new dataset showing how the a
la carte method requires fewer examples of words in context to learn
high-quality embeddings and we obtain state-of-the-art results on a nonce task
and some unsupervised document classification tasks.
| 2,018 | Computation and Language |
Did the Model Understand the Question? | We analyze state-of-the-art deep learning models for three tasks: question
answering on (1) images, (2) tables, and (3) passages of text. Using the notion
of \emph{attribution} (word importance), we find that these deep networks often
ignore important question terms. Leveraging such behavior, we perturb questions
to craft a variety of adversarial examples. Our strongest attacks drop the
accuracy of a visual question answering model from $61.1\%$ to $19\%$, and that
of a tabular question answering model from $33.5\%$ to $3.3\%$. Additionally,
we show how attributions can strengthen attacks proposed by Jia and Liang
(2017) on paragraph comprehension models. Our results demonstrate that
attributions can augment standard measures of accuracy and empower
investigation of model performance. When a model is accurate but for the wrong
reasons, attributions can surface erroneous logic in the model that indicates
inadequacies in the test data.
| 2,018 | Computation and Language |
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue
Systems | This paper presents a large-scale corpus for non-task-oriented dialogue
response selection, which contains over 27K distinct prompts more than 82K
responses collected from social media. To annotate this corpus, we define a
5-grade rating scheme: bad, mediocre, acceptable, good, and excellent,
according to the relevance, coherence, informativeness, interestingness, and
the potential to move a conversation forward. To test the validity and
usefulness of the produced corpus, we compare various unsupervised and
supervised models for response selection. Experimental results confirm that the
proposed corpus is helpful in training response selection models.
| 2,018 | Computation and Language |
Simplifying Sentences with Sequence to Sequence Models | We simplify sentences with an attentive neural network sequence to sequence
model, dubbed S4. The model includes a novel word-copy mechanism and loss
function to exploit linguistic similarities between the original and simplified
sentences. It also jointly uses pre-trained and fine-tuned word embeddings to
capture the semantics of complex sentences and to mitigate the effects of
limited data. When trained and evaluated on pairs of sentences from thousands
of news articles, we observe a 8.8 point improvement in BLEU score over a
sequence to sequence baseline; however, learning word substitutions remains
difficult. Such sequence to sequence models are promising for other text
generation tasks such as style transfer.
| 2,018 | Computation and Language |
Improved ASR for Under-Resourced Languages Through Multi-Task Learning
with Acoustic Landmarks | Furui first demonstrated that the identity of both consonant and vowel can be
perceived from the C-V transition; later, Stevens proposed that acoustic
landmarks are the primary cues for speech perception, and that steady-state
regions are secondary or supplemental. Acoustic landmarks are perceptually
salient, even in a language one doesn't speak, and it has been demonstrated
that non-speakers of the language can identify features such as the primary
articulator of the landmark. These factors suggest a strategy for developing
language-independent automatic speech recognition: landmarks can potentially be
learned once from a suitably labeled corpus and rapidly applied to many other
languages. This paper proposes enhancing the cross-lingual portability of a
neural network by using landmarks as the secondary task in multi-task learning
(MTL). The network is trained in a well-resourced source language with both
phone and landmark labels (English), then adapted to an under-resourced target
language with only word labels (Iban). Landmark-tasked MTL reduces
source-language phone error rate by 2.9% relative, and reduces target-language
word error rate by 1.9%-5.9% depending on the amount of target-language
training data. These results suggest that landmark-tasked MTL causes the DNN to
learn hidden-node features that are useful for cross-lingual adaptation.
| 2,018 | Computation and Language |
Unsupervised Learning of Style-sensitive Word Vectors | This paper presents the first study aimed at capturing stylistic similarity
between words in an unsupervised manner. We propose extending the continuous
bag of words (CBOW) model (Mikolov et al., 2013) to learn style-sensitive word
vectors using a wider context window under the assumption that the style of all
the words in an utterance is consistent. In addition, we introduce a novel task
to predict lexical stylistic similarity and to create a benchmark dataset for
this task. Our experiment with this dataset supports our assumption and
demonstrates that the proposed extensions contribute to the acquisition of
style-sensitive word embeddings.
| 2,018 | Computation and Language |
Marrying up Regular Expressions with Neural Networks: A Case Study for
Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the
number and quality of annotated data, but there is often a shortage of such
training data. In this paper, we ask the question: "Can we combine a neural
network (NN) with regular expressions (RE) to improve supervised learning for
NLP?". In answer, we develop novel methods to exploit the rich expressiveness
of REs at different levels within a NN, showing that the combination
significantly enhances the learning effectiveness when a small number of
training examples are available. We evaluate our approach by applying it to
spoken language understanding for intent detection and slot filling.
Experimental results show that our approach is highly effective in exploiting
the available training data, giving a clear boost to the RE-unaware NN.
| 2,018 | Computation and Language |
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular
Structure Information | We propose a novel neural method to extract drug-drug interactions (DDIs)
from texts using external drug molecular structure information. We encode
textual drug pairs with convolutional neural networks and their molecular pairs
with graph convolutional networks (GCNs), and then we concatenate the outputs
of these two networks. In the experiments, we show that GCNs can predict DDIs
from the molecular structures of drugs in high accuracy and the molecular
information can enhance text-based DDI extraction by 2.39 percent points in the
F-score on the DDIExtraction 2013 shared task data set.
| 2,018 | Computation and Language |
Generating Continuous Representations of Medical Texts | We present an architecture that generates medical texts while learning an
informative, continuous representation with discriminative features. During
training the input to the system is a dataset of captions for medical X-Rays.
The acquired continuous representations are of particular interest for use in
many machine learning techniques where the discrete and high-dimensional nature
of textual input is an obstacle. We use an Adversarially Regularized
Autoencoder to create realistic text in both an unconditional and conditional
setting. We show that this technique is applicable to medical texts which often
contain syntactic and domain-specific shorthands. A quantitative evaluation
shows that we achieve a lower model perplexity than a traditional LSTM
generator.
| 2,018 | Computation and Language |
Continuous Learning in a Hierarchical Multiscale Neural Network | We reformulate the problem of encoding a multi-scale representation of a
sequence in a language model by casting it in a continuous learning framework.
We propose a hierarchical multi-scale language model in which short time-scale
dependencies are encoded in the hidden state of a lower-level recurrent neural
network while longer time-scale dependencies are encoded in the dynamic of the
lower-level network by having a meta-learner update the weights of the
lower-level neural network in an online meta-learning fashion. We use elastic
weights consolidation as a higher-level to prevent catastrophic forgetting in
our continuous learning framework.
| 2,018 | Computation and Language |
CLINIQA: A Machine Intelligence Based Clinical Question Answering System | The recent developments in the field of biomedicine have made large volumes
of biomedical literature available to the medical practitioners. Due to the
large size and lack of efficient searching strategies, medical practitioners
struggle to obtain necessary information available in the biomedical
literature. Moreover, the most sophisticated search engines of age are not
intelligent enough to interpret the clinicians' questions. These facts reflect
the urgent need of an information retrieval system that accepts the queries
from medical practitioners' in natural language and returns the answers quickly
and efficiently. In this paper, we present an implementation of a machine
intelligence based CLINIcal Question Answering system (CLINIQA) to answer
medical practitioner's questions. The system was rigorously evaluated on
different text mining algorithms and the best components for the system were
selected. The system makes use of Unified Medical Language System for semantic
analysis of both questions and medical documents. In addition, the system
employs supervised machine learning algorithms for classification of the
documents, identifying the focus of the question and answer selection.
Effective domain-specific heuristics are designed for answer ranking. The
performance evaluation on hundred clinical questions shows the effectiveness of
our approach.
| 2,018 | Computation and Language |
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia | We study the task of generating from Wikipedia articles question-answer pairs
that cover content beyond a single sentence. We propose a neural network
approach that incorporates coreference knowledge via a novel gating mechanism.
Compared to models that only take into account sentence-level information
(Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the
linguistic knowledge introduced by the coreference representation aids question
generation significantly, producing models that outperform the current
state-of-the-art. We apply our system (composed of an answer span extraction
system and the passage-level QG system) to the 10,000 top-ranking Wikipedia
articles and create a corpus of over one million question-answer pairs. We also
provide a qualitative analysis for this large-scale generated corpus from
Wikipedia.
| 2,018 | Computation and Language |
Author Commitment and Social Power: Automatic Belief Tagging to Infer
the Social Context of Interactions | Understanding how social power structures affect the way we interact with one
another is of great interest to social scientists who want to answer
fundamental questions about human behavior, as well as to computer scientists
who want to build automatic methods to infer the social contexts of
interactions. In this paper, we employ advancements in extra-propositional
semantics extraction within NLP to study how author commitment reflects the
social context of an interaction. Specifically, we investigate whether the
level of commitment expressed by individuals in an organizational interaction
reflects the hierarchical power structures they are part of. We find that
subordinates use significantly more instances of non-commitment than superiors.
More importantly, we also find that subordinates attribute propositions to
other agents more often than superiors do --- an aspect that has not been
studied before. Finally, we show that enriching lexical features with
commitment labels captures important distinctions in social meanings.
| 2,018 | Computation and Language |
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines | Recurrent and convolutional neural networks comprise two distinct families of
models that have proven to be useful for encoding natural language utterances.
In this paper we present SoPa, a new model that aims to bridge these two
approaches. SoPa combines neural representation learning with weighted
finite-state automata (WFSAs) to learn a soft version of traditional surface
patterns. We show that SoPa is an extension of a one-layer CNN, and that such
CNNs are equivalent to a restricted version of SoPa, and accordingly, to a
restricted form of WFSA. Empirically, on three text classification tasks, SoPa
is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline,
and is particularly useful in small data settings.
| 2,018 | Computation and Language |
Paper Abstract Writing through Editing Mechanism | We present a paper abstract writing system based on an attentive neural
sequence-to-sequence model that can take a title as input and automatically
generate an abstract. We design a novel Writing-editing Network that can attend
to both the title and the previously generated abstract drafts and then
iteratively revise and polish the abstract. With two series of Turing tests,
where the human judges are asked to distinguish the system-generated abstracts
from human-written ones, our system passes Turing tests by junior domain
experts at a rate up to 30% and by non-expert at a rate up to 80%.
| 2,020 | Computation and Language |
Learning to Write with Cooperative Discriminators | Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models,
but when used to generate natural language their output tends to be overly
generic, repetitive, and self-contradictory. We postulate that the objective
function optimized by RNN language models, which amounts to the overall
perplexity of a text, is not expressive enough to capture the notion of
communicative goals described by linguistic principles such as Grice's Maxims.
We propose learning a mixture of multiple discriminative models that can be
used to complement the RNN generator and guide the decoding process. Human
evaluation demonstrates that text generated by our system is preferred over
that of baselines by a large margin and significantly enhances the overall
coherence, style, and information content of the generated text.
| 2,018 | Computation and Language |
What's in a Domain? Learning Domain-Robust Text Representations using
Adversarial Training | Most real world language problems require learning from heterogenous corpora,
raising the problem of learning robust models which generalise well to both
similar (in domain) and dissimilar (out of domain) instances to those seen in
training. This requires learning an underlying task, while not learning
irrelevant signals and biases specific to individual domains. We propose a
novel method to optimise both in- and out-of-domain accuracy based on joint
learning of a structured neural model with domain-specific and domain-general
components, coupled with adversarial training for domain. Evaluating on
multi-domain language identification and multi-domain sentiment analysis, we
show substantial improvements over standard domain adaptation techniques, and
domain-adversarial training.
| 2,018 | Computation and Language |
Towards Robust and Privacy-preserving Text Representations | Written text often provides sufficient clues to identify the author, their
gender, age, and other important attributes. Consequently, the authorship of
training and evaluation corpora can have unforeseen impacts, including
differing model performance for different user groups, as well as privacy
implications. In this paper, we propose an approach to explicitly obscure
important author characteristics at training time, such that representations
learned are invariant to these attributes. Evaluating on two tasks, we show
that this leads to increased privacy in the learned representations, as well as
more robust models to varying evaluation conditions, including out-of-domain
corpora.
| 2,018 | Computation and Language |
Narrative Modeling with Memory Chains and Semantic Supervision | Story comprehension requires a deep semantic understanding of the narrative,
making it a challenging task. Inspired by previous studies on ROC Story Cloze
Test, we propose a novel method, tracking various semantic aspects with
external neural memory chains while encouraging each to focus on a particular
semantic aspect. Evaluated on the task of story ending prediction, our model
demonstrates superior performance to a collection of competitive baselines,
setting a new state of the art.
| 2,018 | Computation and Language |
Towards Robust Neural Machine Translation | Small perturbations in the input can severely distort intermediate
representations and thus impact translation quality of neural machine
translation (NMT) models. In this paper, we propose to improve the robustness
of NMT models with adversarial stability training. The basic idea is to make
both the encoder and decoder in NMT models robust against input perturbations
by enabling them to behave similarly for the original input and its perturbed
counterpart. Experimental results on Chinese-English, English-German and
English-French translation tasks show that our approaches can not only achieve
significant improvements over strong NMT systems but also improve the
robustness of NMT models.
| 2,018 | Computation and Language |
Joint Training of Candidate Extraction and Answer Selection for Reading
Comprehension | While sophisticated neural-based techniques have been developed in reading
comprehension, most approaches model the answer in an independent manner,
ignoring its relations with other answer candidates. This problem can be even
worse in open-domain scenarios, where candidates from multiple passages should
be combined to answer a single question. In this paper, we formulate reading
comprehension as an extract-then-select two-stage procedure. We first extract
answer candidates from passages, then select the final answer by combining
information from all the candidates. Furthermore, we regard candidate
extraction as a latent variable and train the two-stage process jointly with
reinforcement learning. As a result, our approach has improved the
state-of-the-art performance significantly on two challenging open-domain
reading comprehension datasets. Further analysis demonstrates the effectiveness
of our model components, especially the information fusion of all the
candidates and the joint training of the extract-then-select procedure.
| 2,018 | Computation and Language |
Color naming reflects both perceptual structure and communicative need | Gibson et al. (2017) argued that color naming is shaped by patterns of
communicative need. In support of this claim, they showed that color naming
systems across languages support more precise communication about warm colors
than cool colors, and that the objects we talk about tend to be warm-colored
rather than cool-colored. Here, we present new analyses that alter this
picture. We show that greater communicative precision for warm than for cool
colors, and greater communicative need, may both be explained by perceptual
structure. However, using an information-theoretic analysis, we also show that
color naming across languages bears signs of communicative need beyond what
would be predicted by perceptual structure alone. We conclude that color naming
is shaped both by perceptual structure, as has traditionally been argued, and
by patterns of communicative need, as argued by Gibson et al. - although for
reasons other than those they advanced.
| 2,018 | Computation and Language |
Contextual Augmentation: Data Augmentation by Words with Paradigmatic
Relations | We propose a novel data augmentation for labeled sentences called contextual
augmentation. We assume an invariance that sentences are natural even if the
words in the sentences are replaced with other words with paradigmatic
relations. We stochastically replace words with other words that are predicted
by a bi-directional language model at the word positions. Words predicted
according to a context are numerous but appropriate for the augmentation of the
original words. Furthermore, we retrofit a language model with a
label-conditional architecture, which allows the model to augment sentences
without breaking the label-compatibility. Through the experiments for six
various different text classification tasks, we demonstrate that the proposed
method improves classifiers based on the convolutional or recurrent neural
networks.
| 2,018 | Computation and Language |
Conversational Analysis using Utterance-level Attention-based
Bidirectional Recurrent Neural Networks | Recent approaches for dialogue act recognition have shown that context from
preceding utterances is important to classify the subsequent one. It was shown
that the performance improves rapidly when the context is taken into account.
We propose an utterance-level attention-based bidirectional recurrent neural
network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances
to classify the current one. In our setup, the BiRNN is given the input set of
current and preceding utterances. Our model outperforms previous models that
use only preceding utterances as context on the used corpus. Another
contribution of the article is to discover the amount of information in each
utterance to classify the subsequent one and to show that context-based
learning not only improves the performance but also achieves higher confidence
in the classification. We use character- and word-level features to represent
the utterances. The results are presented for character and word feature
representations and as an ensemble model of both representations. We found that
when classifying short utterances, the closest preceding utterances contributes
to a higher degree.
| 2,020 | Computation and Language |
A Unified Model for Extractive and Abstractive Summarization using
Inconsistency Loss | We propose a unified model combining the strength of extractive and
abstractive summarization. On the one hand, a simple extractive model can
obtain sentence-level attention with high ROUGE scores but less readable. On
the other hand, a more complicated abstractive model can obtain word-level
dynamic attention to generate a more readable paragraph. In our model,
sentence-level attention is used to modulate the word-level attention such that
words in less attended sentences are less likely to be generated. Moreover, a
novel inconsistency loss function is introduced to penalize the inconsistency
between two levels of attentions. By end-to-end training our model with the
inconsistency loss and original losses of extractive and abstractive models, we
achieve state-of-the-art ROUGE scores while being the most informative and
readable summarization on the CNN/Daily Mail dataset in a solid human
evaluation.
| 2,018 | Computation and Language |
A Context-based Approach for Dialogue Act Recognition using Simple
Recurrent Neural Networks | Dialogue act recognition is an important part of natural language
understanding. We investigate the way dialogue act corpora are annotated and
the learning approaches used so far. We find that the dialogue act is
context-sensitive within the conversation for most of the classes.
Nevertheless, previous models of dialogue act classification work on the
utterance-level and only very few consider context. We propose a novel
context-based learning method to classify dialogue acts using a character-level
language model utterance representation, and we notice significant improvement.
We evaluate this method on the Switchboard Dialogue Act corpus, and our results
show that the consideration of the preceding utterances as a context of the
current utterance improves dialogue act detection.
| 2,018 | Computation and Language |
A robust self-learning method for fully unsupervised cross-lingual
mappings of word embeddings | Recent work has managed to learn cross-lingual word embeddings without
parallel data by mapping monolingual embeddings to a shared space through
adversarial training. However, their evaluation has focused on favorable
conditions, using comparable corpora or closely-related languages, and we show
that they often fail in more realistic scenarios. This work proposes an
alternative approach based on a fully unsupervised initialization that
explicitly exploits the structural similarity of the embeddings, and a robust
self-learning algorithm that iteratively improves this solution. Our method
succeeds in all tested scenarios and obtains the best published results in
standard datasets, even surpassing previous supervised systems. Our
implementation is released as an open source project at
https://github.com/artetxem/vecmap
| 2,021 | Computation and Language |
Automatic Annotation of Locative and Directional Expressions in Arabic | In this paper, we introduce a rule-based approach to annotate Locative and
Directional Expressions in Arabic natural language text. The annotation is
based on a constructed semantic map of the spatiality domain. Challenges are
twofold: first, we need to study how locative and directional expressions are
expressed linguistically in these texts; and second, we need to automatically
annotate the relevant textual segments accordingly. The research method we will
use in this article is analytic-descriptive. We will validate this approach on
specific novel rich with these expressions and show that it has very promising
results. We will be using NOOJ as a software tool to implement finite-state
transducers to annotate linguistic elements according to Locative and
Directional Expressions. In conclusion, NOOJ allowed us to write linguistic
rules for the automatic annotation in Arabic text of Locative and Directional
Expressions.
| 2,018 | Computation and Language |
#phramacovigilance - Exploring Deep Learning Techniques for Identifying
Mentions of Medication Intake from Twitter | Mining social media messages for health and drug related information has
received significant interest in pharmacovigilance research. Social media sites
(e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of
drug usage and analyzing expression of sentiments related to drugs. Most of
these studies are based on aggregated results from a large population rather
than specific sets of individuals. In order to conduct studies at an individual
level or specific cohorts, identifying posts mentioning intake of medicine by
the user is necessary. Towards this objective, we train different deep neural
network classification models on a publicly available annotated dataset and
study their performances on identifying mentions of personal intake of medicine
in tweets. We also design and train a new architecture of a stacked ensemble of
shallow convolutional neural network (CNN) ensembles. We use random search for
tuning the hyperparameters of the models and share the details of the values
taken by the hyperparameters for the best learnt model in different deep neural
network architectures. Our system produces state-of-the-art results, with a
micro- averaged F-score of 0.693.
| 2,018 | Computation and Language |
Composing Finite State Transducers on GPUs | Weighted finite-state transducers (FSTs) are frequently used in language
processing to handle tasks such as part-of-speech tagging and speech
recognition. There has been previous work using multiple CPU cores to
accelerate finite state algorithms, but limited attention has been given to
parallel graphics processing unit (GPU) implementations. In this paper, we
introduce the first (to our knowledge) GPU implementation of the FST
composition operation, and we also discuss the optimizations used to achieve
the best performance on this architecture. We show that our approach obtains
speedups of up to 6x over our serial implementation and 4.5x over OpenFST.
| 2,018 | Computation and Language |
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums | The literature in automated sarcasm detection has mainly focused on lexical,
syntactic and semantic-level analysis of text. However, a sarcastic sentence
can be expressed with contextual presumptions, background and commonsense
knowledge. In this paper, we propose CASCADE (a ContextuAl SarCasm DEtector)
that adopts a hybrid approach of both content and context-driven modeling for
sarcasm detection in online social media discussions. For the latter, CASCADE
aims at extracting contextual information from the discourse of a discussion
thread. Also, since the sarcastic nature and form of expression can vary from
person to person, CASCADE utilizes user embeddings that encode stylometric and
personality features of the users. When used along with content-based feature
extractors such as Convolutional Neural Networks (CNNs), we see a significant
boost in the classification performance on a large Reddit corpus.
| 2,018 | Computation and Language |
First Experiments with Neural Translation of Informal to Formal
Mathematics | We report on our experiments to train deep neural networks that automatically
translate informalized LaTeX-written Mizar texts into the formal Mizar
language. To the best of our knowledge, this is the first time when neural
networks have been adopted in the formalization of mathematics. Using Luong et
al.'s neural machine translation model (NMT), we tested our aligned
informal-formal corpora against various hyperparameters and evaluated their
results. Our experiments show that our best performing model configurations are
able to generate correct Mizar statements on 65.73\% of the inference data,
with the union of all models covering 79.17\%. These results indicate that
formalization through artificial neural network is a promising approach for
automated formalization of mathematics. We present several case studies to
illustrate our results.
| 2,018 | Computation and Language |
Weight Initialization in Neural Language Models | Semantic Similarity is an important application which finds its use in many
downstream NLP applications. Though the task is mathematically defined,
semantic similarity's essence is to capture the notions of similarity
impregnated in humans. Machines use some heuristics to calculate the similarity
between words, but these are typically corpus dependent or are useful for
specific domains. The difference between Semantic Similarity and Semantic
Relatedness motivates the development of new algorithms. For a human, the word
car and road are probably as related as car and bus. But this may not be the
case for computational methods. Ontological methods are good at encoding
Semantic Similarity and Vector Space models are better at encoding Semantic
Relatedness. There is a dearth of methods which leverage ontologies to create
better vector representations. The aim of this proposal is to explore in the
direction of a hybrid method which combines statistical/vector space methods
like Word2Vec and Ontological methods like WordNet to leverage the advantages
provided by both.
| 2,018 | Computation and Language |
Analogical Reasoning on Chinese Morphological and Semantic Relations | Analogical reasoning is effective in capturing linguistic regularities. This
paper proposes an analogical reasoning task on Chinese. After delving into
Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28
explicit semantic relations. A big and balanced dataset CA8 is then built for
this task, including 17813 questions. Furthermore, we systematically explore
the influences of vector representations, context features, and corpora on
analogical reasoning. With the experiments, CA8 is proved to be a reliable
benchmark for evaluating Chinese word embeddings.
| 2,018 | Computation and Language |
Facebook Reaction-Based Emotion Classifier as Cue for Sarcasm Detection | Online social media users react to content in them based on context. Emotions
or mood play a significant part of these reactions, which has filled these
platforms with opinionated content. Different approaches and applications to
make better use of this data are continuously being developed. However, due to
the nature of the data, the variety of platforms, and dynamic online user
behavior, there are still many issues to be dealt with. It remains a challenge
to properly obtain a reliable emotional status from a user prior to posting a
comment. This work introduces a methodology that explores semi-supervised
multilingual emotion detection based on the overlap of Facebook reactions and
textual data. With the resulting emotion detection system we evaluate the
possibility of using emotions and user behavior features for the task of
sarcasm detection. More than 1 million English and Chinese comments from over
62,000 public Facebook pages posts have been collected and processed, conducted
experiments show acceptable performance metrics.
| 2,018 | Computation and Language |
Improving End-of-turn Detection in Spoken Dialogues by Detecting Speaker
Intentions as a Secondary Task | This work focuses on the use of acoustic cues for modeling turn-taking in
dyadic spoken dialogues. Previous work has shown that speaker intentions (e.g.,
asking a question, uttering a backchannel, etc.) can influence turn-taking
behavior and are good predictors of turn-transitions in spoken dialogues.
However, speaker intentions are not readily available for use by automated
systems at run-time; making it difficult to use this information to anticipate
a turn-transition. To this end, we propose a multi-task neural approach for
predicting turn- transitions and speaker intentions simultaneously. Our results
show that adding the auxiliary task of speaker intention prediction improves
the performance of turn-transition prediction in spoken dialogues, without
relying on additional input features during run-time.
| 2,018 | Computation and Language |
Composite Semantic Relation Classification | Different semantic interpretation tasks such as text entailment and question
answering require the classification of semantic relations between terms or
entities within text. However, in most cases it is not possible to assign a
direct semantic relation between entities/terms. This paper proposes an
approach for composite semantic relation classification, extending the
traditional semantic relation classification task. Different from existing
approaches, which use machine learning models built over lexical and
distributional word vector features, the proposed model uses the combination of
a large commonsense knowledge base of binary relations, a distributional
navigational algorithm and sequence classification to provide a solution for
the composite semantic relation classification problem.
| 2,018 | Computation and Language |
Semantic Relatedness for All (Languages): A Comparative Analysis of
Multilingual Semantic Relatedness Using Machine Translation | This paper provides a comparative analysis of the performance of four
state-of-the-art distributional semantic models (DSMs) over 11 languages,
contrasting the native language-specific models with the use of machine
translation over English-based DSMs. The experimental results show that there
is a significant improvement (average of 16.7% for the Spearman correlation) by
using state-of-the-art machine translation approaches. The results also show
that the benefit of using the most informative corpus outweighs the possible
errors introduced by the machine translation. For all languages, the
combination of machine translation over the Word2Vec English distributional
model provided the best results consistently (average Spearman correlation of
0.68).
| 2,018 | Computation and Language |
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