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Deep Joint Entity Disambiguation with Local Neural Attention | We propose a novel deep learning model for joint document-level entity
disambiguation, which leverages learned neural representations. Key components
are entity embeddings, a neural attention mechanism over local context windows,
and a differentiable joint inference stage for disambiguation. Our approach
thereby combines benefits of deep learning with more traditional approaches
such as graphical models and probabilistic mention-entity maps. Extensive
experiments show that we are able to obtain competitive or state-of-the-art
accuracy at moderate computational costs.
| 2,017 | Computation and Language |
Sparse Communication for Distributed Gradient Descent | We make distributed stochastic gradient descent faster by exchanging sparse
updates instead of dense updates. Gradient updates are positively skewed as
most updates are near zero, so we map the 99% smallest updates (by absolute
value) to zero then exchange sparse matrices. This method can be combined with
quantization to further improve the compression. We explore different
configurations and apply them to neural machine translation and MNIST image
classification tasks. Most configurations work on MNIST, whereas different
configurations reduce convergence rate on the more complex translation task.
Our experiments show that we can achieve up to 49% speed up on MNIST and 22% on
NMT without damaging the final accuracy or BLEU.
| 2,017 | Computation and Language |
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity
with Financial Word Embeddings | This paper presents the approach developed at the Faculty of Engineering of
University of Porto, to participate in SemEval 2017, Task 5: Fine-grained
Sentiment Analysis on Financial Microblogs and News. The task consisted in
predicting a real continuous variable from -1.0 to +1.0 representing the
polarity and intensity of sentiment concerning companies/stocks mentioned in
short texts. We modeled the task as a regression analysis problem and combined
traditional techniques such as pre-processing short texts, bag-of-words
representations and lexical-based features with enhanced financial specific
bag-of-embeddings. We used an external collection of tweets and news headlines
mentioning companies/stocks from S\&P 500 to create financial word embeddings
which are able to capture domain-specific syntactic and semantic similarities.
The resulting approach obtained a cosine similarity score of 0.69 in sub-task
5.1 - Microblogs and 0.68 in sub-task 5.2 - News Headlines.
| 2,017 | Computation and Language |
Automatic Disambiguation of French Discourse Connectives | Discourse connectives (e.g. however, because) are terms that can explicitly
convey a discourse relation within a text. While discourse connectives have
been shown to be an effective clue to automatically identify discourse
relations, they are not always used to convey such relations, thus they should
first be disambiguated between discourse-usage non-discourse-usage. In this
paper, we investigate the applicability of features proposed for the
disambiguation of English discourse connectives for French. Our results with
the French Discourse Treebank (FDTB) show that syntactic and lexical features
developed for English texts are as effective for French and allow the
disambiguation of French discourse connectives with an accuracy of 94.2%.
| 2,016 | Computation and Language |
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine | We publicly release a new large-scale dataset, called SearchQA, for machine
comprehension, or question-answering. Unlike recently released datasets, such
as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to
reflect a full pipeline of general question-answering. That is, we start not
from an existing article and generate a question-answer pair, but start from an
existing question-answer pair, crawled from J! Archive, and augment it with
text snippets retrieved by Google. Following this approach, we built SearchQA,
which consists of more than 140k question-answer pairs with each pair having
49.6 snippets on average. Each question-answer-context tuple of the SearchQA
comes with additional meta-data such as the snippet's URL, which we believe
will be valuable resources for future research. We conduct human evaluation as
well as test two baseline methods, one simple word selection and the other deep
learning based, on the SearchQA. We show that there is a meaningful gap between
the human and machine performances. This suggests that the proposed dataset
could well serve as a benchmark for question-answering.
| 2,017 | Computation and Language |
Sentiment analysis based on rhetorical structure theory: Learning deep
neural networks from discourse trees | Prominent applications of sentiment analysis are countless, covering areas
such as marketing, customer service and communication. The conventional
bag-of-words approach for measuring sentiment merely counts term frequencies;
however, it neglects the position of the terms within the discourse. As a
remedy, we develop a discourse-aware method that builds upon the discourse
structure of documents. For this purpose, we utilize rhetorical structure
theory to label (sub-)clauses according to their hierarchical relationships and
then assign polarity scores to individual leaves. To learn from the resulting
rhetorical structure, we propose a tensor-based, tree-structured deep neural
network (named Discourse-LSTM) in order to process the complete discourse tree.
The underlying tensors infer the salient passages of narrative materials. In
addition, we suggest two algorithms for data augmentation (node reordering and
artificial leaf insertion) that increase our training set and reduce
overfitting. Our benchmarks demonstrate the superior performance of our
approach. Moreover, our tensor structure reveals the salient text passages and
thereby provides explanatory insights.
| 2,018 | Computation and Language |
Baselines and test data for cross-lingual inference | The recent years have seen a revival of interest in textual entailment,
sparked by i) the emergence of powerful deep neural network learners for
natural language processing and ii) the timely development of large-scale
evaluation datasets such as SNLI. Recast as natural language inference, the
problem now amounts to detecting the relation between pairs of statements: they
either contradict or entail one another, or they are mutually neutral. Current
research in natural language inference is effectively exclusive to English. In
this paper, we propose to advance the research in SNLI-style natural language
inference toward multilingual evaluation. To that end, we provide test data for
four major languages: Arabic, French, Spanish, and Russian. We experiment with
a set of baselines. Our systems are based on cross-lingual word embeddings and
machine translation. While our best system scores an average accuracy of just
over 75%, we focus largely on enabling further research in multilingual
inference.
| 2,018 | Computation and Language |
Representing Sentences as Low-Rank Subspaces | Sentences are important semantic units of natural language. A generic,
distributional representation of sentences that can capture the latent
semantics is beneficial to multiple downstream applications. We observe a
simple geometry of sentences -- the word representations of a given sentence
(on average 10.23 words in all SemEval datasets with a standard deviation 4.84)
roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this
observation, we represent a sentence by the low-rank subspace spanned by its
word vectors. Such an unsupervised representation is empirically validated via
semantic textual similarity tasks on 19 different datasets, where it
outperforms the sophisticated neural network models, including skip-thought
vectors, by 15% on average.
| 2,017 | Computation and Language |
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their
Use in Parallel Sentence Identification | End-to-end neural machine translation has overtaken statistical machine
translation in terms of translation quality for some language pairs, specially
those with large amounts of parallel data. Besides this palpable improvement,
neural networks provide several new properties. A single system can be trained
to translate between many languages at almost no additional cost other than
training time. Furthermore, internal representations learned by the network
serve as a new semantic representation of words -or sentences- which, unlike
standard word embeddings, are learned in an essentially bilingual or even
multilingual context. In view of these properties, the contribution of the
present work is two-fold. First, we systematically study the NMT context
vectors, i.e. output of the encoder, and their power as an interlingua
representation of a sentence. We assess their quality and effectiveness by
measuring similarities across translations, as well as semantically related and
semantically unrelated sentence pairs. Second, as extrinsic evaluation of the
first point, we identify parallel sentences in comparable corpora, obtaining an
F1=98.2% on data from a shared task when using only NMT context vectors. Using
context vectors jointly with similarity measures F1 reaches 98.9%.
| 2,017 | Computation and Language |
A Broad-Coverage Challenge Corpus for Sentence Understanding through
Inference | This paper introduces the Multi-Genre Natural Language Inference (MultiNLI)
corpus, a dataset designed for use in the development and evaluation of machine
learning models for sentence understanding. In addition to being one of the
largest corpora available for the task of NLI, at 433k examples, this corpus
improves upon available resources in its coverage: it offers data from ten
distinct genres of written and spoken English--making it possible to evaluate
systems on nearly the full complexity of the language--and it offers an
explicit setting for the evaluation of cross-genre domain adaptation.
| 2,018 | Computation and Language |
Extractive Summarization: Limits, Compression, Generalized Model and
Heuristics | Due to its promise to alleviate information overload, text summarization has
attracted the attention of many researchers. However, it has remained a serious
challenge. Here, we first prove empirical limits on the recall (and F1-scores)
of extractive summarizers on the DUC datasets under ROUGE evaluation for both
the single-document and multi-document summarization tasks. Next we define the
concept of compressibility of a document and present a new model of
summarization, which generalizes existing models in the literature and
integrates several dimensions of the summarization, viz., abstractive versus
extractive, single versus multi-document, and syntactic versus semantic.
Finally, we examine some new and existing single-document summarization
algorithms in a single framework and compare with state of the art summarizers
on DUC data.
| 2,017 | Computation and Language |
Predicting Role Relevance with Minimal Domain Expertise in a Financial
Domain | Word embeddings have made enormous inroads in recent years in a wide variety
of text mining applications. In this paper, we explore a word embedding-based
architecture for predicting the relevance of a role between two financial
entities within the context of natural language sentences. In this extended
abstract, we propose a pooled approach that uses a collection of sentences to
train word embeddings using the skip-gram word2vec architecture. We use the
word embeddings to obtain context vectors that are assigned one or more labels
based on manual annotations. We train a machine learning classifier using the
labeled context vectors, and use the trained classifier to predict contextual
role relevance on test data. Our approach serves as a good minimal-expertise
baseline for the task as it is simple and intuitive, uses open-source modules,
requires little feature crafting effort and performs well across roles.
| 2,017 | Computation and Language |
A Large Self-Annotated Corpus for Sarcasm | We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for
sarcasm research and for training and evaluating systems for sarcasm detection.
The corpus has 1.3 million sarcastic statements -- 10 times more than any
previous dataset -- and many times more instances of non-sarcastic statements,
allowing for learning in both balanced and unbalanced label regimes. Each
statement is furthermore self-annotated -- sarcasm is labeled by the author,
not an independent annotator -- and provided with user, topic, and conversation
context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm
detection, and evaluate baseline methods.
| 2,018 | Computation and Language |
Dependency resolution and semantic mining using Tree Adjoining Grammars
for Tamil Language | Tree adjoining grammars (TAGs) provide an ample tool to capture syntax of
many Indian languages. Tamil represents a special challenge to computational
formalisms as it has extensive agglutinative morphology and a comparatively
difficult argument structure. Modelling Tamil syntax and morphology using TAG
is an interesting problem which has not been in focus even though TAGs are over
4 decades old, since its inception. Our research with Tamil TAGs have shown us
that we can not only represent syntax of the language, but to an extent mine
out semantics through dependency resolution of the sentence. But in order to
demonstrate this phenomenal property, we need to parse Tamil language sentences
using TAGs we have built and through parsing obtain a derivation we could use
to resolve dependencies, thus proving the semantic property. We use an in-house
developed pseudo lexical TAG chart parser; algorithm given by Schabes and Joshi
(1988), for generating derivations of sentences. We do not use any statistics
to rank out ambiguous derivations but rather use all of them to understand the
mentioned semantic relation with in TAGs for Tamil. We shall also present a
brief parser analysis for the completeness of our discussions.
| 2,017 | Computation and Language |
Adversarial Multi-task Learning for Text Classification | Neural network models have shown their promising opportunities for multi-task
learning, which focus on learning the shared layers to extract the common and
task-invariant features. However, in most existing approaches, the extracted
shared features are prone to be contaminated by task-specific features or the
noise brought by other tasks. In this paper, we propose an adversarial
multi-task learning framework, alleviating the shared and private latent
feature spaces from interfering with each other. We conduct extensive
experiments on 16 different text classification tasks, which demonstrates the
benefits of our approach. Besides, we show that the shared knowledge learned by
our proposed model can be regarded as off-the-shelf knowledge and easily
transferred to new tasks. The datasets of all 16 tasks are publicly available
at \url{http://nlp.fudan.edu.cn/data/}
| 2,017 | Computation and Language |
Understanding Task Design Trade-offs in Crowdsourced Paraphrase
Collection | Linguistically diverse datasets are critical for training and evaluating
robust machine learning systems, but data collection is a costly process that
often requires experts. Crowdsourcing the process of paraphrase generation is
an effective means of expanding natural language datasets, but there has been
limited analysis of the trade-offs that arise when designing tasks. In this
paper, we present the first systematic study of the key factors in
crowdsourcing paraphrase collection. We consider variations in instructions,
incentives, data domains, and workflows. We manually analyzed paraphrases for
correctness, grammaticality, and linguistic diversity. Our observations provide
new insight into the trade-offs between accuracy and diversity in crowd
responses that arise as a result of task design, providing guidance for future
paraphrase generation procedures.
| 2,017 | Computation and Language |
Redefining Context Windows for Word Embedding Models: An Experimental
Study | Distributional semantic models learn vector representations of words through
the contexts they occur in. Although the choice of context (which often takes
the form of a sliding window) has a direct influence on the resulting
embeddings, the exact role of this model component is still not fully
understood. This paper presents a systematic analysis of context windows based
on a set of four distinct hyper-parameters. We train continuous Skip-Gram
models on two English-language corpora for various combinations of these
hyper-parameters, and evaluate them on both lexical similarity and analogy
tasks. Notable experimental results are the positive impact of cross-sentential
contexts and the surprisingly good performance of right-context windows.
| 2,017 | Computation and Language |
End-to-End Multi-View Networks for Text Classification | We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the text's words. Aggregating many such views results in a
more discriminative and robust representation. Through a novel architecture
that both stacks and concatenates views, we produce a network that emphasizes
both depth and width, allowing training to converge quickly. Using our
multi-view architecture, we establish new state-of-the-art accuracies on two
benchmark tasks.
| 2,017 | Computation and Language |
An Interpretable Knowledge Transfer Model for Knowledge Base Completion | Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer statistical strength through the sharing of concepts. Moreover, the
learned associations between relations and concepts, which are represented by
sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
improvements on both the mean rank and Hits@10 metrics, over all baselines that
do not use additional information.
| 2,017 | Computation and Language |
Global Relation Embedding for Relation Extraction | We study the problem of textual relation embedding with distant supervision.
To combat the wrong labeling problem of distant supervision, we propose to
embed textual relations with global statistics of relations, i.e., the
co-occurrence statistics of textual and knowledge base relations collected from
the entire corpus. This approach turns out to be more robust to the training
noise introduced by distant supervision. On a popular relation extraction
dataset, we show that the learned textual relation embedding can be used to
augment existing relation extraction models and significantly improve their
performance. Most remarkably, for the top 1,000 relational facts discovered by
the best existing model, the precision can be improved from 83.9% to 89.3%.
| 2,018 | Computation and Language |
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support
for rumours | Media is full of false claims. Even Oxford Dictionaries named "post-truth" as
the word of 2016. This makes it more important than ever to build systems that
can identify the veracity of a story, and the kind of discourse there is around
it. RumourEval is a SemEval shared task that aims to identify and handle
rumours and reactions to them, in text. We present an annotation scheme, a
large dataset covering multiple topics - each having their own families of
claims and replies - and use these to pose two concrete challenges as well as
the results achieved by participants on these challenges.
| 2,017 | Computation and Language |
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks
for Early Rumor Detection | The proliferation of social media in communication and information
dissemination has made it an ideal platform for spreading rumors. Automatically
debunking rumors at their stage of diffusion is known as \textit{early rumor
detection}, which refers to dealing with sequential posts regarding disputed
factual claims with certain variations and highly textual duplication over
time. Thus, identifying trending rumors demands an efficient yet flexible model
that is able to capture long-range dependencies among postings and produce
distinct representations for the accurate early detection. However, it is a
challenging task to apply conventional classification algorithms to rumor
detection in earliness since they rely on hand-crafted features which require
intensive manual efforts in the case of large amount of posts. This paper
presents a deep attention model on the basis of recurrent neural networks (RNN)
to learn \textit{selectively} temporal hidden representations of sequential
posts for identifying rumors. The proposed model delves soft-attention into the
recurrence to simultaneously pool out distinct features with particular focus
and produce hidden representations that capture contextual variations of
relevant posts over time. Extensive experiments on real datasets collected from
social media websites demonstrate that (1) the deep attention based RNN model
outperforms state-of-the-arts that rely on hand-crafted features; (2) the
introduction of soft attention mechanism can effectively distill relevant parts
to rumors from original posts in advance; (3) the proposed method detects
rumors more quickly and accurately than competitors.
| 2,017 | Computation and Language |
Cross-domain Semantic Parsing via Paraphrasing | Existing studies on semantic parsing mainly focus on the in-domain setting.
We formulate cross-domain semantic parsing as a domain adaptation problem:
train a semantic parser on some source domains and then adapt it to the target
domain. Due to the diversity of logical forms in different domains, this
problem presents unique and intriguing challenges. By converting logical forms
into canonical utterances in natural language, we reduce semantic parsing to
paraphrasing, and develop an attentive sequence-to-sequence paraphrase model
that is general and flexible to adapt to different domains. We discover two
problems, small micro variance and large macro variance, of pre-trained word
embeddings that hinder their direct use in neural networks, and propose
standardization techniques as a remedy. On the popular Overnight dataset, which
contains eight domains, we show that both cross-domain training and
standardized pre-trained word embeddings can bring significant improvement.
| 2,017 | Computation and Language |
Neural End-to-End Learning for Computational Argumentation Mining | We investigate neural techniques for end-to-end computational argumentation
mining (AM). We frame AM both as a token-based dependency parsing and as a
token-based sequence tagging problem, including a multi-task learning setup.
Contrary to models that operate on the argument component level, we find that
framing AM as dependency parsing leads to subpar performance results. In
contrast, less complex (local) tagging models based on BiLSTMs perform robustly
across classification scenarios, being able to catch long-range dependencies
inherent to the AM problem. Moreover, we find that jointly learning 'natural'
subtasks, in a multi-task learning setup, improves performance.
| 2,017 | Computation and Language |
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and
LSTMs | In this paper we describe our attempt at producing a state-of-the-art Twitter
sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short
Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled
data to pre-train word embeddings. We then use a subset of the unlabeled data
to fine tune the embeddings using distant supervision. The final CNNs and LSTMs
are trained on the SemEval-2017 Twitter dataset where the embeddings are fined
tuned again. To boost performances we ensemble several CNNs and LSTMs together.
Our approach achieved first rank on all of the five English subtasks amongst 40
teams.
| 2,017 | Computation and Language |
Improved Neural Relation Detection for Knowledge Base Question Answering | Relation detection is a core component for many NLP applications including
Knowledge Base Question Answering (KBQA). In this paper, we propose a
hierarchical recurrent neural network enhanced by residual learning that
detects KB relations given an input question. Our method uses deep residual
bidirectional LSTMs to compare questions and relation names via different
hierarchies of abstraction. Additionally, we propose a simple KBQA system that
integrates entity linking and our proposed relation detector to enable one
enhance another. Experimental results evidence that our approach achieves not
only outstanding relation detection performance, but more importantly, it helps
our KBQA system to achieve state-of-the-art accuracy for both single-relation
(SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.
| 2,017 | Computation and Language |
Reinforcement Learning with External Knowledge and Two-Stage Q-functions
for Predicting Popular Reddit Threads | This paper addresses the problem of predicting popularity of comments in an
online discussion forum using reinforcement learning, particularly addressing
two challenges that arise from having natural language state and action spaces.
First, the state representation, which characterizes the history of comments
tracked in a discussion at a particular point, is augmented to incorporate the
global context represented by discussions on world events available in an
external knowledge source. Second, a two-stage Q-learning framework is
introduced, making it feasible to search the combinatorial action space while
also accounting for redundancy among sub-actions. We experiment with five
Reddit communities, showing that the two methods improve over previous reported
results on this task.
| 2,017 | Computation and Language |
A Semantic QA-Based Approach for Text Summarization Evaluation | Many Natural Language Processing and Computational Linguistics applications
involves the generation of new texts based on some existing texts, such as
summarization, text simplification and machine translation. However, there has
been a serious problem haunting these applications for decades, that is, how to
automatically and accurately assess quality of these applications. In this
paper, we will present some preliminary results on one especially useful and
challenging problem in NLP system evaluation: how to pinpoint content
differences of two text passages (especially for large pas-sages such as
articles and books). Our idea is intuitive and very different from existing
approaches. We treat one text passage as a small knowledge base, and ask it a
large number of questions to exhaustively identify all content points in it. By
comparing the correctly answered questions from two text passages, we will be
able to compare their content precisely. The experiment using 2007 DUC
summarization corpus clearly shows promising results.
| 2,018 | Computation and Language |
Stability and Fluctuations in a Simple Model of Phonetic Category Change | In spoken languages, speakers divide up the space of phonetic possibilities
into different regions, corresponding to different phonemes. We consider a
simple exemplar model of how this division of phonetic space varies over time
among a population of language users. In the particular model we consider, we
show that, once the system is initialized with a given set of phonemes, that
phonemes do not become extinct: all phonemes will be maintained in the system
for all time. This is in contrast to what is observed in more complex models.
Furthermore, we show that the boundaries between phonemes fluctuate and we
quantitatively study the fluctuations in a simple instance of our model. These
results prepare the ground for more sophisticated models in which some phonemes
go extinct or new phonemes emerge through other processes.
| 2,018 | Computation and Language |
SwellShark: A Generative Model for Biomedical Named Entity Recognition
without Labeled Data | We present SwellShark, a framework for building biomedical named entity
recognition (NER) systems quickly and without hand-labeled data. Our approach
views biomedical resources like lexicons as function primitives for
autogenerating weak supervision. We then use a generative model to unify and
denoise this supervision and construct large-scale, probabilistically labeled
datasets for training high-accuracy NER taggers. In three biomedical NER tasks,
SwellShark achieves competitive scores with state-of-the-art supervised
benchmarks using no hand-labeled training data. In a drug name extraction task
using patient medical records, one domain expert using SwellShark achieved
within 5.1% of a crowdsourced annotation approach -- which originally utilized
20 teams over the course of several weeks -- in 24 hours.
| 2,017 | Computation and Language |
Improving Context Aware Language Models | Increased adaptability of RNN language models leads to improved predictions
that benefit many applications. However, current methods do not take full
advantage of the RNN structure. We show that the most widely-used approach to
adaptation (concatenating the context with the word embedding at the input to
the recurrent layer) is outperformed by a model that has some low-cost
improvements: adaptation of both the hidden and output layers. and a feature
hashing bias term to capture context idiosyncrasies. Experiments on language
modeling and classification tasks using three different corpora demonstrate the
advantages of the proposed techniques.
| 2,017 | Computation and Language |
Neural System Combination for Machine Translation | Neural machine translation (NMT) becomes a new approach to machine
translation and generates much more fluent results compared to statistical
machine translation (SMT).
However, SMT is usually better than NMT in translation adequacy. It is
therefore a promising direction to combine the advantages of both NMT and SMT.
In this paper, we propose a neural system combination framework leveraging
multi-source NMT, which takes as input the outputs of NMT and SMT systems and
produces the final translation.
Extensive experiments on the Chinese-to-English translation task show that
our model archives significant improvement by 5.3 BLEU points over the best
single system output and 3.4 BLEU points over the state-of-the-art traditional
system combination methods.
| 2,017 | Computation and Language |
Attention Strategies for Multi-Source Sequence-to-Sequence Learning | Modeling attention in neural multi-source sequence-to-sequence learning
remains a relatively unexplored area, despite its usefulness in tasks that
incorporate multiple source languages or modalities. We propose two novel
approaches to combine the outputs of attention mechanisms over each source
sequence, flat and hierarchical. We compare the proposed methods with existing
techniques and present results of systematic evaluation of those methods on the
WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the
proposed methods achieve competitive results on both tasks.
| 2,017 | Computation and Language |
Scientific Article Summarization Using Citation-Context and Article's
Discourse Structure | We propose a summarization approach for scientific articles which takes
advantage of citation-context and the document discourse model. While citations
have been previously used in generating scientific summaries, they lack the
related context from the referenced article and therefore do not accurately
reflect the article's content. Our method overcomes the problem of
inconsistency between the citation summary and the article's content by
providing context for each citation. We also leverage the inherent scientific
article's discourse for producing better summaries. We show that our proposed
method effectively improves over existing summarization approaches (greater
than 30% improvement over the best performing baseline) in terms of
\textsc{Rouge} scores on TAC2014 scientific summarization dataset. While the
dataset we use for evaluation is in the biomedical domain, most of our
approaches are general and therefore adaptable to other domains.
| 2,017 | Computation and Language |
Improving Semantic Composition with Offset Inference | Count-based distributional semantic models suffer from sparsity due to
unobserved but plausible co-occurrences in any text collection. This problem is
amplified for models like Anchored Packed Trees (APTs), that take the
grammatical type of a co-occurrence into account. We therefore introduce a
novel form of distributional inference that exploits the rich type structure in
APTs and infers missing data by the same mechanism that is used for semantic
composition.
| 2,017 | Computation and Language |
Lexical Features in Coreference Resolution: To be Used With Caution | Lexical features are a major source of information in state-of-the-art
coreference resolvers. Lexical features implicitly model some of the linguistic
phenomena at a fine granularity level. They are especially useful for
representing the context of mentions. In this paper we investigate a drawback
of using many lexical features in state-of-the-art coreference resolvers. We
show that if coreference resolvers mainly rely on lexical features, they can
hardly generalize to unseen domains. Furthermore, we show that the current
coreference resolution evaluation is clearly flawed by only evaluating on a
specific split of a specific dataset in which there is a notable overlap
between the training, development and test sets.
| 2,017 | Computation and Language |
Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual
Machine Translation | Sarcasm is a form of speech in which speakers say the opposite of what they
truly mean in order to convey a strong sentiment. In other words, "Sarcasm is
the giant chasm between what I say, and the person who doesn't get it.". In
this paper we present the novel task of sarcasm interpretation, defined as the
generation of a non-sarcastic utterance conveying the same message as the
original sarcastic one. We introduce a novel dataset of 3000 sarcastic tweets,
each interpreted by five human judges. Addressing the task as monolingual
machine translation (MT), we experiment with MT algorithms and evaluation
measures. We then present SIGN: an MT based sarcasm interpretation algorithm
that targets sentiment words, a defining element of textual sarcasm. We show
that while the scores of n-gram based automatic measures are similar for all
interpretation models, SIGN's interpretations are scored higher by humans for
adequacy and sentiment polarity. We conclude with a discussion on future
research directions for our new task.
| 2,017 | Computation and Language |
Medical Text Classification using Convolutional Neural Networks | We present an approach to automatically classify clinical text at a sentence
level. We are using deep convolutional neural networks to represent complex
features. We train the network on a dataset providing a broad categorization of
health information. Through a detailed evaluation, we demonstrate that our
method outperforms several approaches widely used in natural language
processing tasks by about 15%.
| 2,017 | Computation and Language |
Affect-LM: A Neural Language Model for Customizable Affective Text
Generation | Human verbal communication includes affective messages which are conveyed
through use of emotionally colored words. There has been a lot of research in
this direction but the problem of integrating state-of-the-art neural language
models with affective information remains an area ripe for exploration. In this
paper, we propose an extension to an LSTM (Long Short-Term Memory) language
model for generating conversational text, conditioned on affect categories. Our
proposed model, Affect-LM enables us to customize the degree of emotional
content in generated sentences through an additional design parameter.
Perception studies conducted using Amazon Mechanical Turk show that Affect-LM
generates naturally looking emotional sentences without sacrificing grammatical
correctness. Affect-LM also learns affect-discriminative word representations,
and perplexity experiments show that additional affective information in
conversational text can improve language model prediction.
| 2,017 | Computation and Language |
Deep Multitask Learning for Semantic Dependency Parsing | We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at https://github.com/Noahs-ARK/NeurboParser.
| 2,017 | Computation and Language |
Argument Mining with Structured SVMs and RNNs | We propose a novel factor graph model for argument mining, designed for
settings in which the argumentative relations in a document do not necessarily
form a tree structure. (This is the case in over 20% of the web comments
dataset we release.) Our model jointly learns elementary unit type
classification and argumentative relation prediction. Moreover, our model
supports SVM and RNN parametrizations, can enforce structure constraints (e.g.,
transitivity), and can express dependencies between adjacent relations and
propositions. Our approaches outperform unstructured baselines in both web
comments and argumentative essay datasets.
| 2,017 | Computation and Language |
Learning to Skim Text | Recurrent Neural Networks are showing much promise in many sub-areas of
natural language processing, ranging from document classification to machine
translation to automatic question answering. Despite their promise, many
recurrent models have to read the whole text word by word, making it slow to
handle long documents. For example, it is difficult to use a recurrent network
to read a book and answer questions about it. In this paper, we present an
approach of reading text while skipping irrelevant information if needed. The
underlying model is a recurrent network that learns how far to jump after
reading a few words of the input text. We employ a standard policy gradient
method to train the model to make discrete jumping decisions. In our benchmarks
on four different tasks, including number prediction, sentiment analysis, news
article classification and automatic Q\&A, our proposed model, a modified LSTM
with jumping, is up to 6 times faster than the standard sequential LSTM, while
maintaining the same or even better accuracy.
| 2,017 | Computation and Language |
Deep Keyphrase Generation | Keyphrase provides highly-condensed information that can be effectively used
for understanding, organizing and retrieving text content. Though previous
studies have provided many workable solutions for automated keyphrase
extraction, they commonly divided the to-be-summarized content into multiple
text chunks, then ranked and selected the most meaningful ones. These
approaches could neither identify keyphrases that do not appear in the text,
nor capture the real semantic meaning behind the text. We propose a generative
model for keyphrase prediction with an encoder-decoder framework, which can
effectively overcome the above drawbacks. We name it as deep keyphrase
generation since it attempts to capture the deep semantic meaning of the
content with a deep learning method. Empirical analysis on six datasets
demonstrates that our proposed model not only achieves a significant
performance boost on extracting keyphrases that appear in the source text, but
also can generate absent keyphrases based on the semantic meaning of the text.
Code and dataset are available at
https://github.com/memray/OpenNMT-kpg-release.
| 2,021 | Computation and Language |
Learning weakly supervised multimodal phoneme embeddings | Recent works have explored deep architectures for learning multimodal speech
representation (e.g. audio and images, articulation and audio) in a supervised
way. Here we investigate the role of combining different speech modalities,
i.e. audio and visual information representing the lips movements, in a weakly
supervised way using Siamese networks and lexical same-different side
information. In particular, we ask whether one modality can benefit from the
other to provide a richer representation for phone recognition in a weakly
supervised setting. We introduce mono-task and multi-task methods for merging
speech and visual modalities for phone recognition. The mono-task learning
consists in applying a Siamese network on the concatenation of the two
modalities, while the multi-task learning receives several different
combinations of modalities at train time. We show that multi-task learning
enhances discriminability for visual and multimodal inputs while minimally
impacting auditory inputs. Furthermore, we present a qualitative analysis of
the obtained phone embeddings, and show that cross-modal visual input can
improve the discriminability of phonological features which are visually
discernable (rounding, open/close, labial place of articulation), resulting in
representations that are closer to abstract linguistic features than those
based on audio only.
| 2,017 | Computation and Language |
Neural Machine Translation via Binary Code Prediction | In this paper, we propose a new method for calculating the output layer in
neural machine translation systems. The method is based on predicting a binary
code for each word and can reduce computation time/memory requirements of the
output layer to be logarithmic in vocabulary size in the best case. In
addition, we also introduce two advanced approaches to improve the robustness
of the proposed model: using error-correcting codes and combining softmax and
binary codes. Experiments on two English-Japanese bidirectional translation
tasks show proposed models achieve BLEU scores that approach the softmax, while
reducing memory usage to the order of less than 1/10 and improving decoding
speed on CPUs by x5 to x10.
| 2,017 | Computation and Language |
Adversarial Neural Machine Translation | In this paper, we study a new learning paradigm for Neural Machine
Translation (NMT). Instead of maximizing the likelihood of the human
translation as in previous works, we minimize the distinction between human
translation and the translation given by an NMT model. To achieve this goal,
inspired by the recent success of generative adversarial networks (GANs), we
employ an adversarial training architecture and name it as Adversarial-NMT. In
Adversarial-NMT, the training of the NMT model is assisted by an adversary,
which is an elaborately designed Convolutional Neural Network (CNN). The goal
of the adversary is to differentiate the translation result generated by the
NMT model from that by human. The goal of the NMT model is to produce high
quality translations so as to cheat the adversary. A policy gradient method is
leveraged to co-train the NMT model and the adversary. Experimental results on
English$\rightarrow$French and German$\rightarrow$English translation tasks
show that Adversarial-NMT can achieve significantly better translation quality
than several strong baselines.
| 2,018 | Computation and Language |
A* CCG Parsing with a Supertag and Dependency Factored Model | We propose a new A* CCG parsing model in which the probability of a tree is
decomposed into factors of CCG categories and its syntactic dependencies both
defined on bi-directional LSTMs. Our factored model allows the precomputation
of all probabilities and runs very efficiently, while modeling sentence
structures explicitly via dependencies. Our model achieves the state-of-the-art
results on English and Japanese CCG parsing.
| 2,017 | Computation and Language |
Naturalizing a Programming Language via Interactive Learning | Our goal is to create a convenient natural language interface for performing
well-specified but complex actions such as analyzing data, manipulating text,
and querying databases. However, existing natural language interfaces for such
tasks are quite primitive compared to the power one wields with a programming
language. To bridge this gap, we start with a core programming language and
allow users to "naturalize" the core language incrementally by defining
alternative, more natural syntax and increasingly complex concepts in terms of
compositions of simpler ones. In a voxel world, we show that a community of
users can simultaneously teach a common system a diverse language and use it to
build hundreds of complex voxel structures. Over the course of three days,
these users went from using only the core language to using the naturalized
language in 85.9\% of the last 10K utterances.
| 2,017 | Computation and Language |
Translating Neuralese | Several approaches have recently been proposed for learning decentralized
deep multiagent policies that coordinate via a differentiable communication
channel. While these policies are effective for many tasks, interpretation of
their induced communication strategies has remained a challenge. Here we
propose to interpret agents' messages by translating them. Unlike in typical
machine translation problems, we have no parallel data to learn from. Instead
we develop a translation model based on the insight that agent messages and
natural language strings mean the same thing if they induce the same belief
about the world in a listener. We present theoretical guarantees and empirical
evidence that our approach preserves both the semantics and pragmatics of
messages by ensuring that players communicating through a translation layer do
not suffer a substantial loss in reward relative to players with a common
language.
| 2,018 | Computation and Language |
Differentiable Scheduled Sampling for Credit Assignment | We demonstrate that a continuous relaxation of the argmax operation can be
used to create a differentiable approximation to greedy decoding for
sequence-to-sequence (seq2seq) models. By incorporating this approximation into
the scheduled sampling training procedure (Bengio et al., 2015)--a well-known
technique for correcting exposure bias--we introduce a new training objective
that is continuous and differentiable everywhere and that can provide
informative gradients near points where previous decoding decisions change
their value. In addition, by using a related approximation, we demonstrate a
similar approach to sampled-based training. Finally, we show that our approach
outperforms cross-entropy training and scheduled sampling procedures in two
sequence prediction tasks: named entity recognition and machine translation.
| 2,017 | Computation and Language |
Learning to Create and Reuse Words in Open-Vocabulary Neural Language
Modeling | Fixed-vocabulary language models fail to account for one of the most
characteristic statistical facts of natural language: the frequent creation and
reuse of new word types. Although character-level language models offer a
partial solution in that they can create word types not attested in the
training corpus, they do not capture the "bursty" distribution of such words.
In this paper, we augment a hierarchical LSTM language model that generates
sequences of word tokens character by character with a caching mechanism that
learns to reuse previously generated words. To validate our model we construct
a new open-vocabulary language modeling corpus (the Multilingual Wikipedia
Corpus, MWC) from comparable Wikipedia articles in 7 typologically diverse
languages and demonstrate the effectiveness of our model across this range of
languages.
| 2,017 | Computation and Language |
Fast and Accurate Neural Word Segmentation for Chinese | Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.
| 2,017 | Computation and Language |
Using Global Constraints and Reranking to Improve Cognates Detection | Global constraints and reranking have not been used in cognates detection
research to date. We propose methods for using global constraints by performing
rescoring of the score matrices produced by state of the art cognates detection
systems. Using global constraints to perform rescoring is complementary to
state of the art methods for performing cognates detection and results in
significant performance improvements beyond current state of the art
performance on publicly available datasets with different language pairs and
various conditions such as different levels of baseline state of the art
performance and different data size conditions, including with more realistic
large data size conditions than have been evaluated with in the past.
| 1,992 | Computation and Language |
Selective Encoding for Abstractive Sentence Summarization | We propose a selective encoding model to extend the sequence-to-sequence
framework for abstractive sentence summarization. It consists of a sentence
encoder, a selective gate network, and an attention equipped decoder. The
sentence encoder and decoder are built with recurrent neural networks. The
selective gate network constructs a second level sentence representation by
controlling the information flow from encoder to decoder. The second level
representation is tailored for sentence summarization task, which leads to
better performance. We evaluate our model on the English Gigaword, DUC 2004 and
MSR abstractive sentence summarization datasets. The experimental results show
that the proposed selective encoding model outperforms the state-of-the-art
baseline models.
| 2,017 | Computation and Language |
Robust Incremental Neural Semantic Graph Parsing | Parsing sentences to linguistically-expressive semantic representations is a
key goal of Natural Language Processing. Yet statistical parsing has focused
almost exclusively on bilexical dependencies or domain-specific logical forms.
We propose a neural encoder-decoder transition-based parser which is the first
full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The
model architecture uses stack-based embedding features, predicting graphs
jointly with unlexicalized predicates and their token alignments. Our parser is
more accurate than attention-based baselines on MRS, and on an additional
Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes
it an order of magnitude faster than a high-precision grammar-based parser.
Further, the 86.69% Smatch score of our MRS parser is higher than the
upper-bound on AMR parsing, making MRS an attractive choice as a semantic
representation.
| 2,017 | Computation and Language |
Being Negative but Constructively: Lessons Learnt from Creating Better
Visual Question Answering Datasets | Visual question answering (Visual QA) has attracted a lot of attention
lately, seen essentially as a form of (visual) Turing test that artificial
intelligence should strive to achieve. In this paper, we study a crucial
component of this task: how can we design good datasets for the task? We focus
on the design of multiple-choice based datasets where the learner has to select
the right answer from a set of candidate ones including the target (\ie the
correct one) and the decoys (\ie the incorrect ones). Through careful analysis
of the results attained by state-of-the-art learning models and human
annotators on existing datasets, we show that the design of the decoy answers
has a significant impact on how and what the learning models learn from the
datasets. In particular, the resulting learner can ignore the visual
information, the question, or both while still doing well on the task. Inspired
by this, we propose automatic procedures to remedy such design deficiencies. We
apply the procedures to re-construct decoy answers for two popular Visual QA
datasets as well as to create a new Visual QA dataset from the Visual Genome
project, resulting in the largest dataset for this task. Extensive empirical
studies show that the design deficiencies have been alleviated in the remedied
datasets and the performance on them is likely a more faithful indicator of the
difference among learning models. The datasets are released and publicly
available via http://www.teds.usc.edu/website_vqa/.
| 2,018 | Computation and Language |
An Analysis of Action Recognition Datasets for Language and Vision Tasks | A large amount of recent research has focused on tasks that combine language
and vision, resulting in a proliferation of datasets and methods. One such task
is action recognition, whose applications include image annotation, scene
under- standing and image retrieval. In this survey, we categorize the existing
ap- proaches based on how they conceptualize this problem and provide a
detailed review of existing datasets, highlighting their di- versity as well as
advantages and disad- vantages. We focus on recently devel- oped datasets which
link visual informa- tion with linguistic resources and provide a fine-grained
syntactic and semantic anal- ysis of actions in images.
| 2,017 | Computation and Language |
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge
Graph Embeddings | We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.
| 2,017 | Computation and Language |
Lexically Constrained Decoding for Sequence Generation Using Grid Beam
Search | We present Grid Beam Search (GBS), an algorithm which extends beam search to
allow the inclusion of pre-specified lexical constraints. The algorithm can be
used with any model that generates a sequence $ \mathbf{\hat{y}} =
\{y_{0}\ldots y_{T}\} $, by maximizing $ p(\mathbf{y} | \mathbf{x}) =
\prod\limits_{t}p(y_{t} | \mathbf{x}; \{y_{0} \ldots y_{t-1}\}) $. Lexical
constraints take the form of phrases or words that must be present in the
output sequence. This is a very general way to incorporate additional knowledge
into a model's output without requiring any modification of the model
parameters or training data. We demonstrate the feasibility and flexibility of
Lexically Constrained Decoding by conducting experiments on Neural
Interactive-Predictive Translation, as well as Domain Adaptation for Neural
Machine Translation. Experiments show that GBS can provide large improvements
in translation quality in interactive scenarios, and that, even without any
user input, GBS can be used to achieve significant gains in performance in
domain adaptation scenarios.
| 2,017 | Computation and Language |
Found in Translation: Reconstructing Phylogenetic Language Trees from
Translations | Translation has played an important role in trade, law, commerce, politics,
and literature for thousands of years. Translators have always tried to be
invisible; ideal translations should look as if they were written originally in
the target language. We show that traces of the source language remain in the
translation product to the extent that it is possible to uncover the history of
the source language by looking only at the translation. Specifically, we
automatically reconstruct phylogenetic language trees from monolingual texts
(translated from several source languages). The signal of the source language
is so powerful that it is retained even after two phases of translation. This
strongly indicates that source language interference is the most dominant
characteristic of translated texts, overshadowing the more subtle signals of
universal properties of translation.
| 2,017 | Computation and Language |
Semi-supervised Multitask Learning for Sequence Labeling | We propose a sequence labeling framework with a secondary training objective,
learning to predict surrounding words for every word in the dataset. This
language modeling objective incentivises the system to learn general-purpose
patterns of semantic and syntactic composition, which are also useful for
improving accuracy on different sequence labeling tasks. The architecture was
evaluated on a range of datasets, covering the tasks of error detection in
learner texts, named entity recognition, chunking and POS-tagging. The novel
language modeling objective provided consistent performance improvements on
every benchmark, without requiring any additional annotated or unannotated
data.
| 2,017 | Computation and Language |
Watset: Automatic Induction of Synsets from a Graph of Synonyms | This paper presents a new graph-based approach that induces synsets using
synonymy dictionaries and word embeddings. First, we build a weighted graph of
synonyms extracted from commonly available resources, such as Wiktionary.
Second, we apply word sense induction to deal with ambiguous words. Finally, we
cluster the disambiguated version of the ambiguous input graph into synsets.
Our meta-clustering approach lets us use an efficient hard clustering algorithm
to perform a fuzzy clustering of the graph. Despite its simplicity, our
approach shows excellent results, outperforming five competitive
state-of-the-art methods in terms of F-score on three gold standard datasets
for English and Russian derived from large-scale manually constructed lexical
resources.
| 2,018 | Computation and Language |
What is the Essence of a Claim? Cross-Domain Claim Identification | Argument mining has become a popular research area in NLP. It typically
includes the identification of argumentative components, e.g. claims, as the
central component of an argument. We perform a qualitative analysis across six
different datasets and show that these appear to conceptualize claims quite
differently. To learn about the consequences of such different
conceptualizations of claim for practical applications, we carried out
extensive experiments using state-of-the-art feature-rich and deep learning
systems, to identify claims in a cross-domain fashion. While the divergent
perception of claims in different datasets is indeed harmful to cross-domain
classification, we show that there are shared properties on the lexical level
as well as system configurations that can help to overcome these gaps.
| 2,022 | Computation and Language |
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance
Classification with Branch-LSTM | This paper describes team Turing's submission to SemEval 2017 RumourEval:
Determining rumour veracity and support for rumours (SemEval 2017 Task 8,
Subtask A). Subtask A addresses the challenge of rumour stance classification,
which involves identifying the attitude of Twitter users towards the
truthfulness of the rumour they are discussing. Stance classification is
considered to be an important step towards rumour verification, therefore
performing well in this task is expected to be useful in debunking false
rumours. In this work we classify a set of Twitter posts discussing rumours
into either supporting, denying, questioning or commenting on the underlying
rumours. We propose a LSTM-based sequential model that, through modelling the
conversational structure of tweets, which achieves an accuracy of 0.784 on the
RumourEval test set outperforming all other systems in Subtask A.
| 2,017 | Computation and Language |
Parsing Speech: A Neural Approach to Integrating Lexical and
Acoustic-Prosodic Information | In conversational speech, the acoustic signal provides cues that help
listeners disambiguate difficult parses. For automatically parsing spoken
utterances, we introduce a model that integrates transcribed text and
acoustic-prosodic features using a convolutional neural network over energy and
pitch trajectories coupled with an attention-based recurrent neural network
that accepts text and prosodic features. We find that different types of
acoustic-prosodic features are individually helpful, and together give
statistically significant improvements in parse and disfluency detection F1
scores over a strong text-only baseline. For this study with known sentence
boundaries, error analyses show that the main benefit of acoustic-prosodic
features is in sentences with disfluencies, attachment decisions are most
improved, and transcription errors obscure gains from prosody.
| 2,018 | Computation and Language |
A Trie-Structured Bayesian Model for Unsupervised Morphological
Segmentation | In this paper, we introduce a trie-structured Bayesian model for unsupervised
morphological segmentation. We adopt prior information from different sources
in the model. We use neural word embeddings to discover words that are
morphologically derived from each other and thereby that are semantically
similar. We use letter successor variety counts obtained from tries that are
built by neural word embeddings. Our results show that using different
information sources such as neural word embeddings and letter successor variety
as prior information improves morphological segmentation in a Bayesian model.
Our model outperforms other unsupervised morphological segmentation models on
Turkish and gives promising results on English and German for scarce resources.
| 2,017 | Computation and Language |
Predicting Native Language from Gaze | A fundamental question in language learning concerns the role of a speaker's
first language in second language acquisition. We present a novel methodology
for studying this question: analysis of eye-movement patterns in second
language reading of free-form text. Using this methodology, we demonstrate for
the first time that the native language of English learners can be predicted
from their gaze fixations when reading English. We provide analysis of
classifier uncertainty and learned features, which indicates that differences
in English reading are likely to be rooted in linguistic divergences across
native languages. The presented framework complements production studies and
offers new ground for advancing research on multilingualism.
| 2,017 | Computation and Language |
Ruminating Reader: Reasoning with Gated Multi-Hop Attention | To answer the question in machine comprehension (MC) task, the models need to
establish the interaction between the question and the context. To tackle the
problem that the single-pass model cannot reflect on and correct its answer, we
present Ruminating Reader. Ruminating Reader adds a second pass of attention
and a novel information fusion component to the Bi-Directional Attention Flow
model (BiDAF). We propose novel layer structures that construct an query-aware
context vector representation and fuse encoding representation with
intermediate representation on top of BiDAF model. We show that a multi-hop
attention mechanism can be applied to a bi-directional attention structure. In
experiments on SQuAD, we find that the Reader outperforms the BiDAF baseline by
a substantial margin, and matches or surpasses the performance of all other
published systems.
| 2,017 | Computation and Language |
Recognizing Descriptive Wikipedia Categories for Historical Figures | Wikipedia is a useful knowledge source that benefits many applications in
language processing and knowledge representation. An important feature of
Wikipedia is that of categories. Wikipedia pages are assigned different
categories according to their contents as human-annotated labels which can be
used in information retrieval, ad hoc search improvements, entity ranking and
tag recommendations. However, important pages are usually assigned too many
categories, which makes it difficult to recognize the most important ones that
give the best descriptions.
In this paper, we propose an approach to recognize the most descriptive
Wikipedia categories. We observe that historical figures in a precise category
presumably are mutually similar and such categorical coherence could be
evaluated via texts or Wikipedia links of corresponding members in the
category. We rank descriptive level of Wikipedia categories according to their
coherence and our ranking yield an overall agreement of 88.27% compared with
human wisdom.
| 2,017 | Computation and Language |
A Challenge Set Approach to Evaluating Machine Translation | Neural machine translation represents an exciting leap forward in translation
quality. But what longstanding weaknesses does it resolve, and which remain? We
address these questions with a challenge set approach to translation evaluation
and error analysis. A challenge set consists of a small set of sentences, each
hand-designed to probe a system's capacity to bridge a particular structural
divergence between languages. To exemplify this approach, we present an
English-French challenge set, and use it to analyze phrase-based and neural
systems. The resulting analysis provides not only a more fine-grained picture
of the strengths of neural systems, but also insight into which linguistic
phenomena remain out of reach.
| 2,017 | Computation and Language |
Detecting English Writing Styles For Non Native Speakers | This paper presents the first attempt, up to our knowledge, to classify
English writing styles on this scale with the challenge of classifying day to
day language written by writers with different backgrounds covering various
areas of topics.The paper proposes simple machine learning algorithms and
simple to generate features to solve hard problems. Relying on the scale of the
data available from large sources of knowledge like Wikipedia. We believe such
sources of data are crucial to generate robust solutions for the web with high
accuracy and easy to deploy in practice. The paper achieves 74\% accuracy
classifying native versus non native speakers writing styles.
Moreover, the paper shows some interesting observations on the similarity
between different languages measured by the similarity of their users English
writing styles. This technique could be used to show some well known facts
about languages as in grouping them into families, which our experiments
support.
| 2,017 | Computation and Language |
Streaming Word Embeddings with the Space-Saving Algorithm | We develop a streaming (one-pass, bounded-memory) word embedding algorithm
based on the canonical skip-gram with negative sampling algorithm implemented
in word2vec. We compare our streaming algorithm to word2vec empirically by
measuring the cosine similarity between word pairs under each algorithm and by
applying each algorithm in the downstream task of hashtag prediction on a
two-month interval of the Twitter sample stream. We then discuss the results of
these experiments, concluding they provide partial validation of our approach
as a streaming replacement for word2vec. Finally, we discuss potential failure
modes and suggest directions for future work.
| 2,017 | Computation and Language |
Multi-Task Video Captioning with Video and Entailment Generation | Video captioning, the task of describing the content of a video, has seen
some promising improvements in recent years with sequence-to-sequence models,
but accurately learning the temporal and logical dynamics involved in the task
still remains a challenge, especially given the lack of sufficient annotated
data. We improve video captioning by sharing knowledge with two related
directed-generation tasks: a temporally-directed unsupervised video prediction
task to learn richer context-aware video encoder representations, and a
logically-directed language entailment generation task to learn better
video-entailed caption decoder representations. For this, we present a
many-to-many multi-task learning model that shares parameters across the
encoders and decoders of the three tasks. We achieve significant improvements
and the new state-of-the-art on several standard video captioning datasets
using diverse automatic and human evaluations. We also show mutual multi-task
improvements on the entailment generation task.
| 2,017 | Computation and Language |
Abstract Syntax Networks for Code Generation and Semantic Parsing | Tasks like code generation and semantic parsing require mapping unstructured
(or partially structured) inputs to well-formed, executable outputs. We
introduce abstract syntax networks, a modeling framework for these problems.
The outputs are represented as abstract syntax trees (ASTs) and constructed by
a decoder with a dynamically-determined modular structure paralleling the
structure of the output tree. On the benchmark Hearthstone dataset for code
generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy,
compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we
perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with
no task-specific engineering.
| 2,017 | Computation and Language |
Adversarial Multi-Criteria Learning for Chinese Word Segmentation | Different linguistic perspectives causes many diverse segmentation criteria
for Chinese word segmentation (CWS). Most existing methods focus on improve the
performance for each single criterion. However, it is interesting to exploit
these different criteria and mining their common underlying knowledge. In this
paper, we propose adversarial multi-criteria learning for CWS by integrating
shared knowledge from multiple heterogeneous segmentation criteria. Experiments
on eight corpora with heterogeneous segmentation criteria show that the
performance of each corpus obtains a significant improvement, compared to
single-criterion learning. Source codes of this paper are available on Github.
| 2,017 | Computation and Language |
Joint POS Tagging and Dependency Parsing with Transition-based Neural
Networks | While part-of-speech (POS) tagging and dependency parsing are observed to be
closely related, existing work on joint modeling with manually crafted feature
templates suffers from the feature sparsity and incompleteness problems. In
this paper, we propose an approach to joint POS tagging and dependency parsing
using transition-based neural networks. Three neural network based classifiers
are designed to resolve shift/reduce, tagging, and labeling conflicts.
Experiments show that our approach significantly outperforms previous methods
for joint POS tagging and dependency parsing across a variety of natural
languages.
| 2,017 | Computation and Language |
280 Birds with One Stone: Inducing Multilingual Taxonomies from
Wikipedia using Character-level Classification | We propose a simple, yet effective, approach towards inducing multilingual
taxonomies from Wikipedia. Given an English taxonomy, our approach leverages
the interlanguage links of Wikipedia followed by character-level classifiers to
induce high-precision, high-coverage taxonomies in other languages. Through
experiments, we demonstrate that our approach significantly outperforms the
state-of-the-art, heuristics-heavy approaches for six languages. As a
consequence of our work, we release presumably the largest and the most
accurate multilingual taxonomic resource spanning over 280 languages.
| 2,017 | Computation and Language |
Fine-Grained Entity Typing with High-Multiplicity Assignments | As entity type systems become richer and more fine-grained, we expect the
number of types assigned to a given entity to increase. However, most
fine-grained typing work has focused on datasets that exhibit a low degree of
type multiplicity. In this paper, we consider the high-multiplicity regime
inherent in data sources such as Wikipedia that have semi-open type systems. We
introduce a set-prediction approach to this problem and show that our model
outperforms unstructured baselines on a new Wikipedia-based fine-grained typing
corpus.
| 2,017 | Computation and Language |
Automatic Compositor Attribution in the First Folio of Shakespeare | Compositor attribution, the clustering of pages in a historical printed
document by the individual who set the type, is a bibliographic task that
relies on analysis of orthographic variation and inspection of visual details
of the printed page. In this paper, we introduce a novel unsupervised model
that jointly describes the textual and visual features needed to distinguish
compositors. Applied to images of Shakespeare's First Folio, our model predicts
attributions that agree with the manual judgements of bibliographers with an
accuracy of 87%, even on text that is the output of OCR.
| 2,017 | Computation and Language |
Other Topics You May Also Agree or Disagree: Modeling Inter-Topic
Preferences using Tweets and Matrix Factorization | We present in this paper our approach for modeling inter-topic preferences of
Twitter users: for example, those who agree with the Trans-Pacific Partnership
(TPP) also agree with free trade. This kind of knowledge is useful not only for
stance detection across multiple topics but also for various real-world
applications including public opinion surveys, electoral predictions, electoral
campaigns, and online debates. In order to extract users' preferences on
Twitter, we design linguistic patterns in which people agree and disagree about
specific topics (e.g., "A is completely wrong"). By applying these linguistic
patterns to a collection of tweets, we extract statements agreeing and
disagreeing with various topics. Inspired by previous work on item
recommendation, we formalize the task of modeling inter-topic preferences as
matrix factorization: representing users' preferences as a user-topic matrix
and mapping both users and topics onto a latent feature space that abstracts
the preferences. Our experimental results demonstrate both that our proposed
approach is useful in predicting missing preferences of users and that the
latent vector representations of topics successfully encode inter-topic
preferences.
| 2,017 | Computation and Language |
Topically Driven Neural Language Model | Language models are typically applied at the sentence level, without access
to the broader document context. We present a neural language model that
incorporates document context in the form of a topic model-like architecture,
thus providing a succinct representation of the broader document context
outside of the current sentence. Experiments over a range of datasets
demonstrate that our model outperforms a pure sentence-based model in terms of
language model perplexity, and leads to topics that are potentially more
coherent than those produced by a standard LDA topic model. Our model also has
the ability to generate related sentences for a topic, providing another way to
interpret topics.
| 2,017 | Computation and Language |
Riemannian Optimization for Skip-Gram Negative Sampling | Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its
implementation in "word2vec" software, is usually optimized by stochastic
gradient descent. However, the optimization of SGNS objective can be viewed as
a problem of searching for a good matrix with the low-rank constraint. The most
standard way to solve this type of problems is to apply Riemannian optimization
framework to optimize the SGNS objective over the manifold of required low-rank
matrices. In this paper, we propose an algorithm that optimizes SGNS objective
using Riemannian optimization and demonstrates its superiority over popular
competitors, such as the original method to train SGNS and SVD over SPPMI
matrix.
| 2,017 | Computation and Language |
Enriching Complex Networks with Word Embeddings for Detecting Mild
Cognitive Impairment from Speech Transcripts | Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose.
Linguistic features, mainly from parsers, have been used to detect MCI, but
this is not suitable for large-scale assessments. MCI disfluencies produce
non-grammatical speech that requires manual or high precision automatic
correction of transcripts. In this paper, we modeled transcripts into complex
networks and enriched them with word embedding (CNE) to better represent short
texts produced in neuropsychological assessments. The network measurements were
applied with well-known classifiers to automatically identify MCI in
transcripts, in a binary classification task. A comparison was made with the
performance of traditional approaches using Bag of Words (BoW) and linguistic
features for three datasets: DementiaBank in English, and Cinderella and
Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using
only complex networks, while Support Vector Machine was superior to other
classifiers. CNE provided the highest accuracies for DementiaBank and
Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably
owing to its short narratives. The approach using linguistic features yielded
higher accuracy if the transcriptions of the Cinderella dataset were manually
revised. Taken together, the results indicate that complex networks enriched
with embedding is promising for detecting MCI in large-scale assessments
| 2,017 | Computation and Language |
A Recurrent Neural Model with Attention for the Recognition of Chinese
Implicit Discourse Relations | We introduce an attention-based Bi-LSTM for Chinese implicit discourse
relations and demonstrate that modeling argument pairs as a joint sequence can
outperform word order-agnostic approaches. Our model benefits from a partial
sampling scheme and is conceptually simple, yet achieves state-of-the-art
performance on the Chinese Discourse Treebank. We also visualize its attention
activity to illustrate the model's ability to selectively focus on the relevant
parts of an input sequence.
| 2,018 | Computation and Language |
Punny Captions: Witty Wordplay in Image Descriptions | Wit is a form of rich interaction that is often grounded in a specific
situation (e.g., a comment in response to an event). In this work, we attempt
to build computational models that can produce witty descriptions for a given
image. Inspired by a cognitive account of humor appreciation, we employ
linguistic wordplay, specifically puns, in image descriptions. We develop two
approaches which involve retrieving witty descriptions for a given image from a
large corpus of sentences, or generating them via an encoder-decoder neural
network architecture. We compare our approach against meaningful baseline
approaches via human studies and show substantial improvements. We find that
when a human is subject to similar constraints as the model regarding word
usage and style, people vote the image descriptions generated by our model to
be slightly wittier than human-written witty descriptions. Unsurprisingly,
humans are almost always wittier than the model when they are free to choose
the vocabulary, style, etc.
| 2,018 | Computation and Language |
Diversity driven Attention Model for Query-based Abstractive
Summarization | Abstractive summarization aims to generate a shorter version of the document
covering all the salient points in a compact and coherent fashion. On the other
hand, query-based summarization highlights those points that are relevant in
the context of a given query. The encode-attend-decode paradigm has achieved
notable success in machine translation, extractive summarization, dialog
systems, etc. But it suffers from the drawback of generation of repeated
phrases. In this work we propose a model for the query-based summarization task
based on the encode-attend-decode paradigm with two key additions (i) a query
attention model (in addition to document attention model) which learns to focus
on different portions of the query at different time steps (instead of using a
static representation for the query) and (ii) a new diversity based attention
model which aims to alleviate the problem of repeating phrases in the summary.
In order to enable the testing of this model we introduce a new query-based
summarization dataset building on debatepedia. Our experiments show that with
these two additions the proposed model clearly outperforms vanilla
encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.
| 2,018 | Computation and Language |
From Characters to Words to in Between: Do We Capture Morphology? | Words can be represented by composing the representations of subword units
such as word segments, characters, and/or character n-grams. While such
representations are effective and may capture the morphological regularities of
words, they have not been systematically compared, and it is not understood how
they interact with different morphological typologies. On a language modeling
task, we present experiments that systematically vary (1) the basic unit of
representation, (2) the composition of these representations, and (3) the
morphological typology of the language modeled. Our results extend previous
findings that character representations are effective across typologies, and we
find that a previously unstudied combination of character trigram
representations composed with bi-LSTMs outperforms most others. But we also
find room for improvement: none of the character-level models match the
predictive accuracy of a model with access to true morphological analyses, even
when learned from an order of magnitude more data.
| 2,017 | Computation and Language |
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation | Sequence-to-sequence models have shown strong performance across a broad
range of applications. However, their application to parsing and generating
text usingAbstract Meaning Representation (AMR)has been limited, due to the
relatively limited amount of labeled data and the non-sequential nature of the
AMR graphs. We present a novel training procedure that can lift this limitation
using millions of unlabeled sentences and careful preprocessing of the AMR
graphs. For AMR parsing, our model achieves competitive results of 62.1SMATCH,
the current best score reported without significant use of external semantic
resources. For AMR generation, our model establishes a new state-of-the-art
performance of BLEU 33.8. We present extensive ablative and qualitative
analysis including strong evidence that sequence-based AMR models are robust
against ordering variations of graph-to-sequence conversions.
| 2,017 | Computation and Language |
Question Answering on Knowledge Bases and Text using Universal Schema
and Memory Networks | Existing question answering methods infer answers either from a knowledge
base or from raw text. While knowledge base (KB) methods are good at answering
compositional questions, their performance is often affected by the
incompleteness of the KB. Au contraire, web text contains millions of facts
that are absent in the KB, however in an unstructured form. {\it Universal
schema} can support reasoning on the union of both structured KBs and
unstructured text by aligning them in a common embedded space. In this paper we
extend universal schema to natural language question answering, employing
\emph{memory networks} to attend to the large body of facts in the combination
of text and KB. Our models can be trained in an end-to-end fashion on
question-answer pairs. Evaluation results on \spades fill-in-the-blank question
answering dataset show that exploiting universal schema for question answering
is better than using either a KB or text alone. This model also outperforms the
current state-of-the-art by 8.5 $F_1$ points.\footnote{Code and data available
in \url{https://rajarshd.github.io/TextKBQA}}
| 2,017 | Computation and Language |
Learning Structured Natural Language Representations for Semantic
Parsing | We introduce a neural semantic parser that converts natural language
utterances to intermediate representations in the form of predicate-argument
structures, which are induced with a transition system and subsequently mapped
to target domains. The semantic parser is trained end-to-end using annotated
logical forms or their denotations. We obtain competitive results on various
datasets. The induced predicate-argument structures shed light on the types of
representations useful for semantic parsing and how these are different from
linguistically motivated ones.
| 2,017 | Computation and Language |
Duluth at Semeval-2017 Task 7 : Puns upon a midnight dreary, Lexical
Semantics for the weak and weary | This paper describes the Duluth systems that participated in SemEval-2017
Task 7 : Detection and Interpretation of English Puns. The Duluth systems
participated in all three subtasks, and relied on methods that included word
sense disambiguation and measures of semantic relatedness.
| 2,017 | Computation and Language |
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection | This paper describes the Duluth system that participated in SemEval-2017 Task
6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks
A and B using N-gram language models, ranking highly in the task evaluation.
This paper discusses the results of our system in the development and
evaluation stages and from two post-evaluation runs.
| 2,017 | Computation and Language |
A GRU-Gated Attention Model for Neural Machine Translation | Neural machine translation (NMT) heavily relies on an attention network to
produce a context vector for each target word prediction. In practice, we find
that context vectors for different target words are quite similar to one
another and therefore are insufficient in discriminatively predicting target
words. The reason for this might be that context vectors produced by the
vanilla attention network are just a weighted sum of source representations
that are invariant to decoder states. In this paper, we propose a novel
GRU-gated attention model (GAtt) for NMT which enhances the degree of
discrimination of context vectors by enabling source representations to be
sensitive to the partial translation generated by the decoder. GAtt uses a
gated recurrent unit (GRU) to combine two types of information: treating a
source annotation vector originally produced by the bidirectional encoder as
the history state while the corresponding previous decoder state as the input
to the GRU. The GRU-combined information forms a new source annotation vector.
In this way, we can obtain translation-sensitive source representations which
are then feed into the attention network to generate discriminative context
vectors. We further propose a variant that regards a source annotation vector
as the current input while the previous decoder state as the history.
Experiments on NIST Chinese-English translation tasks show that both GAtt-based
models achieve significant improvements over the vanilla attentionbased NMT.
Further analyses on attention weights and context vectors demonstrate the
effectiveness of GAtt in improving the discrimination power of representations
and handling the challenging issue of over-translation.
| 2,019 | Computation and Language |
A Survey of Neural Network Techniques for Feature Extraction from Text | This paper aims to catalyze the discussions about text feature extraction
techniques using neural network architectures. The research questions discussed
in the paper focus on the state-of-the-art neural network techniques that have
proven to be useful tools for language processing, language generation, text
classification and other computational linguistics tasks.
| 2,017 | Computation and Language |
Learning a Neural Semantic Parser from User Feedback | We present an approach to rapidly and easily build natural language
interfaces to databases for new domains, whose performance improves over time
based on user feedback, and requires minimal intervention. To achieve this, we
adapt neural sequence models to map utterances directly to SQL with its full
expressivity, bypassing any intermediate meaning representations. These models
are immediately deployed online to solicit feedback from real users to flag
incorrect queries. Finally, the popularity of SQL facilitates gathering
annotations for incorrect predictions using the crowd, which is directly used
to improve our models. This complete feedback loop, without intermediate
representations or database specific engineering, opens up new ways of building
high quality semantic parsers. Experiments suggest that this approach can be
deployed quickly for any new target domain, as we show by learning a semantic
parser for an online academic database from scratch.
| 2,017 | Computation and Language |
Mapping Instructions and Visual Observations to Actions with
Reinforcement Learning | We propose to directly map raw visual observations and text input to actions
for instruction execution. While existing approaches assume access to
structured environment representations or use a pipeline of separately trained
models, we learn a single model to jointly reason about linguistic and visual
input. We use reinforcement learning in a contextual bandit setting to train a
neural network agent. To guide the agent's exploration, we use reward shaping
with different forms of supervision. Our approach does not require intermediate
representations, planning procedures, or training different models. We evaluate
in a simulated environment, and show significant improvements over supervised
learning and common reinforcement learning variants.
| 2,017 | Computation and Language |
Word Affect Intensities | Words often convey affect -- emotions, feelings, and attitudes. Further,
different words can convey affect to various degrees (intensities). However,
existing manually created lexicons for basic emotions (such as anger and fear)
indicate only coarse categories of affect association (for example, associated
with anger or not associated with anger). Automatic lexicons of affect provide
fine degrees of association, but they tend not to be accurate as human-created
lexicons. Here, for the first time, we present a manually created affect
intensity lexicon with real-valued scores of intensity for four basic emotions:
anger, fear, joy, and sadness. (We will subsequently add entries for more
emotions such as disgust, anticipation, trust, and surprise.) We refer to this
dataset as the NRC Affect Intensity Lexicon, or AIL for short. AIL has entries
for close to 6,000 English words. We used a technique called best-worst scaling
(BWS) to create the lexicon. BWS improves annotation consistency and obtains
reliable fine-grained scores (split-half reliability > 0.91). We also compare
the entries in AIL with the entries in the NRC VAD Lexicon, which has valence,
arousal, and dominance (VAD) scores for 20K English words. We find that anger,
fear, and sadness words, on average, have very similar VAD scores. However,
sadness words tend to have slightly lower dominance scores than fear and anger
words. The Affect Intensity Lexicon has applications in automatic emotion
analysis in a number of domains such as commerce, education, intelligence, and
public health. AIL is also useful in the building of natural language
generation systems.
| 2,022 | Computation and Language |
How compatible are our discourse annotations? Insights from mapping
RST-DT and PDTB annotations | Discourse-annotated corpora are an important resource for the community, but
they are often annotated according to different frameworks. This makes
comparison of the annotations difficult, thereby also preventing researchers
from searching the corpora in a unified way, or using all annotated data
jointly to train computational systems. Several theoretical proposals have
recently been made for mapping the relational labels of different frameworks to
each other, but these proposals have so far not been validated against existing
annotations. The two largest discourse relation annotated resources, the Penn
Discourse Treebank and the Rhetorical Structure Theory Discourse Treebank, have
however been annotated on the same text, allowing for a direct comparison of
the annotation layers. We propose a method for automatically aligning the
discourse segments, and then evaluate existing mapping proposals by comparing
the empirically observed against the proposed mappings. Our analysis highlights
the influence of segmentation on subsequent discourse relation labeling, and
shows that while agreement between frameworks is reasonable for explicit
relations, agreement on implicit relations is low. We identify several sources
of systematic discrepancies between the two annotation schemes and discuss
consequences of these discrepancies for future annotation and for the training
of automatic discourse relation labellers.
| 2,018 | Computation and Language |
Past, Present, Future: A Computational Investigation of the Typology of
Tense in 1000 Languages | We present SuperPivot, an analysis method for low-resource languages that
occur in a superparallel corpus, i.e., in a corpus that contains an order of
magnitude more languages than parallel corpora currently in use. We show that
SuperPivot performs well for the crosslingual analysis of the linguistic
phenomenon of tense. We produce analysis results for more than 1000 languages,
conducting - to the best of our knowledge - the largest crosslingual
computational study performed to date. We extend existing methodology for
leveraging parallel corpora for typological analysis by overcoming a limiting
assumption of earlier work: We only require that a linguistic feature is
overtly marked in a few of thousands of languages as opposed to requiring that
it be marked in all languages under investigation.
| 2,017 | Computation and Language |
Neural Word Segmentation with Rich Pretraining | Neural word segmentation research has benefited from large-scale raw texts by
leveraging them for pretraining character and word embeddings. On the other
hand, statistical segmentation research has exploited richer sources of
external information, such as punctuation, automatic segmentation and POS. We
investigate the effectiveness of a range of external training sources for
neural word segmentation by building a modular segmentation model, pretraining
the most important submodule using rich external sources. Results show that
such pretraining significantly improves the model, leading to accuracies
competitive to the best methods on six benchmarks.
| 2,017 | Computation and Language |
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