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Sentence Object Notation: Multilingual sentence notation based on
Wordnet | The representation of sentences is a very important task. It can be used as a
way to exchange data inter-applications. One main characteristic, that a
notation must have, is a minimal size and a representative form. This can
reduce the transfer time, and hopefully the processing time as well.
Usually, sentence representation is associated to the processed language. The
grammar of this language affects how we represent the sentence. To avoid
language-dependent notations, we have to come up with a new representation
which don't use words, but their meanings. This can be done using a lexicon
like wordnet, instead of words we use their synsets. As for syntactic
relations, they have to be universal as much as possible.
Our new notation is called STON "SenTences Object Notation", which somehow
has similarities to JSON. It is meant to be minimal, representative and
language-independent syntactic representation. Also, we want it to be readable
and easy to be created. This simplifies developing simple automatic generators
and creating test banks manually. Its benefit is to be used as a medium between
different parts of applications like: text summarization, language translation,
etc. The notation is based on 4 languages: Arabic, English, Franch and
Japanese; and there are some cases where these languages don't agree on one
representation. Also, given the diversity of grammatical structure of different
world languages, this annotation may fail for some languages which allows more
future improvements.
| 2,018 | Computation and Language |
Social Media Analysis based on Semanticity of Streaming and Batch Data | Languages shared by people differ in different regions based on their
accents, pronunciation and word usages. In this era sharing of language takes
place mainly through social media and blogs. Every second swing of such a micro
posts exist which induces the need of processing those micro posts, in-order to
extract knowledge out of it. Knowledge extraction differs with respect to the
application in which the research on cognitive science fed the necessities for
the same. This work further moves forward such a research by extracting
semantic information of streaming and batch data in applications like Named
Entity Recognition and Author Profiling. In the case of Named Entity
Recognition context of a single micro post has been utilized and context that
lies in the pool of micro posts were utilized to identify the sociolect aspects
of the author of those micro posts. In this work Conditional Random Field has
been utilized to do the entity recognition and a novel approach has been
proposed to find the sociolect aspects of the author (Gender, Age group).
| 2,018 | Computation and Language |
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit | We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP
annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language
processing (NLP) tasks including word segmentation, part-of-speech (POS)
tagging, named entity recognition (NER) and dependency parsing, and obtains
state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to
provide rich linguistic annotations to facilitate research work on Vietnamese
NLP. Our VnCoreNLP is open-source and available at:
https://github.com/vncorenlp/VnCoreNLP
| 2,018 | Computation and Language |
Slugbot: An Application of a Novel and Scalable Open Domain Socialbot
Framework | In this paper we introduce a novel, open domain socialbot for the Amazon
Alexa Prize competition, aimed at carrying on friendly conversations with users
on a variety of topics. We present our modular system, highlighting our
different data sources and how we use the human mind as a model for data
management. Additionally we build and employ natural language understanding and
information retrieval tools and APIs to expand our knowledge bases. We describe
our semistructured, scalable framework for crafting topic-specific dialogue
flows, and give details on our dialogue management schemes and scoring
mechanisms. Finally we briefly evaluate the performance of our system and
observe the challenges that an open domain socialbot faces.
| 2,017 | Computation and Language |
A Multi-task Learning Approach for Improving Product Title Compression
with User Search Log Data | It is a challenging and practical research problem to obtain effective
compression of lengthy product titles for E-commerce. This is particularly
important as more and more users browse mobile E-commerce apps and more
merchants make the original product titles redundant and lengthy for Search
Engine Optimization. Traditional text summarization approaches often require a
large amount of preprocessing costs and do not capture the important issue of
conversion rate in E-commerce. This paper proposes a novel multi-task learning
approach for improving product title compression with user search log data. In
particular, a pointer network-based sequence-to-sequence approach is utilized
for title compression with an attentive mechanism as an extractive method and
an attentive encoder-decoder approach is utilized for generating user search
queries. The encoding parameters (i.e., semantic embedding of original titles)
are shared among the two tasks and the attention distributions are jointly
optimized. An extensive set of experiments with both human annotated data and
online deployment demonstrate the advantage of the proposed research for both
compression qualities and online business values.
| 2,018 | Computation and Language |
Learning Feature Representations for Keyphrase Extraction | In supervised approaches for keyphrase extraction, a candidate phrase is
encoded with a set of hand-crafted features and machine learning algorithms are
trained to discriminate keyphrases from non-keyphrases. Although the
manually-designed features have shown to work well in practice, feature
engineering is a difficult process that requires expert knowledge and normally
does not generalize well. In this paper, we present SurfKE, a feature learning
framework that exploits the text itself to automatically discover patterns that
keyphrases exhibit. Our model represents the document as a graph and
automatically learns feature representation of phrases. The proposed model
obtains remarkable improvements in performance over strong baselines.
| 2,018 | Computation and Language |
Towards Understanding and Answering Multi-Sentence Recommendation
Questions on Tourism | We introduce the first system towards the novel task of answering complex
multisentence recommendation questions in the tourism domain. Our solution uses
a pipeline of two modules: question understanding and answering. For question
understanding, we define an SQL-like query language that captures the semantic
intent of a question; it supports operators like subset, negation, preference
and similarity, which are often found in recommendation questions. We train and
compare traditional CRFs as well as bidirectional LSTM-based models for
converting a question to its semantic representation. We extend these models to
a semisupervised setting with partially labeled sequences gathered through
crowdsourcing. We find that our best model performs semi-supervised training of
BiDiLSTM+CRF with hand-designed features and CCM(Chang et al., 2007)
constraints. Finally, in an end to end QA system, our answering component
converts our question representation into queries fired on underlying knowledge
sources. Our experiments on two different answer corpora demonstrate that our
system can significantly outperform baselines with up to 20 pt higher accuracy
and 17 pt higher recall.
| 2,020 | Computation and Language |
Shielding Google's language toxicity model against adversarial attacks | Lack of moderation in online communities enables participants to incur in
personal aggression, harassment or cyberbullying, issues that have been
accentuated by extremist radicalisation in the contemporary post-truth politics
scenario. This kind of hostility is usually expressed by means of toxic
language, profanity or abusive statements. Recently Google has developed a
machine-learning-based toxicity model in an attempt to assess the hostility of
a comment; unfortunately, it has been suggested that said model can be deceived
by adversarial attacks that manipulate the text sequence of the comment. In
this paper we firstly characterise such adversarial attacks as using
obfuscation and polarity transformations. The former deceives by corrupting
toxic trigger content with typographic edits, whereas the latter deceives by
grammatical negation of the toxic content. Then, we propose a two--stage
approach to counter--attack these anomalies, bulding upon a recently proposed
text deobfuscation method and the toxicity scoring model. Lastly, we conducted
an experiment with approximately 24000 distorted comments, showing how in this
way it is feasible to restore toxicity of the adversarial variants, while
incurring roughly on a twofold increase in processing time. Even though novel
adversary challenges would keep coming up derived from the versatile nature of
written language, we anticipate that techniques combining machine learning and
text pattern recognition methods, each one targeting different layers of
linguistic features, would be needed to achieve robust detection of toxic
language, thus fostering aggression--free digital interaction.
| 2,018 | Computation and Language |
Unsupervised Low-Dimensional Vector Representations for Words, Phrases
and Text that are Transparent, Scalable, and produce Similarity Metrics that
are Complementary to Neural Embeddings | Neural embeddings are a popular set of methods for representing words,
phrases or text as a low dimensional vector (typically 50-500 dimensions).
However, it is difficult to interpret these dimensions in a meaningful manner,
and creating neural embeddings requires extensive training and tuning of
multiple parameters and hyperparameters. We present here a simple unsupervised
method for representing words, phrases or text as a low dimensional vector, in
which the meaning and relative importance of dimensions is transparent to
inspection. We have created a near-comprehensive vector representation of
words, and selected bigrams, trigrams and abbreviations, using the set of
titles and abstracts in PubMed as a corpus. This vector is used to create
several novel implicit word-word and text-text similarity metrics. The implicit
word-word similarity metrics correlate well with human judgement of word pair
similarity and relatedness, and outperform or equal all other reported methods
on a variety of biomedical benchmarks, including several implementations of
neural embeddings trained on PubMed corpora. Our implicit word-word metrics
capture different aspects of word-word relatedness than word2vec-based metrics
and are only partially correlated (rho = ~0.5-0.8 depending on task and
corpus). The vector representations of words, bigrams, trigrams, abbreviations,
and PubMed title+abstracts are all publicly available from
http://arrowsmith.psych.uic.edu for release under CC-BY-NC license. Several
public web query interfaces are also available at the same site, including one
which allows the user to specify a given word and view its most closely related
terms according to direct co-occurrence as well as different implicit
similarity metrics.
| 2,018 | Computation and Language |
Knowledge-based Word Sense Disambiguation using Topic Models | Word Sense Disambiguation is an open problem in Natural Language Processing
which is particularly challenging and useful in the unsupervised setting where
all the words in any given text need to be disambiguated without using any
labeled data. Typically WSD systems use the sentence or a small window of words
around the target word as the context for disambiguation because their
computational complexity scales exponentially with the size of the context. In
this paper, we leverage the formalism of topic model to design a WSD system
that scales linearly with the number of words in the context. As a result, our
system is able to utilize the whole document as the context for a word to be
disambiguated. The proposed method is a variant of Latent Dirichlet Allocation
in which the topic proportions for a document are replaced by synset
proportions. We further utilize the information in the WordNet by assigning a
non-uniform prior to synset distribution over words and a logistic-normal prior
for document distribution over synsets. We evaluate the proposed method on
Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English
All-Word WSD datasets and show that it outperforms the state-of-the-art
unsupervised knowledge-based WSD system by a significant margin.
| 2,018 | Computation and Language |
Using reinforcement learning to learn how to play text-based games | The ability to learn optimal control policies in systems where action space
is defined by sentences in natural language would allow many interesting
real-world applications such as automatic optimisation of dialogue systems.
Text-based games with multiple endings and rewards are a promising platform for
this task, since their feedback allows us to employ reinforcement learning
techniques to jointly learn text representations and control policies. We
present a general text game playing agent, testing its generalisation and
transfer learning performance and showing its ability to play multiple games at
once. We also present pyfiction, an open-source library for universal access to
different text games that could, together with our agent that implements its
interface, serve as a baseline for future research.
| 2,018 | Computation and Language |
Explorations in an English Poetry Corpus: A Neurocognitive Poetics
Perspective | This paper describes a corpus of about 3000 English literary texts with about
250 million words extracted from the Gutenberg project that span a range of
genres from both fiction and non-fiction written by more than 130 authors
(e.g., Darwin, Dickens, Shakespeare). Quantitative Narrative Analysis (QNA) is
used to explore a cleaned subcorpus, the Gutenberg English Poetry Corpus (GEPC)
which comprises over 100 poetic texts with around 2 million words from about 50
authors (e.g., Keats, Joyce, Wordsworth). Some exemplary QNA studies show
author similarities based on latent semantic analysis, significant topics for
each author or various text-analytic metrics for George Eliot's poem 'How Lisa
Loved the King' and James Joyce's 'Chamber Music', concerning e.g. lexical
diversity or sentiment analysis. The GEPC is particularly suited for research
in Digital Humanities, Natural Language Processing or Neurocognitive Poetics,
e.g. as training and test corpus, or for stimulus development and control.
| 2,018 | Computation and Language |
Analysis of Wikipedia-based Corpora for Question Answering | This paper gives comprehensive analyses of corpora based on Wikipedia for
several tasks in question answering. Four recent corpora are collected,WikiQA,
SelQA, SQuAD, and InfoQA, and first analyzed intrinsically by contextual
similarities, question types, and answer categories. These corpora are then
analyzed extrinsically by three question answering tasks, answer retrieval,
selection, and triggering. An indexing-based method for the creation of a
silver-standard dataset for answer retrieval using the entire Wikipedia is also
presented. Our analysis shows the uniqueness of these corpora and suggests a
better use of them for statistical question answering learning.
| 2,018 | Computation and Language |
MIZAN: A Large Persian-English Parallel Corpus | One of the most major and essential tasks in natural language processing is
machine translation that is now highly dependent upon multilingual parallel
corpora. Through this paper, we introduce the biggest Persian-English parallel
corpus with more than one million sentence pairs collected from masterpieces of
literature. We also present acquisition process and statistics of the corpus,
and experiment a base-line statistical machine translation system using the
corpus.
| 2,020 | Computation and Language |
Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment
Identification | Multilingual speakers often switch between languages to express themselves on
social communication platforms. Sometimes, the original script of the language
is preserved, while using a common script for all the languages is quite
popular as well due to convenience. On such occasions, multiple languages are
being mixed with different rules of grammar, using the same script which makes
it a challenging task for natural language processing even in case of accurate
sentiment identification. In this paper, we report results of various
experiments carried out on movie reviews dataset having this code-mixing
property of two languages, English and Bengali, both typed in Roman script. We
have tested various machine learning algorithms trained only on English
features on our code-mixed data and have achieved the maximum accuracy of
59.00% using Naive Bayes (NB) model. We have also tested various models trained
on code-mixed data, as well as English features and the highest accuracy of
72.50% was obtained by a Support Vector Machine (SVM) model. Finally, we have
analyzed the misclassified snippets and have discussed the challenges needed to
be resolved for better accuracy.
| 2,018 | Computation and Language |
Lifelong Learning for Sentiment Classification | This paper proposes a novel lifelong learning (LL) approach to sentiment
classification. LL mimics the human continuous learning process, i.e.,
retaining the knowledge learned from past tasks and use it to help future
learning. In this paper, we first discuss LL in general and then LL for
sentiment classification in particular. The proposed LL approach adopts a
Bayesian optimization framework based on stochastic gradient descent. Our
experimental results show that the proposed method outperforms baseline methods
significantly, which demonstrates that lifelong learning is a promising
research direction.
| 2,015 | Computation and Language |
Denotation Extraction for Interactive Learning in Dialogue Systems | This paper presents a novel task using real user data obtained in
human-machine conversation. The task concerns with denotation extraction from
answer hints collected interactively in a dialogue. The task is motivated by
the need for large amounts of training data for question answering dialogue
system development, where the data is often expensive and hard to collect.
Being able to collect denotation interactively and directly from users, one
could improve, for example, natural understanding components on-line and ease
the collection of the training data. This paper also presents introductory
results of evaluation of several denotation extraction models including
attention-based neural network approaches.
| 2,018 | Computation and Language |
Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using
Attention | The topical stance detection problem addresses detecting the stance of the
text content with respect to a given topic: whether the sentiment of the given
text content is in FAVOR of (positive), is AGAINST (negative), or is NONE
(neutral) towards the given topic. Using the concept of attention, we develop a
two-phase solution. In the first phase, we classify subjectivity - whether a
given tweet is neutral or subjective with respect to the given topic. In the
second phase, we classify sentiment of the subjective tweets (ignoring the
neutral tweets) - whether a given subjective tweet has a FAVOR or AGAINST
stance towards the topic. We propose a Long Short-Term memory (LSTM) based deep
neural network for each phase, and embed attention at each of the phases. On
the SemEval 2016 stance detection Twitter task dataset, we obtain a best-case
macro F-score of 68.84% and a best-case accuracy of 60.2%, outperforming the
existing deep learning based solutions. Our framework, T-PAN, is the first in
the topical stance detection literature, that uses deep learning within a
two-phase architecture.
| 2,018 | Computation and Language |
Translating Pro-Drop Languages with Reconstruction Models | Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. To date, very little attention has been paid to the
dropped pronoun (DP) problem within neural machine translation (NMT). In this
work, we propose a novel reconstruction-based approach to alleviating DP
translation problems for NMT models. Firstly, DPs within all source sentences
are automatically annotated with parallel information extracted from the
bilingual training corpus. Next, the annotated source sentence is reconstructed
from hidden representations in the NMT model. With auxiliary training
objectives, in terms of reconstruction scores, the parameters associated with
the NMT model are guided to produce enhanced hidden representations that are
encouraged as much as possible to embed annotated DP information. Experimental
results on both Chinese-English and Japanese-English dialogue translation tasks
show that the proposed approach significantly and consistently improves
translation performance over a strong NMT baseline, which is directly built on
the training data annotated with DPs.
| 2,018 | Computation and Language |
MilkQA: a Dataset of Consumer Questions for the Task of Answer Selection | We introduce MilkQA, a question answering dataset from the dairy domain
dedicated to the study of consumer questions. The dataset contains 2,657 pairs
of questions and answers, written in the Portuguese language and originally
collected by the Brazilian Agricultural Research Corporation (Embrapa). All
questions were motivated by real situations and written by thousands of authors
with very different backgrounds and levels of literacy, while answers were
elaborated by specialists from Embrapa's customer service. Our dataset was
filtered and anonymized by three human annotators. Consumer questions are a
challenging kind of question that is usually employed as a form of seeking
information. Although several question answering datasets are available, most
of such resources are not suitable for research on answer selection models for
consumer questions. We aim to fill this gap by making MilkQA publicly
available. We study the behavior of four answer selection models on MilkQA: two
baseline models and two convolutional neural network archictetures. Our results
show that MilkQA poses real challenges to computational models, particularly
due to linguistic characteristics of its questions and to their unusually
longer lengths. Only one of the experimented models gives reasonable results,
at the cost of high computational requirements.
| 2,017 | Computation and Language |
Discrete symbolic optimization and Boltzmann sampling by continuous
neural dynamics: Gradient Symbolic Computation | Gradient Symbolic Computation is proposed as a means of solving discrete
global optimization problems using a neurally plausible continuous stochastic
dynamical system. Gradient symbolic dynamics involves two free parameters that
must be adjusted as a function of time to obtain the global maximizer at the
end of the computation. We provide a summary of what is known about the GSC
dynamics for special cases of settings of the parameters, and also establish
that there is a schedule for the two parameters for which convergence to the
correct answer occurs with high probability. These results put the empirical
results already obtained for GSC on a sound theoretical footing.
| 2,018 | Computation and Language |
Group Communication Analysis: A Computational Linguistics Approach for
Detecting Sociocognitive Roles in Multi-Party Interactions | Roles are one of the most important concepts in understanding human
sociocognitive behavior. During group interactions, members take on different
roles within the discussion. Roles have distinct patterns of behavioral
engagement (i.e., active or passive, leading or following), contribution
characteristics (i.e., providing new information or echoing given material),
and social orientation (i.e., individual or group). Different combinations of
these roles can produce characteristically different group outcomes, being
either less or more productive towards collective goals. In online
collaborative learning environments, this can lead to better or worse learning
outcomes for the individual participants. In this study, we propose and
validate a novel approach for detecting emergent roles from the participants'
contributions and patterns of interaction. Specifically, we developed a group
communication analysis (GCA) by combining automated computational linguistic
techniques with analyses of the sequential interactions of online group
communication. The GCA was applied to three large collaborative interaction
datasets (participant N = 2,429; group N = 3,598). Cluster analyses and linear
mixed-effects modeling were used to assess the validity of the GCA approach and
the influence of learner roles on student and group performance. The results
indicate that participants' patterns in linguistic coordination and cohesion
are representative of the roles that individuals play in collaborative
discussions. More broadly, GCA provides a framework for researchers to explore
the micro intra- and interpersonal patterns associated with the participants'
roles and the sociocognitive processes related to successful collaboration.
| 2,018 | Computation and Language |
Unsupervised Part-of-Speech Induction | Part-of-Speech (POS) tagging is an old and fundamental task in natural
language processing. While supervised POS taggers have shown promising
accuracy, it is not always feasible to use supervised methods due to lack of
labeled data. In this project, we attempt to unsurprisingly induce POS tags by
iteratively looking for a recurring pattern of words through a hierarchical
agglomerative clustering process. Our approach shows promising results when
compared to the tagging results of the state-of-the-art unsupervised POS
taggers.
| 2,018 | Computation and Language |
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction | Distant supervised relation extraction is an efficient approach to scale
relation extraction to very large corpora, and has been widely used to find
novel relational facts from plain text. Recent studies on neural relation
extraction have shown great progress on this task via modeling the sentences in
low-dimensional spaces, but seldom considered syntax information to model the
entities. In this paper, we propose to learn syntax-aware entity embedding for
neural relation extraction. First, we encode the context of entities on a
dependency tree as sentence-level entity embedding based on tree-GRU. Then, we
utilize both intra-sentence and inter-sentence attentions to obtain sentence
set-level entity embedding over all sentences containing the focus entity pair.
Finally, we combine both sentence embedding and entity embedding for relation
classification. We conduct experiments on a widely used real-world dataset and
the experimental results show that our model can make full use of all
informative instances and achieve state-of-the-art performance of relation
extraction.
| 2,018 | Computation and Language |
Improved English to Russian Translation by Neural Suffix Prediction | Neural machine translation (NMT) suffers a performance deficiency when a
limited vocabulary fails to cover the source or target side adequately, which
happens frequently when dealing with morphologically rich languages. To address
this problem, previous work focused on adjusting translation granularity or
expanding the vocabulary size. However, morphological information is relatively
under-considered in NMT architectures, which may further improve translation
quality. We propose a novel method, which can not only reduce data sparsity but
also model morphology through a simple but effective mechanism. By predicting
the stem and suffix separately during decoding, our system achieves an
improvement of up to 1.98 BLEU compared with previous work on English to
Russian translation. Our method is orthogonal to different NMT architectures
and stably gains improvements on various domains.
| 2,018 | Computation and Language |
Topic-based Evaluation for Conversational Bots | Dialog evaluation is a challenging problem, especially for non task-oriented
dialogs where conversational success is not well-defined. We propose to
evaluate dialog quality using topic-based metrics that describe the ability of
a conversational bot to sustain coherent and engaging conversations on a topic,
and the diversity of topics that a bot can handle. To detect conversation
topics per utterance, we adopt Deep Average Networks (DAN) and train a topic
classifier on a variety of question and query data categorized into multiple
topics. We propose a novel extension to DAN by adding a topic-word attention
table that allows the system to jointly capture topic keywords in an utterance
and perform topic classification. We compare our proposed topic based metrics
with the ratings provided by users and show that our metrics both correlate
with and complement human judgment. Our analysis is performed on tens of
thousands of real human-bot dialogs from the Alexa Prize competition and
highlights user expectations for conversational bots.
| 2,017 | Computation and Language |
On Evaluating and Comparing Open Domain Dialog Systems | Conversational agents are exploding in popularity. However, much work remains
in the area of non goal-oriented conversations, despite significant growth in
research interest over recent years. To advance the state of the art in
conversational AI, Amazon launched the Alexa Prize, a 2.5-million dollar
university competition where sixteen selected university teams built
conversational agents to deliver the best social conversational experience.
Alexa Prize provided the academic community with the unique opportunity to
perform research with a live system used by millions of users. The subjectivity
associated with evaluating conversations is key element underlying the
challenge of building non-goal oriented dialogue systems. In this paper, we
propose a comprehensive evaluation strategy with multiple metrics designed to
reduce subjectivity by selecting metrics which correlate well with human
judgement. The proposed metrics provide granular analysis of the conversational
agents, which is not captured in human ratings. We show that these metrics can
be used as a reasonable proxy for human judgment. We provide a mechanism to
unify the metrics for selecting the top performing agents, which has also been
applied throughout the Alexa Prize competition. To our knowledge, to date it is
the largest setting for evaluating agents with millions of conversations and
hundreds of thousands of ratings from users. We believe that this work is a
step towards an automatic evaluation process for conversational AIs.
| 2,017 | Computation and Language |
Stochastic Learning of Nonstationary Kernels for Natural Language
Modeling | Natural language processing often involves computations with semantic or
syntactic graphs to facilitate sophisticated reasoning based on structural
relationships. While convolution kernels provide a powerful tool for comparing
graph structure based on node (word) level relationships, they are difficult to
customize and can be computationally expensive. We propose a generalization of
convolution kernels, with a nonstationary model, for better expressibility of
natural languages in supervised settings. For a scalable learning of the
parameters introduced with our model, we propose a novel algorithm that
leverages stochastic sampling on k-nearest neighbor graphs, along with
approximations based on locality-sensitive hashing. We demonstrate the
advantages of our approach on a challenging real-world (structured inference)
problem of automatically extracting biological models from the text of
scientific papers.
| 2,018 | Computation and Language |
Did William Shakespeare and Thomas Kyd Write Edward III? | William Shakespeare is believed to be a significant author in the anonymous
play, The Reign of King Edward III, published in 1596. However, recently,
Thomas Kyd, has been suggested as the primary author. Using a neurolinguistics
approach to authorship identification we use a four-feature technique, RPAS, to
convert the 19 scenes in Edward III into a multi-dimensional vector. Three
complementary analytical techniques are applied to cluster the data and reduce
single technique bias before an alternate method, seriation, is used to measure
the distances between clusters and test the strength of the connections. We
find the multivariate techniques robust and are able to allocate up to 14
scenes to Thomas Kyd, and further question if scenes long believed to be
Shakespeare's are not his.
| 2,017 | Computation and Language |
Black-box Generation of Adversarial Text Sequences to Evade Deep
Learning Classifiers | Although various techniques have been proposed to generate adversarial
samples for white-box attacks on text, little attention has been paid to
black-box attacks, which are more realistic scenarios. In this paper, we
present a novel algorithm, DeepWordBug, to effectively generate small text
perturbations in a black-box setting that forces a deep-learning classifier to
misclassify a text input. We employ novel scoring strategies to identify the
critical tokens that, if modified, cause the classifier to make an incorrect
prediction. Simple character-level transformations are applied to the
highest-ranked tokens in order to minimize the edit distance of the
perturbation, yet change the original classification. We evaluated DeepWordBug
on eight real-world text datasets, including text classification, sentiment
analysis, and spam detection. We compare the result of DeepWordBug with two
baselines: Random (Black-box) and Gradient (White-box). Our experimental
results indicate that DeepWordBug reduces the prediction accuracy of current
state-of-the-art deep-learning models, including a decrease of 68\% on average
for a Word-LSTM model and 48\% on average for a Char-CNN model.
| 2,018 | Computation and Language |
Detecting Offensive Language in Tweets Using Deep Learning | This paper addresses the important problem of discerning hateful content in
social media. We propose a detection scheme that is an ensemble of Recurrent
Neural Network (RNN) classifiers, and it incorporates various features
associated with user-related information, such as the users' tendency towards
racism or sexism. These data are fed as input to the above classifiers along
with the word frequency vectors derived from the textual content. Our approach
has been evaluated on a publicly available corpus of 16k tweets, and the
results demonstrate its effectiveness in comparison to existing state of the
art solutions. More specifically, our scheme can successfully distinguish
racism and sexism messages from normal text, and achieve higher classification
quality than current state-of-the-art algorithms.
| 2,018 | Computation and Language |
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings | Keyphrase extraction is the task of automatically selecting a small set of
phrases that best describe a given free text document. Supervised keyphrase
extraction requires large amounts of labeled training data and generalizes very
poorly outside the domain of the training data. At the same time, unsupervised
systems have poor accuracy, and often do not generalize well, as they require
the input document to belong to a larger corpus also given as input. Addressing
these drawbacks, in this paper, we tackle keyphrase extraction from single
documents with EmbedRank: a novel unsupervised method, that leverages sentence
embeddings. EmbedRank achieves higher F-scores than graph-based state of the
art systems on standard datasets and is suitable for real-time processing of
large amounts of Web data. With EmbedRank, we also explicitly increase coverage
and diversity among the selected keyphrases by introducing an embedding-based
maximal marginal relevance (MMR) for new phrases. A user study including over
200 votes showed that, although reducing the phrases' semantic overlap leads to
no gains in F-score, our high diversity selection is preferred by humans.
| 2,018 | Computation and Language |
An Interpretable Reasoning Network for Multi-Relation Question Answering | Multi-relation Question Answering is a challenging task, due to the
requirement of elaborated analysis on questions and reasoning over multiple
fact triples in knowledge base. In this paper, we present a novel model called
Interpretable Reasoning Network that employs an interpretable, hop-by-hop
reasoning process for question answering. The model dynamically decides which
part of an input question should be analyzed at each hop; predicts a relation
that corresponds to the current parsed results; utilizes the predicted relation
to update the question representation and the state of the reasoning process;
and then drives the next-hop reasoning. Experiments show that our model yields
state-of-the-art results on two datasets. More interestingly, the model can
offer traceable and observable intermediate predictions for reasoning analysis
and failure diagnosis, thereby allowing manual manipulation in predicting the
final answer.
| 2,018 | Computation and Language |
Predicting Movie Genres Based on Plot Summaries | This project explores several Machine Learning methods to predict movie
genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent
Neural Networks are used for text classification, while K-binary
transformation, rank method and probabilistic classification with learned
probability threshold are employed for the multi-label problem involved in the
genre tagging task.Experiments with more than 250,000 movies show that
employing the Gated Recurrent Units (GRU) neural networks for the probabilistic
classification with learned probability threshold approach achieves the best
result on the test set. The model attains a Jaccard Index of 50.0%, a F-score
of 0.56, and a hit rate of 80.5%.
| 2,018 | Computation and Language |
Topic Modeling on Health Journals with Regularized Variational Inference | Topic modeling enables exploration and compact representation of a corpus.
The CaringBridge (CB) dataset is a massive collection of journals written by
patients and caregivers during a health crisis. Topic modeling on the CB
dataset, however, is challenging due to the asynchronous nature of multiple
authors writing about their health journeys. To overcome this challenge we
introduce the Dynamic Author-Persona topic model (DAP), a probabilistic
graphical model designed for temporal corpora with multiple authors. The
novelty of the DAP model lies in its representation of authors by a persona ---
where personas capture the propensity to write about certain topics over time.
Further, we present a regularized variational inference algorithm, which we use
to encourage the DAP model's personas to be distinct. Our results show
significant improvements over competing topic models --- particularly after
regularization, and highlight the DAP model's unique ability to capture common
journeys shared by different authors.
| 2,018 | Computation and Language |
What Level of Quality can Neural Machine Translation Attain on Literary
Text? | Given the rise of a new approach to MT, Neural MT (NMT), and its promising
performance on different text types, we assess the translation quality it can
attain on what is perceived to be the greatest challenge for MT: literary text.
Specifically, we target novels, arguably the most popular type of literary
text. We build a literary-adapted NMT system for the English-to-Catalan
translation direction and evaluate it against a system pertaining to the
previous dominant paradigm in MT: statistical phrase-based MT (PBSMT). To this
end, for the first time we train MT systems, both NMT and PBSMT, on large
amounts of literary text (over 100 million words) and evaluate them on a set of
twelve widely known novels spanning from the the 1920s to the present day.
According to the BLEU automatic evaluation metric, NMT is significantly better
than PBSMT (p < 0.01) on all the novels considered. Overall, NMT results in a
11% relative improvement (3 points absolute) over PBSMT. A complementary human
evaluation on three of the books shows that between 17% and 34% of the
translations, depending on the book, produced by NMT (versus 8% and 20% with
PBSMT) are perceived by native speakers of the target language to be of
equivalent quality to translations produced by a professional human translator.
| 2,018 | Computation and Language |
AliMe Assist: An Intelligent Assistant for Creating an Innovative
E-commerce Experience | We present AliMe Assist, an intelligent assistant designed for creating an
innovative online shopping experience in E-commerce. Based on question
answering (QA), AliMe Assist offers assistance service, customer service, and
chatting service. It is able to take voice and text input, incorporate context
to QA, and support multi-round interaction. Currently, it serves millions of
customer questions per day and is able to address 85% of them. In this paper,
we demonstrate the system, present the underlying techniques, and share our
experience in dealing with real-world QA in the E-commerce field.
| 2,018 | Computation and Language |
Real-time Road Traffic Information Detection Through Social Media | In current study, a mechanism to extract traffic related information such as
congestion and incidents from textual data from the internet is proposed. The
current source of data is Twitter. As the data being considered is extremely
large in size automated models are developed to stream, download, and mine the
data in real-time. Furthermore, if any tweet has traffic related information
then the models should be able to infer and extract this data.
Currently, the data is collected only for United States and a total of
120,000 geo-tagged traffic related tweets are extracted, while six million
geo-tagged non-traffic related tweets are retrieved and classification models
are trained. Furthermore, this data is used for various kinds of spatial and
temporal analysis. A mechanism to calculate level of traffic congestion,
safety, and traffic perception for cities in U.S. is proposed. Traffic
congestion and safety rankings for the various urban areas are obtained and
then they are statistically validated with existing widely adopted rankings.
Traffic perception depicts the attitude and perception of people towards the
traffic.
It is also seen that traffic related data when visualized spatially and
temporally provides the same pattern as the actual traffic flows for various
urban areas. When visualized at the city level, it is clearly visible that the
flow of tweets is similar to flow of vehicles and that the traffic related
tweets are representative of traffic within the cities. With all the findings
in current study, it is shown that significant amount of traffic related
information can be extracted from Twitter and other sources on internet.
Furthermore, Twitter and these data sources are freely available and are not
bound by spatial and temporal limitations. That is, wherever there is a user
there is a potential for data.
| 2,018 | Computation and Language |
Variational Recurrent Neural Machine Translation | Partially inspired by successful applications of variational recurrent neural
networks, we propose a novel variational recurrent neural machine translation
(VRNMT) model in this paper. Different from the variational NMT, VRNMT
introduces a series of latent random variables to model the translation
procedure of a sentence in a generative way, instead of a single latent
variable. Specifically, the latent random variables are included into the
hidden states of the NMT decoder with elements from the variational
autoencoder. In this way, these variables are recurrently generated, which
enables them to further capture strong and complex dependencies among the
output translations at different timesteps. In order to deal with the
challenges in performing efficient posterior inference and large-scale training
during the incorporation of latent variables, we build a neural posterior
approximator, and equip it with a reparameterization technique to estimate the
variational lower bound. Experiments on Chinese-English and English-German
translation tasks demonstrate that the proposed model achieves significant
improvements over both the conventional and variational NMT models.
| 2,018 | Computation and Language |
Asynchronous Bidirectional Decoding for Neural Machine Translation | The dominant neural machine translation (NMT) models apply unified
attentional encoder-decoder neural networks for translation. Traditionally, the
NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a
left-toright manner, leaving the target-side contexts generated from right to
left unexploited during translation. In this paper, we equip the conventional
attentional encoder-decoder NMT framework with a backward decoder, in order to
explore bidirectional decoding for NMT. Attending to the hidden state sequence
produced by the encoder, our backward decoder first learns to generate the
target-side hidden state sequence from right to left. Then, the forward decoder
performs translation in the forward direction, while in each translation
prediction timestep, it simultaneously applies two attention models to consider
the source-side and reverse target-side hidden states, respectively. With this
new architecture, our model is able to fully exploit source- and target-side
contexts to improve translation quality altogether. Experimental results on
NIST Chinese-English and WMT English-German translation tasks demonstrate that
our model achieves substantial improvements over the conventional NMT by 3.14
and 1.38 BLEU points, respectively. The source code of this work can be
obtained from https://github.com/DeepLearnXMU/ABDNMT.
| 2,018 | Computation and Language |
Adversarial Learning for Chinese NER from Crowd Annotations | To quickly obtain new labeled data, we can choose crowdsourcing as an
alternative way at lower cost in a short time. But as an exchange, crowd
annotations from non-experts may be of lower quality than those from experts.
In this paper, we propose an approach to performing crowd annotation learning
for Chinese Named Entity Recognition (NER) to make full use of the noisy
sequence labels from multiple annotators. Inspired by adversarial learning, our
approach uses a common Bi-LSTM and a private Bi-LSTM for representing
annotator-generic and -specific information. The annotator-generic information
is the common knowledge for entities easily mastered by the crowd. Finally, we
build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we
create two data sets for Chinese NER tasks from two domains. The experimental
results show that our system achieves better scores than strong baseline
systems.
| 2,018 | Computation and Language |
OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language
Understanding | In practice, most spoken language understanding systems process user input in
a pipelined manner; first domain is predicted, then intent and semantic slots
are inferred according to the semantic frames of the predicted domain. The
pipeline approach, however, has some disadvantages: error propagation and lack
of information sharing. To address these issues, we present a unified neural
network that jointly performs domain, intent, and slot predictions. Our
approach adopts a principled architecture for multitask learning to fold in the
state-of-the-art models for each task. With a few more ingredients, e.g.
orthography-sensitive input encoding and curriculum training, our model
delivered significant improvements in all three tasks across all domains over
strong baselines, including one using oracle prediction for domain detection,
on real user data of a commercial personal assistant.
| 2,018 | Computation and Language |
Beyond Word Importance: Contextual Decomposition to Extract Interactions
from LSTMs | The driving force behind the recent success of LSTMs has been their ability
to learn complex and non-linear relationships. Consequently, our inability to
describe these relationships has led to LSTMs being characterized as black
boxes. To this end, we introduce contextual decomposition (CD), an
interpretation algorithm for analysing individual predictions made by standard
LSTMs, without any changes to the underlying model. By decomposing the output
of a LSTM, CD captures the contributions of combinations of words or variables
to the final prediction of an LSTM. On the task of sentiment analysis with the
Yelp and SST data sets, we show that CD is able to reliably identify words and
phrases of contrasting sentiment, and how they are combined to yield the LSTM's
final prediction. Using the phrase-level labels in SST, we also demonstrate
that CD is able to successfully extract positive and negative negations from an
LSTM, something which has not previously been done.
| 2,018 | Computation and Language |
Automatic Detection of Cyberbullying in Social Media Text | While social media offer great communication opportunities, they also
increase the vulnerability of young people to threatening situations online.
Recent studies report that cyberbullying constitutes a growing problem among
youngsters. Successful prevention depends on the adequate detection of
potentially harmful messages and the information overload on the Web requires
intelligent systems to identify potential risks automatically. The focus of
this paper is on automatic cyberbullying detection in social media text by
modelling posts written by bullies, victims, and bystanders of online bullying.
We describe the collection and fine-grained annotation of a training corpus for
English and Dutch and perform a series of binary classification experiments to
determine the feasibility of automatic cyberbullying detection. We make use of
linear support vector machines exploiting a rich feature set and investigate
which information sources contribute the most for this particular task.
Experiments on a holdout test set reveal promising results for the detection of
cyberbullying-related posts. After optimisation of the hyperparameters, the
classifier yields an F1-score of 64% and 61% for English and Dutch
respectively, and considerably outperforms baseline systems based on keywords
and word unigrams.
| 2,020 | Computation and Language |
Natural Language Multitasking: Analyzing and Improving Syntactic
Saliency of Hidden Representations | We train multi-task autoencoders on linguistic tasks and analyze the learned
hidden sentence representations. The representations change significantly when
translation and part-of-speech decoders are added. The more decoders a model
employs, the better it clusters sentences according to their syntactic
similarity, as the representation space becomes less entangled. We explore the
structure of the representation space by interpolating between sentences, which
yields interesting pseudo-English sentences, many of which have recognizable
syntactic structure. Lastly, we point out an interesting property of our
models: The difference-vector between two sentences can be added to change a
third sentence with similar features in a meaningful way.
| 2,018 | Computation and Language |
Universal Language Model Fine-tuning for Text Classification | Inductive transfer learning has greatly impacted computer vision, but
existing approaches in NLP still require task-specific modifications and
training from scratch. We propose Universal Language Model Fine-tuning
(ULMFiT), an effective transfer learning method that can be applied to any task
in NLP, and introduce techniques that are key for fine-tuning a language model.
Our method significantly outperforms the state-of-the-art on six text
classification tasks, reducing the error by 18-24% on the majority of datasets.
Furthermore, with only 100 labeled examples, it matches the performance of
training from scratch on 100x more data. We open-source our pretrained models
and code.
| 2,018 | Computation and Language |
Contextual and Position-Aware Factorization Machines for Sentiment
Classification | While existing machine learning models have achieved great success for
sentiment classification, they typically do not explicitly capture
sentiment-oriented word interaction, which can lead to poor results for
fine-grained analysis at the snippet level (a phrase or sentence).
Factorization Machine provides a possible approach to learning element-wise
interaction for recommender systems, but they are not directly applicable to
our task due to the inability to model contexts and word sequences. In this
work, we develop two Position-aware Factorization Machines which consider word
interaction, context and position information. Such information is jointly
encoded in a set of sentiment-oriented word interaction vectors. Compared to
traditional word embeddings, SWI vectors explicitly capture sentiment-oriented
word interaction and simplify the parameter learning. Experimental results show
that while they have comparable performance with state-of-the-art methods for
document-level classification, they benefit the snippet/sentence-level
sentiment analysis.
| 2,018 | Computation and Language |
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy
Learning | Training a task-completion dialogue agent via reinforcement learning (RL) is
costly because it requires many interactions with real users. One common
alternative is to use a user simulator. However, a user simulator usually lacks
the language complexity of human interlocutors and the biases in its design may
tend to degrade the agent. To address these issues, we present Deep Dyna-Q,
which to our knowledge is the first deep RL framework that integrates planning
for task-completion dialogue policy learning. We incorporate into the dialogue
agent a model of the environment, referred to as the world model, to mimic real
user response and generate simulated experience. During dialogue policy
learning, the world model is constantly updated with real user experience to
approach real user behavior, and in turn, the dialogue agent is optimized using
both real experience and simulated experience. The effectiveness of our
approach is demonstrated on a movie-ticket booking task in both simulated and
human-in-the-loop settings.
| 2,018 | Computation and Language |
Investigating the Working of Text Classifiers | Text classification is one of the most widely studied tasks in natural
language processing. Motivated by the principle of compositionality, large
multilayer neural network models have been employed for this task in an attempt
to effectively utilize the constituent expressions. Almost all of the reported
work train large networks using discriminative approaches, which come with a
caveat of no proper capacity control, as they tend to latch on to any signal
that may not generalize. Using various recent state-of-the-art approaches for
text classification, we explore whether these models actually learn to compose
the meaning of the sentences or still just focus on some keywords or lexicons
for classifying the document. To test our hypothesis, we carefully construct
datasets where the training and test splits have no direct overlap of such
lexicons, but overall language structure would be similar. We study various
text classifiers and observe that there is a big performance drop on these
datasets. Finally, we show that even simple models with our proposed
regularization techniques, which disincentivize focusing on key lexicons, can
substantially improve classification accuracy.
| 2,018 | Computation and Language |
Size vs. Structure in Training Corpora for Word Embedding Models:
Araneum Russicum Maximum and Russian National Corpus | In this paper, we present a distributional word embedding model trained on
one of the largest available Russian corpora: Araneum Russicum Maximum (over 10
billion words crawled from the web). We compare this model to the model trained
on the Russian National Corpus (RNC). The two corpora are much different in
their size and compilation procedures. We test these differences by evaluating
the trained models against the Russian part of the Multilingual SimLex999
semantic similarity dataset. We detect and describe numerous issues in this
dataset and publish a new corrected version. Aside from the already known fact
that the RNC is generally a better training corpus than web corpora, we
enumerate and explain fine differences in how the models process semantic
similarity task, what parts of the evaluation set are difficult for particular
models and why. Additionally, the learning curves for both models are
described, showing that the RNC is generally more robust as training material
for this task.
| 2,017 | Computation and Language |
Evaluating neural network explanation methods using hybrid documents and
morphological agreement | The behavior of deep neural networks (DNNs) is hard to understand. This makes
it necessary to explore post hoc explanation methods. We conduct the first
comprehensive evaluation of explanation methods for NLP. To this end, we design
two novel evaluation paradigms that cover two important classes of NLP
problems: small context and large context problems. Both paradigms require no
manual annotation and are therefore broadly applicable. We also introduce
LIMSSE, an explanation method inspired by LIME that is designed for NLP. We
show empirically that LIMSSE, LRP and DeepLIFT are the most effective
explanation methods and recommend them for explaining DNNs in NLP.
| 2,019 | Computation and Language |
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity | Recognizing semantically similar sentences or paragraphs across languages is
beneficial for many tasks, ranging from cross-lingual information retrieval and
plagiarism detection to machine translation. Recently proposed methods for
predicting cross-lingual semantic similarity of short texts, however, make use
of tools and resources (e.g., machine translation systems, syntactic parsers or
named entity recognition) that for many languages (or language pairs) do not
exist. In contrast, we propose an unsupervised and a very resource-light
approach for measuring semantic similarity between texts in different
languages. To operate in the bilingual (or multilingual) space, we project
continuous word vectors (i.e., word embeddings) from one language to the vector
space of the other language via the linear translation model. We then align
words according to the similarity of their vectors in the bilingual embedding
space and investigate different unsupervised measures of semantic similarity
exploiting bilingual embeddings and word alignments. Requiring only a
limited-size set of word translation pairs between the languages, the proposed
approach is applicable to virtually any pair of languages for which there
exists a sufficiently large corpus, required to learn monolingual word
embeddings. Experimental results on three different datasets for measuring
semantic textual similarity show that our simple resource-light approach
reaches performance close to that of supervised and resource intensive methods,
displaying stability across different language pairs. Furthermore, we evaluate
the proposed method on two extrinsic tasks, namely extraction of parallel
sentences from comparable corpora and cross lingual plagiarism detection, and
show that it yields performance comparable to those of complex
resource-intensive state-of-the-art models for the respective tasks.
| 2,018 | Computation and Language |
A Practitioners' Guide to Transfer Learning for Text Classification
using Convolutional Neural Networks | Transfer Learning (TL) plays a crucial role when a given dataset has
insufficient labeled examples to train an accurate model. In such scenarios,
the knowledge accumulated within a model pre-trained on a source dataset can be
transferred to a target dataset, resulting in the improvement of the target
model. Though TL is found to be successful in the realm of image-based
applications, its impact and practical use in Natural Language Processing (NLP)
applications is still a subject of research. Due to their hierarchical
architecture, Deep Neural Networks (DNN) provide flexibility and customization
in adjusting their parameters and depth of layers, thereby forming an apt area
for exploiting the use of TL. In this paper, we report the results and
conclusions obtained from extensive empirical experiments using a Convolutional
Neural Network (CNN) and try to uncover thumb rules to ensure a successful
positive transfer. In addition, we also highlight the flawed means that could
lead to a negative transfer. We explore the transferability of various layers
and describe the effect of varying hyper-parameters on the transfer
performance. Also, we present a comparison of accuracy value and model size
against state-of-the-art methods. Finally, we derive inferences from the
empirical results and provide best practices to achieve a successful positive
transfer.
| 2,018 | Computation and Language |
Efficient Text Classification Using Tree-structured Multi-linear
Principal Component Analysis | A novel text data dimension reduction technique, called the tree-structured
multi-linear principal component anal- ysis (TMPCA), is proposed in this work.
Being different from traditional text dimension reduction methods that deal
with the word-level representation, the TMPCA technique reduces the dimension
of input sequences and sentences to simplify the following text classification
tasks. It is shown mathematically and experimentally that the TMPCA tool
demands much lower complexity (and, hence, less computing power) than the
ordinary principal component analysis (PCA). Furthermore, it is demon- strated
by experimental results that the support vector machine (SVM) method applied to
the TMPCA-processed data achieves commensurable or better performance than the
state-of-the-art recurrent neural network (RNN) approach.
| 2,018 | Computation and Language |
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text | Web 2.0 has brought with it numerous user-produced data revealing one's
thoughts, experiences, and knowledge, which are a great source for many tasks,
such as information extraction, and knowledge base construction. However, the
colloquial nature of the texts poses new challenges for current natural
language processing techniques, which are more adapt to the formal form of the
language. Ellipsis is a common linguistic phenomenon that some words are left
out as they are understood from the context, especially in oral utterance,
hindering the improvement of dependency parsing, which is of great importance
for tasks relied on the meaning of the sentence. In order to promote research
in this area, we are releasing a Chinese dependency treebank of 319 weibos,
containing 572 sentences with omissions restored and contexts reserved.
| 2,018 | Computation and Language |
A Deep Reinforcement Learning Chatbot (Short Version) | We present MILABOT: a deep reinforcement learning chatbot developed by the
Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
competition. MILABOT is capable of conversing with humans on popular small talk
topics through both speech and text. The system consists of an ensemble of
natural language generation and retrieval models, including neural network and
template-based models. By applying reinforcement learning to crowdsourced data
and real-world user interactions, the system has been trained to select an
appropriate response from the models in its ensemble. The system has been
evaluated through A/B testing with real-world users, where it performed
significantly better than other systems. The results highlight the potential of
coupling ensemble systems with deep reinforcement learning as a fruitful path
for developing real-world, open-domain conversational agents.
| 2,018 | Computation and Language |
Attentive Recurrent Tensor Model for Community Question Answering | A major challenge to the problem of community question answering is the
lexical and semantic gap between the sentence representations. Some solutions
to minimize this gap includes the introduction of extra parameters to deep
models or augmenting the external handcrafted features. In this paper, we
propose a novel attentive recurrent tensor network for solving the lexical and
semantic gap in community question answering. We introduce token-level and
phrase-level attention strategy that maps input sequences to the output using
trainable parameters. Further, we use the tensor parameters to introduce a
3-way interaction between question, answer and external features in vector
space. We introduce simplified tensor matrices with L2 regularization that
results in smooth optimization during training. The proposed model achieves
state-of-the-art performance on the task of answer sentence selection (TrecQA
and WikiQA datasets) while outperforming the current state-of-the-art on the
tasks of best answer selection (Yahoo! L4) and answer triggering task (WikiQA).
| 2,018 | Computation and Language |
Embedding Learning Through Multilingual Concept Induction | We present a new method for estimating vector space representations of words:
embedding learning by concept induction. We test this method on a highly
parallel corpus and learn semantic representations of words in 1259 different
languages in a single common space. An extensive experimental evaluation on
crosslingual word similarity and sentiment analysis indicates that
concept-based multilingual embedding learning performs better than previous
approaches.
| 2,018 | Computation and Language |
Neural Multi-task Learning in Automated Assessment | Grammatical error detection and automated essay scoring are two tasks in the
area of automated assessment. Traditionally these tasks have been treated
independently with different machine learning models and features used for each
task. In this paper, we develop a multi-task neural network model that jointly
optimises for both tasks, and in particular we show that neural automated essay
scoring can be significantly improved. We show that while the essay score
provides little evidence to inform grammatical error detection, the essay score
is highly influenced by error detection.
| 2,018 | Computation and Language |
BiographyNet: Extracting Relations Between People and Events | This paper describes BiographyNet, a digital humanities project (2012-2016)
that brings together researchers from history, computational linguistics and
computer science. The project uses data from the Biography Portal of the
Netherlands (BPN), which contains approximately 125,000 biographies from a
variety of Dutch biographical dictionaries from the eighteenth century until
now, describing around 76,000 individuals. BiographyNet's aim is to strengthen
the value of the portal and comparable biographical datasets for historical
research, by improving the search options and the presentation of its outcome,
with a historically justified NLP pipeline that works through a user evaluated
demonstrator. The project's main target group are professional historians. The
project therefore worked with two key concepts: "provenance" -understood as a
term allowing for both historical source criticism and for references to
data-management and programming interventions in digitized sources; and
"perspective" interpreted as inherent uncertainty concerning the interpretation
of historical results.
| 2,018 | Computation and Language |
Unsupervised Open Relation Extraction | We explore methods to extract relations between named entities from free text
in an unsupervised setting. In addition to standard feature extraction, we
develop a novel method to re-weight word embeddings. We alleviate the problem
of features sparsity using an individual feature reduction. Our approach
exhibits a significant improvement by 5.8% over the state-of-the-art relation
clustering scoring a F1-score of 0.416 on the NYT-FB dataset.
| 2,018 | Computation and Language |
Adversarial Texts with Gradient Methods | Adversarial samples for images have been extensively studied in the
literature. Among many of the attacking methods, gradient-based methods are
both effective and easy to compute. In this work, we propose a framework to
adapt the gradient attacking methods on images to text domain. The main
difficulties for generating adversarial texts with gradient methods are i) the
input space is discrete, which makes it difficult to accumulate small noise
directly in the inputs, and ii) the measurement of the quality of the
adversarial texts is difficult. We tackle the first problem by searching for
adversarials in the embedding space and then reconstruct the adversarial texts
via nearest neighbor search. For the latter problem, we employ the Word Mover's
Distance (WMD) to quantify the quality of adversarial texts. Through extensive
experiments on three datasets, IMDB movie reviews, Reuters-2 and Reuters-5
newswires, we show that our framework can leverage gradient attacking methods
to generate very high-quality adversarial texts that are only a few words
different from the original texts. There are many cases where we can change one
word to alter the label of the whole piece of text. We successfully incorporate
FGM and DeepFool into our framework. In addition, we empirically show that WMD
is closely related to the quality of adversarial texts.
| 2,018 | Computation and Language |
Siamese Neural Networks with Random Forest for detecting duplicate
question pairs | Determining whether two given questions are semantically similar is a fairly
challenging task given the different structures and forms that the questions
can take. In this paper, we use Gated Recurrent Units(GRU) in combination with
other highly used machine learning algorithms like Random Forest, Adaboost and
SVM for the similarity prediction task on a dataset released by Quora,
consisting of about 400k labeled question pairs. We got the best result by
using the Siamese adaptation of a Bidirectional GRU with a Random Forest
classifier, which landed us among the top 24% in the competition Quora Question
Pairs hosted on Kaggle.
| 2,018 | Computation and Language |
Early Detection of Social Media Hoaxes at Scale | The unmoderated nature of social media enables the diffusion of hoaxes, which
in turn jeopardises the credibility of information gathered from social media
platforms. Existing research on automated detection of hoaxes has the
limitation of using relatively small datasets, owing to the difficulty of
getting labelled data. This in turn has limited research exploring early
detection of hoaxes as well as exploring other factors such as the effect of
the size of the training data or the use of sliding windows. To mitigate this
problem, we introduce a semi-automated method that leverages the Wikidata
knowledge base to build large-scale datasets for veracity classification,
focusing on celebrity death reports. This enables us to create a dataset with
4,007 reports including over 13 million tweets, 15% of which are fake.
Experiments using class-specific representations of word embeddings show that
we can achieve F1 scores nearing 72% within 10 minutes of the first tweet being
posted when we expand the size of the training data following our
semi-automated means. Our dataset represents a realistic scenario with a real
distribution of true, commemorative and false stories, which we release for
further use as a benchmark in future research.
| 2,020 | Computation and Language |
Assertion-based QA with Question-Aware Open Information Extraction | We present assertion based question answering (ABQA), an open domain question
answering task that takes a question and a passage as inputs, and outputs a
semi-structured assertion consisting of a subject, a predicate and a list of
arguments. An assertion conveys more evidences than a short answer span in
reading comprehension, and it is more concise than a tedious passage in
passage-based QA. These advantages make ABQA more suitable for human-computer
interaction scenarios such as voice-controlled speakers. Further progress
towards improving ABQA requires richer supervised dataset and powerful models
of text understanding. To remedy this, we introduce a new dataset called
WebAssertions, which includes hand-annotated QA labels for 358,427 assertions
in 55,960 web passages. To address ABQA, we develop both generative and
extractive approaches. The backbone of our generative approach is sequence to
sequence learning. In order to capture the structure of the output assertion,
we introduce a hierarchical decoder that first generates the structure of the
assertion and then generates the words of each field. The extractive approach
is based on learning to rank. Features at different levels of granularity are
designed to measure the semantic relevance between a question and an assertion.
Experimental results show that our approaches have the ability to infer
question-aware assertions from a passage. We further evaluate our approaches by
incorporating the ABQA results as additional features in passage-based QA.
Results on two datasets show that ABQA features significantly improve the
accuracy on passage-based~QA.
| 2,018 | Computation and Language |
The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering'
Language Embeddings | Offensive or antagonistic language targeted at individuals and social groups
based on their personal characteristics (also known as cyber hate speech or
cyberhate) has been frequently posted and widely circulated viathe World Wide
Web. This can be considered as a key risk factor for individual and societal
tension linked toregional instability. Automated Web-based cyberhate detection
is important for observing and understandingcommunity and regional societal
tension - especially in online social networks where posts can be rapidlyand
widely viewed and disseminated. While previous work has involved using
lexicons, bags-of-words orprobabilistic language parsing approaches, they often
suffer from a similar issue which is that cyberhate can besubtle and indirect -
thus depending on the occurrence of individual words or phrases can lead to a
significantnumber of false negatives, providing inaccurate representation of
the trends in cyberhate. This problemmotivated us to challenge thinking around
the representation of subtle language use, such as references toperceived
threats from "the other" including immigration or job prosperity in a hateful
context. We propose anovel framework that utilises language use around the
concept of "othering" and intergroup threat theory toidentify these subtleties
and we implement a novel classification method using embedding learning to
computesemantic distances between parts of speech considered to be part of an
"othering" narrative. To validate ourapproach we conduct several experiments on
different types of cyberhate, namely religion, disability, race andsexual
orientation, with F-measure scores for classifying hateful instances obtained
through applying ourmodel of 0.93, 0.86, 0.97 and 0.98 respectively, providing
a significant improvement in classifier accuracy overthe state-of-the-art
| 2,018 | Computation and Language |
What did you Mention? A Large Scale Mention Detection Benchmark for
Spoken and Written Text | We describe a large, high-quality benchmark for the evaluation of Mention
Detection tools. The benchmark contains annotations of both named entities as
well as other types of entities, annotated on different types of text, ranging
from clean text taken from Wikipedia, to noisy spoken data. The benchmark was
built through a highly controlled crowd sourcing process to ensure its quality.
We describe the benchmark, the process and the guidelines that were used to
build it. We then demonstrate the results of a state-of-the-art system running
on that benchmark.
| 2,018 | Computation and Language |
Analyzing Language Learned by an Active Question Answering Agent | We analyze the language learned by an agent trained with reinforcement
learning as a component of the ActiveQA system [Buck et al., 2017]. In
ActiveQA, question answering is framed as a reinforcement learning task in
which an agent sits between the user and a black box question-answering system.
The agent learns to reformulate the user's questions to elicit the optimal
answers. It probes the system with many versions of a question that are
generated via a sequence-to-sequence question reformulation model, then
aggregates the returned evidence to find the best answer. This process is an
instance of \emph{machine-machine} communication. The question reformulation
model must adapt its language to increase the quality of the answers returned,
matching the language of the question answering system. We find that the agent
does not learn transformations that align with semantic intuitions but
discovers through learning classical information retrieval techniques such as
tf-idf re-weighting and stemming.
| 2,018 | Computation and Language |
Query Focused Abstractive Summarization: Incorporating Query Relevance,
Multi-Document Coverage, and Summary Length Constraints into seq2seq Models | Query Focused Summarization (QFS) has been addressed mostly using extractive
methods. Such methods, however, produce text which suffers from low coherence.
We investigate how abstractive methods can be applied to QFS, to overcome such
limitations. Recent developments in neural-attention based sequence-to-sequence
models have led to state-of-the-art results on the task of abstractive generic
single document summarization. Such models are trained in an end to end method
on large amounts of training data. We address three aspects to make abstractive
summarization applicable to QFS: (a)since there is no training data, we
incorporate query relevance into a pre-trained abstractive model; (b) since
existing abstractive models are trained in a single-document setting, we design
an iterated method to embed abstractive models within the multi-document
requirement of QFS; (c) the abstractive models we adapt are trained to generate
text of specific length (about 100 words), while we aim at generating output of
a different size (about 250 words); we design a way to adapt the target size of
the generated summaries to a given size ratio. We compare our method (Relevance
Sensitive Attention for QFS) to extractive baselines and with various ways to
combine abstractive models on the DUC QFS datasets and demonstrate solid
improvements on ROUGE performance.
| 2,018 | Computation and Language |
SentiPers: A Sentiment Analysis Corpus for Persian | Sentiment Analysis (SA) is a major field of study in natural language
processing, computational linguistics and information retrieval. Interest in SA
has been constantly growing in both academia and industry over the recent
years. Moreover, there is an increasing need for generating appropriate
resources and datasets in particular for low resource languages including
Persian. These datasets play an important role in designing and developing
appropriate opinion mining platforms using supervised, semi-supervised or
unsupervised methods. In this paper, we outline the entire process of
developing a manually annotated sentiment corpus, SentiPers, which covers
formal and informal written contemporary Persian. To the best of our knowledge,
SentiPers is a unique sentiment corpus with such a rich annotation in three
different levels including document-level, sentence-level, and
entity/aspect-level for Persian. The corpus contains more than 26000 sentences
of users opinions from digital product domain and benefits from special
characteristics such as quantifying the positiveness or negativity of an
opinion through assigning a number within a specific range to any given
sentence. Furthermore, we present statistics on various components of our
corpus as well as studying the inter-annotator agreement among the annotators.
Finally, some of the challenges that we faced during the annotation process
will be discussed as well.
| 2,021 | Computation and Language |
HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments | The science of happiness is an area of positive psychology concerned with
understanding what behaviors make people happy in a sustainable fashion.
Recently, there has been interest in developing technologies that help
incorporate the findings of the science of happiness into users' daily lives by
steering them towards behaviors that increase happiness. With the goal of
building technology that can understand how people express their happy moments
in text, we crowd-sourced HappyDB, a corpus of 100,000 happy moments that we
make publicly available. This paper describes HappyDB and its properties, and
outlines several important NLP problems that can be studied with the help of
the corpus. We also apply several state-of-the-art analysis techniques to
analyze HappyDB. Our results demonstrate the need for deeper NLP techniques to
be developed which makes HappyDB an exciting resource for follow-on research.
| 2,018 | Computation and Language |
Evaluating Layers of Representation in Neural Machine Translation on
Part-of-Speech and Semantic Tagging Tasks | While neural machine translation (NMT) models provide improved translation
quality in an elegant, end-to-end framework, it is less clear what they learn
about language. Recent work has started evaluating the quality of vector
representations learned by NMT models on morphological and syntactic tasks. In
this paper, we investigate the representations learned at different layers of
NMT encoders. We train NMT systems on parallel data and use the trained models
to extract features for training a classifier on two tasks: part-of-speech and
semantic tagging. We then measure the performance of the classifier as a proxy
to the quality of the original NMT model for the given task. Our quantitative
analysis yields interesting insights regarding representation learning in NMT
models. For instance, we find that higher layers are better at learning
semantics while lower layers tend to be better for part-of-speech tagging. We
also observe little effect of the target language on source-side
representations, especially with higher quality NMT models.
| 2,017 | Computation and Language |
Vietnamese Open Information Extraction | Open information extraction (OIE) is the process to extract relations and
their arguments automatically from textual documents without the need to
restrict the search to predefined relations. In recent years, several OIE
systems for the English language have been created but there is not any system
for the Vietnamese language. In this paper, we propose a method of OIE for
Vietnamese using a clause-based approach. Accordingly, we exploit Vietnamese
dependency parsing using grammar clauses that strives to consider all possible
relations in a sentence. The corresponding clause types are identified by their
propositions as extractable relations based on their grammatical functions of
constituents. As a result, our system is the first OIE system named vnOIE for
the Vietnamese language that can generate open relations and their arguments
from Vietnamese text with highly scalable extraction while being domain
independent. Experimental results show that our OIE system achieves promising
results with a precision of 83.71%.
| 2,018 | Computation and Language |
Improving Review Representations with User Attention and Product
Attention for Sentiment Classification | Neural network methods have achieved great success in reviews sentiment
classification. Recently, some works achieved improvement by incorporating user
and product information to generate a review representation. However, in
reviews, we observe that some words or sentences show strong user's preference,
and some others tend to indicate product's characteristic. The two kinds of
information play different roles in determining the sentiment label of a
review. Therefore, it is not reasonable to encode user and product information
together into one representation. In this paper, we propose a novel framework
to encode user and product information. Firstly, we apply two individual
hierarchical neural networks to generate two representations, with user
attention or with product attention. Then, we design a combined strategy to
make full use of the two representations for training and final prediction. The
experimental results show that our model obviously outperforms other
state-of-the-art methods on IMDB and Yelp datasets. Through the visualization
of attention over words related to user or product, we validate our observation
mentioned above.
| 2,018 | Computation and Language |
Deep Learning for Sentiment Analysis : A Survey | Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.
| 2,018 | Computation and Language |
A Question-Focused Multi-Factor Attention Network for Question Answering | Neural network models recently proposed for question answering (QA) primarily
focus on capturing the passage-question relation. However, they have minimal
capability to link relevant facts distributed across multiple sentences which
is crucial in achieving deeper understanding, such as performing multi-sentence
reasoning, co-reference resolution, etc. They also do not explicitly focus on
the question and answer type which often plays a critical role in QA. In this
paper, we propose a novel end-to-end question-focused multi-factor attention
network for answer extraction. Multi-factor attentive encoding using
tensor-based transformation aggregates meaningful facts even when they are
located in multiple sentences. To implicitly infer the answer type, we also
propose a max-attentional question aggregation mechanism to encode a question
vector based on the important words in a question. During prediction, we
incorporate sequence-level encoding of the first wh-word and its immediately
following word as an additional source of question type information. Our
proposed model achieves significant improvements over the best prior
state-of-the-art results on three large-scale challenging QA datasets, namely
NewsQA, TriviaQA, and SearchQA.
| 2,018 | Computation and Language |
Continuous Space Reordering Models for Phrase-based MT | Bilingual sequence models improve phrase-based translation and reordering by
overcoming phrasal independence assumption and handling long range reordering.
However, due to data sparsity, these models often fall back to very small
context sizes. This problem has been previously addressed by learning sequences
over generalized representations such as POS tags or word clusters. In this
paper, we explore an alternative based on neural network models. More
concretely we train neuralized versions of lexicalized reordering and the
operation sequence models using feed-forward neural network. Our results show
improvements of up to 0.6 and 0.5 BLEU points on top of the baseline
German->English and English->German systems. We also observed improvements
compared to the systems that used POS tags and word clusters to train these
models. Because we modify the bilingual corpus to integrate reordering
operations, this allows us to also train a sequence-to-sequence neural MT model
having explicit reordering triggers. Our motivation was to directly enable
reordering information in the encoder-decoder framework, which otherwise relies
solely on the attention model to handle long range reordering. We tried both
coarser and fine-grained reordering operations. However, these experiments did
not yield any improvements over the baseline Neural MT systems.
| 2,018 | Computation and Language |
Context Models for OOV Word Translation in Low-Resource Languages | Out-of-vocabulary word translation is a major problem for the translation of
low-resource languages that suffer from a lack of parallel training data. This
paper evaluates the contributions of target-language context models towards the
translation of OOV words, specifically in those cases where OOV translations
are derived from external knowledge sources, such as dictionaries. We develop
both neural and non-neural context models and evaluate them within both
phrase-based and self-attention based neural machine translation systems. Our
results show that neural language models that integrate additional context
beyond the current sentence are the most effective in disambiguating possible
OOV word translations. We present an efficient second-pass lattice-rescoring
method for wide-context neural language models and demonstrate performance
improvements over state-of-the-art self-attention based neural MT systems in
five out of six low-resource language pairs.
| 2,018 | Computation and Language |
A Multilayer Convolutional Encoder-Decoder Neural Network for
Grammatical Error Correction | We improve automatic correction of grammatical, orthographic, and collocation
errors in text using a multilayer convolutional encoder-decoder neural network.
The network is initialized with embeddings that make use of character N-gram
information to better suit this task. When evaluated on common benchmark test
data sets (CoNLL-2014 and JFLEG), our model substantially outperforms all prior
neural approaches on this task as well as strong statistical machine
translation-based systems with neural and task-specific features trained on the
same data. Our analysis shows the superiority of convolutional neural networks
over recurrent neural networks such as long short-term memory (LSTM) networks
in capturing the local context via attention, and thereby improving the
coverage in correcting grammatical errors. By ensembling multiple models, and
incorporating an N-gram language model and edit features via rescoring, our
novel method becomes the first neural approach to outperform the current
state-of-the-art statistical machine translation-based approach, both in terms
of grammaticality and fluency.
| 2,018 | Computation and Language |
A Simple Theoretical Model of Importance for Summarization | Research on summarization has mainly been driven by empirical approaches,
crafting systems to perform well on standard datasets with the notion of
information Importance remaining latent. We argue that establishing theoretical
models of Importance will advance our understanding of the task and help to
further improve summarization systems. To this end, we propose simple but
rigorous definitions of several concepts that were previously used only
intuitively in summarization: Redundancy, Relevance, and Informativeness.
Importance arises as a single quantity naturally unifying these concepts.
Additionally, we provide intuitions to interpret the proposed quantities and
experiments to demonstrate the potential of the framework to inform and guide
subsequent works.
| 2,019 | Computation and Language |
Exploration on Generating Traditional Chinese Medicine Prescription from
Symptoms with an End-to-End method | Traditional Chinese Medicine (TCM) is an influential form of medical
treatment in China and surrounding areas. In this paper, we propose a TCM
prescription generation task that aims to automatically generate a herbal
medicine prescription based on textual symptom descriptions.
Sequence-to-sequence (seq2seq) model has been successful in dealing with
sequence generation tasks. We explore a potential end-to-end solution to the
TCM prescription generation task using seq2seq models. However, experiments
show that directly applying seq2seq model leads to unfruitful results due to
the repetition problem. To solve the problem, we propose a novel decoder with
coverage mechanism and a novel soft loss function. The experimental results
demonstrate the effectiveness of the proposed approach. Judged by professors
who excel in TCM, the generated prescriptions are rated 7.3 out of 10. It shows
that the model can indeed help with the prescribing procedure in real life.
| 2,018 | Computation and Language |
Improving Word Vector with Prior Knowledge in Semantic Dictionary | Using low dimensional vector space to represent words has been very effective
in many NLP tasks. However, it doesn't work well when faced with the problem of
rare and unseen words. In this paper, we propose to leverage the knowledge in
semantic dictionary in combination with some morphological information to build
an enhanced vector space. We get an improvement of 2.3% over the
state-of-the-art Heidel Time system in temporal expression recognition, and
obtain a large gain in other name entity recognition (NER) tasks. The semantic
dictionary Hownet alone also shows promising results in computing lexical
similarity.
| 2,018 | Computation and Language |
A Sheaf Model of Contradictions and Disagreements. Preliminary Report
and Discussion | We introduce a new formal model -- based on the mathematical construct of
sheaves -- for representing contradictory information in textual sources. This
model has the advantage of letting us (a) identify the causes of the
inconsistency; (b) measure how strong it is; (c) and do something about it,
e.g. suggest ways to reconcile inconsistent advice. This model naturally
represents the distinction between contradictions and disagreements. It is
based on the idea of representing natural language sentences as formulas with
parameters sitting on lattices, creating partial orders based on predicates
shared by theories, and building sheaves on these partial orders with products
of lattices as stalks. Degrees of disagreement are measured by the existence of
global and local sections.
Limitations of the sheaf approach and connections to recent work in natural
language processing, as well as the topics of contextuality in physics, data
fusion, topological data analysis and epistemology are also discussed.
| 2,018 | Computation and Language |
Combining Convolution and Recursive Neural Networks for Sentiment
Analysis | This paper addresses the problem of sentence-level sentiment analysis. In
recent years, Convolution and Recursive Neural Networks have been proven to be
effective network architecture for sentence-level sentiment analysis.
Nevertheless, each of them has their own potential drawbacks. For alleviating
their weaknesses, we combined Convolution and Recursive Neural Networks into a
new network architecture. In addition, we employed transfer learning from a
large document-level labeled sentiment dataset to improve the word embedding in
our models. The resulting models outperform all recent Convolution and
Recursive Neural Networks. Beyond that, our models achieve comparable
performance with state-of-the-art systems on Stanford Sentiment Treebank.
| 2,018 | Computation and Language |
Multi-Pointer Co-Attention Networks for Recommendation | Many recent state-of-the-art recommender systems such as D-ATT, TransNet and
DeepCoNN exploit reviews for representation learning. This paper proposes a new
neural architecture for recommendation with reviews. Our model operates on a
multi-hierarchical paradigm and is based on the intuition that not all reviews
are created equal, i.e., only a select few are important. The importance,
however, should be dynamically inferred depending on the current target. To
this end, we propose a review-by-review pointer-based learning scheme that
extracts important reviews, subsequently matching them in a word-by-word
fashion. This enables not only the most informative reviews to be utilized for
prediction but also a deeper word-level interaction. Our pointer-based method
operates with a novel gumbel-softmax based pointer mechanism that enables the
incorporation of discrete vectors within differentiable neural architectures.
Our pointer mechanism is co-attentive in nature, learning pointers which are
co-dependent on user-item relationships. Finally, we propose a multi-pointer
learning scheme that learns to combine multiple views of interactions between
user and item. Overall, we demonstrate the effectiveness of our proposed model
via extensive experiments on \textbf{24} benchmark datasets from Amazon and
Yelp. Empirical results show that our approach significantly outperforms
existing state-of-the-art, with up to 19% and 71% relative improvement when
compared to TransNet and DeepCoNN respectively. We study the behavior of our
multi-pointer learning mechanism, shedding light on evidence aggregation
patterns in review-based recommender systems.
| 2,018 | Computation and Language |
A Survey of Word Embeddings Evaluation Methods | Word embeddings are real-valued word representations able to capture lexical
semantics and trained on natural language corpora. Models proposing these
representations have gained popularity in the recent years, but the issue of
the most adequate evaluation method still remains open. This paper presents an
extensive overview of the field of word embeddings evaluation, highlighting
main problems and proposing a typology of approaches to evaluation, summarizing
16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and
experimental methods, systematize information about evaluation datasets and
discuss some key challenges.
| 2,018 | Computation and Language |
Helping Crisis Responders Find the Informative Needle in the Tweet
Haystack | Crisis responders are increasingly using social media, data and other digital
sources of information to build a situational understanding of a crisis
situation in order to design an effective response. However with the increased
availability of such data, the challenge of identifying relevant information
from it also increases. This paper presents a successful automatic approach to
handling this problem. Messages are filtered for informativeness based on a
definition of the concept drawn from prior research and crisis response
experts. Informative messages are tagged for actionable data -- for example,
people in need, threats to rescue efforts, changes in environment, and so on.
In all, eight categories of actionability are identified. The two components --
informativeness and actionability classification -- are packaged together as an
openly-available tool called Emina (Emergent Informativeness and
Actionability).
| 2,018 | Computation and Language |
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts | Motivated by a project to create a system for people who are deaf or
hard-of-hearing that would use automatic speech recognition (ASR) to produce
real-time text captions of spoken English during in-person meetings with
hearing individuals, we have augmented a transcript of the Switchboard
conversational dialogue corpus with an overlay of word-importance annotations,
with a numeric score for each word, to indicate its importance to the meaning
of each dialogue turn. Further, we demonstrate the utility of this corpus by
training an automatic word importance labeling model; our best performing model
has an F-score of 0.60 in an ordinal 6-class word-importance classification
task with an agreement (concordance correlation coefficient) of 0.839 with the
human annotators (agreement score between annotators is 0.89). Finally, we
discuss our intended future applications of this resource, particularly for the
task of evaluating ASR performance, i.e. creating metrics that predict
ASR-output caption text usability for DHH users better thanWord Error Rate
(WER).
| 2,018 | Computation and Language |
Accelerating recurrent neural network language model based online speech
recognition system | This paper presents methods to accelerate recurrent neural network based
language models (RNNLMs) for online speech recognition systems. Firstly, a
lossy compression of the past hidden layer outputs (history vector) with
caching is introduced in order to reduce the number of LM queries. Next, RNNLM
computations are deployed in a CPU-GPU hybrid manner, which computes each layer
of the model on a more advantageous platform. The added overhead by data
exchanges between CPU and GPU is compensated through a frame-wise batching
strategy. The performance of the proposed methods evaluated on LibriSpeech test
sets indicates that the reduction in history vector precision improves the
average recognition speed by 1.23 times with minimum degradation in accuracy.
On the other hand, the CPU-GPU hybrid parallelization enables RNNLM based
real-time recognition with a four times improvement in speed.
| 2,018 | Computation and Language |
A State-of-the-Art of Semantic Change Computation | This paper reviews the state-of-the-art of semantic change computation, one
emerging research field in computational linguistics, proposing a framework
that summarizes the literature by identifying and expounding five essential
components in the field: diachronic corpus, diachronic word sense
characterization, change modelling, evaluation data and data visualization.
Despite the potential of the field, the review shows that current studies are
mainly focused on testifying hypotheses proposed in theoretical linguistics and
that several core issues remain to be solved: the need for diachronic corpora
of languages other than English, the need for comprehensive evaluation data for
evaluation, the comparison and construction of approaches to diachronic word
sense characterization and change modelling, and further exploration of data
visualization techniques for hypothesis justification.
| 2,018 | Computation and Language |
An Attention-Based Word-Level Interaction Model: Relation Detection for
Knowledge Base Question Answering | Relation detection plays a crucial role in Knowledge Base Question Answering
(KBQA) because of the high variance of relation expression in the question.
Traditional deep learning methods follow an encoding-comparing paradigm, where
the question and the candidate relation are represented as vectors to compare
their semantic similarity. Max- or average- pooling operation, which compresses
the sequence of words into fixed-dimensional vectors, becomes the bottleneck of
information. In this paper, we propose to learn attention-based word-level
interactions between questions and relations to alleviate the bottleneck issue.
Similar to the traditional models, the question and relation are firstly
represented as sequences of vectors. Then, instead of merging the sequence into
a single vector with pooling operation, soft alignments between words from the
question and the relation are learned. The aligned words are subsequently
compared with the convolutional neural network (CNN) and the comparison results
are merged finally. Through performing the comparison on low-level
representations, the attention-based word-level interaction model (ABWIM)
relieves the information loss issue caused by merging the sequence into a
fixed-dimensional vector before the comparison. The experimental results of
relation detection on both SimpleQuestions and WebQuestions datasets show that
ABWIM achieves state-of-the-art accuracy, demonstrating its effectiveness.
| 2,018 | Computation and Language |
Pilot study for the COST Action "Reassembling the Republic of Letters":
language-driven network analysis of letters from the Hartlib's Papers | The present report summarizes an exploratory study which we carried out in
the context of the COST Action IS1310 "Reassembling the Republic of Letters,
1500-1800", and which is relevant to the activities of Working Group 3 "Texts
and Topics" and Working Group 2 "People and Networks". In this study we
investigated the use of Natural Language Processing (NLP) and Network Text
Analysis on a small sample of seventeenth-century letters selected from Hartlib
Papers, whose records are in one of the catalogues of Early Modern Letters
Online (EMLO) and whose online edition is available on the website of the
Humanities Research Institute at the University of Sheffield
(http://www.hrionline.ac.uk/hartlib/). We outline the NLP pipeline used to
automatically process the texts into a network representation, in order to
identify the texts' "narrative centrality", i.e. the most central entities in
the texts, and the relations between them.
| 2,018 | Computation and Language |
PEYMA: A Tagged Corpus for Persian Named Entities | The goal in the NER task is to classify proper nouns of a text into classes
such as person, location, and organization. This is an important preprocessing
step in many NLP tasks such as question-answering and summarization. Although
many research studies have been conducted in this area in English and the
state-of-the-art NER systems have reached performances of higher than 90
percent in terms of F1 measure, there are very few research studies for this
task in Persian. One of the main important causes of this may be the lack of a
standard Persian NER dataset to train and test NER systems. In this research we
create a standard, big-enough tagged Persian NER dataset which will be
distributed for free for research purposes. In order to construct such a
standard dataset, we studied standard NER datasets which are constructed for
English researches and found out that almost all of these datasets are
constructed using news texts. So we collected documents from ten news websites.
Later, in order to provide annotators with some guidelines to tag these
documents, after studying guidelines used for constructing CoNLL and MUC
standard English datasets, we set our own guidelines considering the Persian
linguistic rules.
| 2,018 | Computation and Language |
Preparation of Improved Turkish DataSet for Sentiment Analysis in Social
Media | A public dataset, with a variety of properties suitable for sentiment
analysis [1], event prediction, trend detection and other text mining
applications, is needed in order to be able to successfully perform analysis
studies. The vast majority of data on social media is text-based and it is not
possible to directly apply machine learning processes into these raw data,
since several different processes are required to prepare the data before the
implementation of the algorithms. For example, different misspellings of same
word enlarge the word vector space unnecessarily, thereby it leads to reduce
the success of the algorithm and increase the computational power requirement.
This paper presents an improved Turkish dataset with an effective spelling
correction algorithm based on Hadoop [2]. The collected data is recorded on the
Hadoop Distributed File System and the text based data is processed by
MapReduce programming model. This method is suitable for the storage and
processing of large sized text based social media data. In this study, movie
reviews have been automatically recorded with Apache ManifoldCF (MCF) [3] and
data clusters have been created. Various methods compared such as Levenshtein
and Fuzzy String Matching have been proposed to create a public dataset from
collected data. Experimental results show that the proposed algorithm, which
can be used as an open source dataset in sentiment analysis studies, have been
performed successfully to the detection and correction of spelling errors.
| 2,018 | Computation and Language |
Generating Wikipedia by Summarizing Long Sequences | We show that generating English Wikipedia articles can be approached as a
multi- document summarization of source documents. We use extractive
summarization to coarsely identify salient information and a neural abstractive
model to generate the article. For the abstractive model, we introduce a
decoder-only architecture that can scalably attend to very long sequences, much
longer than typical encoder- decoder architectures used in sequence
transduction. We show that this model can generate fluent, coherent
multi-sentence paragraphs and even whole Wikipedia articles. When given
reference documents, we show it can extract relevant factual information as
reflected in perplexity, ROUGE scores and human evaluations.
| 2,018 | Computation and Language |
The New Modality: Emoji Challenges in Prediction, Anticipation, and
Retrieval | Over the past decade, emoji have emerged as a new and widespread form of
digital communication, spanning diverse social networks and spoken languages.
We propose to treat these ideograms as a new modality in their own right,
distinct in their semantic structure from both the text in which they are often
embedded as well as the images which they resemble. As a new modality, emoji
present rich novel possibilities for representation and interaction. In this
paper, we explore the challenges that arise naturally from considering the
emoji modality through the lens of multimedia research. Specifically, the ways
in which emoji can be related to other common modalities such as text and
images. To do so, we first present a large scale dataset of real-world emoji
usage collected from Twitter. This dataset contains examples of both text-emoji
and image-emoji relationships. We present baseline results on the challenge of
predicting emoji from both text and images, using state-of-the-art neural
networks. Further, we offer a first consideration into the problem of how to
account for new, unseen emoji - a relevant issue as the emoji vocabulary
continues to expand on a yearly basis. Finally, we present results for
multimedia retrieval using emoji as queries.
| 2,018 | Computation and Language |
Paraphrase-Supervised Models of Compositionality | Compositional vector space models of meaning promise new solutions to
stubborn language understanding problems. This paper makes two contributions
toward this end: (i) it uses automatically-extracted paraphrase examples as a
source of supervision for training compositional models, replacing previous
work which relied on manual annotations used for the same purpose, and (ii)
develops a context-aware model for scoring phrasal compositionality.
Experimental results indicate that these multiple sources of information can be
used to learn partial semantic supervision that matches previous techniques in
intrinsic evaluation tasks. Our approaches are also evaluated for their impact
on a machine translation system where we show improvements in translation
quality, demonstrating that compositionality in interpretation correlates with
compositionality in translation.
| 2,018 | Computation and Language |
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention
for Sequence Modeling | Many natural language processing tasks solely rely on sparse dependencies
between a few tokens in a sentence. Soft attention mechanisms show promising
performance in modeling local/global dependencies by soft probabilities between
every two tokens, but they are not effective and efficient when applied to long
sentences. By contrast, hard attention mechanisms directly select a subset of
tokens but are difficult and inefficient to train due to their combinatorial
nature. In this paper, we integrate both soft and hard attention into one
context fusion model, "reinforced self-attention (ReSA)", for the mutual
benefit of each other. In ReSA, a hard attention trims a sequence for a soft
self-attention to process, while the soft attention feeds reward signals back
to facilitate the training of the hard one. For this purpose, we develop a
novel hard attention called "reinforced sequence sampling (RSS)", selecting
tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA
efficiently extracts the sparse dependencies between each pair of selected
tokens. We finally propose an RNN/CNN-free sentence-encoding model, "reinforced
self-attention network (ReSAN)", solely based on ReSA. It achieves
state-of-the-art performance on both Stanford Natural Language Inference (SNLI)
and Sentences Involving Compositional Knowledge (SICK) datasets.
| 2,018 | Computation and Language |
Nested LSTMs | We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple
levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to
stacking. The value of a memory cell in an NLSTM is computed by an LSTM cell,
which has its own inner memory cell. Specifically, instead of computing the
value of the (outer) memory cell as $c^{outer}_t = f_t \odot c_{t-1} + i_t
\odot g_t$, NLSTM memory cells use the concatenation $(f_t \odot c_{t-1}, i_t
\odot g_t)$ as input to an inner LSTM (or NLSTM) memory cell, and set
$c^{outer}_t$ = $h^{inner}_t$. Nested LSTMs outperform both stacked and
single-layer LSTMs with similar numbers of parameters in our experiments on
various character-level language modeling tasks, and the inner memories of an
LSTM learn longer term dependencies compared with the higher-level units of a
stacked LSTM.
| 2,017 | Computation and Language |
Complex Sequential Question Answering: Towards Learning to Converse Over
Linked Question Answer Pairs with a Knowledge Graph | While conversing with chatbots, humans typically tend to ask many questions,
a significant portion of which can be answered by referring to large-scale
knowledge graphs (KG). While Question Answering (QA) and dialog systems have
been studied independently, there is a need to study them closely to evaluate
such real-world scenarios faced by bots involving both these tasks. Towards
this end, we introduce the task of Complex Sequential QA which combines the two
tasks of (i) answering factual questions through complex inferencing over a
realistic-sized KG of millions of entities, and (ii) learning to converse
through a series of coherently linked QA pairs. Through a labor intensive
semi-automatic process, involving in-house and crowdsourced workers, we created
a dataset containing around 200K dialogs with a total of 1.6M turns. Further,
unlike existing large scale QA datasets which contain simple questions that can
be answered from a single tuple, the questions in our dialogs require a larger
subgraph of the KG. Specifically, our dataset has questions which require
logical, quantitative, and comparative reasoning as well as their combinations.
This calls for models which can: (i) parse complex natural language questions,
(ii) use conversation context to resolve coreferences and ellipsis in
utterances, (iii) ask for clarifications for ambiguous queries, and finally
(iv) retrieve relevant subgraphs of the KG to answer such questions. However,
our experiments with a combination of state of the art dialog and QA models
show that they clearly do not achieve the above objectives and are inadequate
for dealing with such complex real world settings. We believe that this new
dataset coupled with the limitations of existing models as reported in this
paper should encourage further research in Complex Sequential QA.
| 2,018 | Computation and Language |
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