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Training an adaptive dialogue policy for interactive learning of
visually grounded word meanings | We present a multi-modal dialogue system for interactive learning of
perceptually grounded word meanings from a human tutor. The system integrates
an incremental, semantic parsing/generation framework - Dynamic Syntax and Type
Theory with Records (DS-TTR) - with a set of visual classifiers that are
learned throughout the interaction and which ground the meaning representations
that it produces. We use this system in interaction with a simulated human
tutor to study the effects of different dialogue policies and capabilities on
the accuracy of learned meanings, learning rates, and efforts/costs to the
tutor. We show that the overall performance of the learning agent is affected
by (1) who takes initiative in the dialogues; (2) the ability to express/use
their confidence level about visual attributes; and (3) the ability to process
elliptical and incrementally constructed dialogue turns. Ultimately, we train
an adaptive dialogue policy which optimises the trade-off between classifier
accuracy and tutoring costs.
| 2,017 | Computation and Language |
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of
Visually Grounded Word Meanings | We motivate and describe a new freely available human-human dialogue dataset
for interactive learning of visually grounded word meanings through ostensive
definition by a tutor to a learner. The data has been collected using a novel,
character-by-character variant of the DiET chat tool (Healey et al., 2003;
Mills and Healey, submitted) with a novel task, where a Learner needs to learn
invented visual attribute words (such as " burchak " for square) from a tutor.
As such, the text-based interactions closely resemble face-to-face conversation
and thus contain many of the linguistic phenomena encountered in natural,
spontaneous dialogue. These include self-and other-correction, mid-sentence
continuations, interruptions, overlaps, fillers, and hedges. We also present a
generic n-gram framework for building user (i.e. tutor) simulations from this
type of incremental data, which is freely available to researchers. We show
that the simulations produce outputs that are similar to the original data
(e.g. 78% turn match similarity). Finally, we train and evaluate a
Reinforcement Learning dialogue control agent for learning visually grounded
word meanings, trained from the BURCHAK corpus. The learned policy shows
comparable performance to a rule-based system built previously.
| 2,017 | Computation and Language |
Synonym Discovery with Etymology-based Word Embeddings | We propose a novel approach to learn word embeddings based on an extended
version of the distributional hypothesis. Our model derives word embedding
vectors using the etymological composition of words, rather than the context in
which they appear. It has the strength of not requiring a large text corpus,
but instead it requires reliable access to etymological roots of words, making
it specially fit for languages with logographic writing systems. The model
consists on three steps: (1) building an etymological graph, which is a
bipartite network of words and etymological roots, (2) obtaining the
biadjacency matrix of the etymological graph and reducing its dimensionality,
(3) using columns/rows of the resulting matrices as embedding vectors. We test
our model in the Chinese and Sino-Korean vocabularies. Our graphs are formed by
a set of 117,000 Chinese words, and a set of 135,000 Sino-Korean words. In both
cases we show that our model performs well in the task of synonym discovery.
| 2,017 | Computation and Language |
Symbol, Conversational, and Societal Grounding with a Toy Robot | Essential to meaningful interaction is grounding at the symbolic,
conversational, and societal levels. We present ongoing work with Anki's Cozmo
toy robot as a research platform where we leverage the recent
words-as-classifiers model of lexical semantics in interactive reference
resolution tasks for language grounding.
| 2,017 | Computation and Language |
Speaker Role Contextual Modeling for Language Understanding and Dialogue
Policy Learning | Language understanding (LU) and dialogue policy learning are two essential
components in conversational systems. Human-human dialogues are not
well-controlled and often random and unpredictable due to their own goals and
speaking habits. This paper proposes a role-based contextual model to consider
different speaker roles independently based on the various speaking patterns in
the multi-turn dialogues. The experiments on the benchmark dataset show that
the proposed role-based model successfully learns role-specific behavioral
patterns for contextual encoding and then significantly improves language
understanding and dialogue policy learning tasks.
| 2,017 | Computation and Language |
Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken
Language Understanding | Spoken language understanding (SLU) is an essential component in
conversational systems. Most SLU component treats each utterance independently,
and then the following components aggregate the multi-turn information in the
separate phases. In order to avoid error propagation and effectively utilize
contexts, prior work leveraged history for contextual SLU. However, the
previous model only paid attention to the content in history utterances without
considering their temporal information and speaker roles. In the dialogues, the
most recent utterances should be more important than the least recent ones.
Furthermore, users usually pay attention to 1) self history for reasoning and
2) others' utterances for listening, the speaker of the utterances may provides
informative cues to help understanding. Therefore, this paper proposes an
attention-based network that additionally leverages temporal information and
speaker role for better SLU, where the attention to contexts and speaker roles
can be automatically learned in an end-to-end manner. The experiments on the
benchmark Dialogue State Tracking Challenge 4 (DSTC4) dataset show that the
time-aware dynamic role attention networks significantly improve the
understanding performance.
| 2,017 | Computation and Language |
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction | Vector-space models, from word embeddings to neural network parsers, have
many advantages for NLP. But how to generalise from fixed-length word vectors
to a vector space for arbitrary linguistic structures is still unclear. In this
paper we propose bag-of-vector embeddings of arbitrary linguistic graphs. A
bag-of-vector space is the minimal nonparametric extension of a vector space,
allowing the representation to grow with the size of the graph, but not tying
the representation to any specific tree or graph structure. We propose
efficient training and inference algorithms based on tensor factorisation for
embedding arbitrary graphs in a bag-of-vector space. We demonstrate the
usefulness of this representation by training bag-of-vector embeddings of
dependency graphs and evaluating them on unsupervised semantic induction for
the Semantic Textual Similarity and Natural Language Inference tasks.
| 2,017 | Computation and Language |
What Words Do We Use to Lie?: Word Choice in Deceptive Messages | Text messaging is the most widely used form of computer-mediated
communication (CMC). Previous findings have shown that linguistic factors can
reliably indicate messages as deceptive. For example, users take longer and use
more words to craft deceptive messages than they do truthful messages. Existing
research has also examined how factors, such as student status and gender,
affect rates of deception and word choice in deceptive messages. However, this
research has been limited by small sample sizes and has returned contradicting
findings. This paper aims to address these issues by using a dataset of text
messages collected from a large and varied set of participants using an Android
messaging application. The results of this paper show significant differences
in word choice and frequency of deceptive messages between male and female
participants, as well as between students and non-students.
| 2,022 | Computation and Language |
Fully Automated Fact Checking Using External Sources | Given the constantly growing proliferation of false claims online in recent
years, there has been also a growing research interest in automatically
distinguishing false rumors from factually true claims. Here, we propose a
general-purpose framework for fully-automatic fact checking using external
sources, tapping the potential of the entire Web as a knowledge source to
confirm or reject a claim. Our framework uses a deep neural network with LSTM
text encoding to combine semantic kernels with task-specific embeddings that
encode a claim together with pieces of potentially-relevant text fragments from
the Web, taking the source reliability into account. The evaluation results
show good performance on two different tasks and datasets: (i) rumor detection
and (ii) fact checking of the answers to a question in community question
answering forums.
| 2,017 | Computation and Language |
Robust Tuning Datasets for Statistical Machine Translation | We explore the idea of automatically crafting a tuning dataset for
Statistical Machine Translation (SMT) that makes the hyper-parameters of the
SMT system more robust with respect to some specific deficiencies of the
parameter tuning algorithms. This is an under-explored research direction,
which can allow better parameter tuning. In this paper, we achieve this goal by
selecting a subset of the available sentence pairs, which are more suitable for
specific combinations of optimizers, objective functions, and evaluation
measures. We demonstrate the potential of the idea with the pairwise ranking
optimization (PRO) optimizer, which is known to yield too short translations.
We show that the learning problem can be alleviated by tuning on a subset of
the development set, selected based on sentence length. In particular, using
the longest 50% of the tuning sentences, we achieve two-fold tuning speedup,
and improvements in BLEU score that rival those of alternatives, which fix
BLEU+1's smoothing instead.
| 2,017 | Computation and Language |
Mathematical foundations of matrix syntax | Matrix syntax is a formal model of syntactic relations in language. The
purpose of this paper is to explain its mathematical foundations, for an
audience with some formal background. We make an axiomatic presentation,
motivating each axiom on linguistic and practical grounds. The resulting
mathematical structure resembles some aspects of quantum mechanics. Matrix
syntax allows us to describe a number of language phenomena that are otherwise
very difficult to explain, such as linguistic chains, and is arguably a more
economical theory of language than most of the theories proposed in the context
of the minimalist program in linguistics. In particular, sentences are
naturally modelled as vectors in a Hilbert space with a tensor product
structure, built from 2x2 matrices belonging to some specific group.
| 2,019 | Computation and Language |
Visual Reasoning with Natural Language | Natural language provides a widely accessible and expressive interface for
robotic agents. To understand language in complex environments, agents must
reason about the full range of language inputs and their correspondence to the
world. Such reasoning over language and vision is an open problem that is
receiving increasing attention. While existing data sets focus on visual
diversity, they do not display the full range of natural language expressions,
such as counting, set reasoning, and comparisons.
We propose a simple task for natural language visual reasoning, where images
are paired with descriptive statements. The task is to predict if a statement
is true for the given scene. This abstract describes our existing synthetic
images corpus and our current work on collecting real vision data.
| 2,017 | Computation and Language |
A Crowd-Annotated Spanish Corpus for Humor Analysis | Computational Humor involves several tasks, such as humor recognition, humor
generation, and humor scoring, for which it is useful to have human-curated
data. In this work we present a corpus of 27,000 tweets written in Spanish and
crowd-annotated by their humor value and funniness score, with about four
annotations per tweet, tagged by 1,300 people over the Internet. It is equally
divided between tweets coming from humorous and non-humorous accounts. The
inter-annotator agreement Krippendorff's alpha value is 0.5710. The dataset is
available for general use and can serve as a basis for humor detection and as a
first step to tackle subjectivity.
| 2,018 | Computation and Language |
Attentive Convolution: Equipping CNNs with RNN-style Attention
Mechanisms | In NLP, convolutional neural networks (CNNs) have benefited less than
recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that
this is because the attention in CNNs has been mainly implemented as attentive
pooling (i.e., it is applied to pooling) rather than as attentive convolution
(i.e., it is integrated into convolution). Convolution is the differentiator of
CNNs in that it can powerfully model the higher-level representation of a word
by taking into account its local fixed-size context in the input text t^x. In
this work, we propose an attentive convolution network, ATTCONV. It extends the
context scope of the convolution operation, deriving higher-level features for
a word not only from local context, but also information extracted from
nonlocal context by the attention mechanism commonly used in RNNs. This
nonlocal context can come (i) from parts of the input text t^x that are distant
or (ii) from extra (i.e., external) contexts t^y. Experiments on sentence
modeling with zero-context (sentiment analysis), single-context (textual
entailment) and multiple-context (claim verification) demonstrate the
effectiveness of ATTCONV in sentence representation learning with the
incorporation of context. In particular, attentive convolution outperforms
attentive pooling and is a strong competitor to popular attentive RNNs.
| 2,018 | Computation and Language |
Improving speech recognition by revising gated recurrent units | Speech recognition is largely taking advantage of deep learning, showing that
substantial benefits can be obtained by modern Recurrent Neural Networks
(RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which
typically reach state-of-the-art performance in many tasks thanks to their
ability to learn long-term dependencies and robustness to vanishing gradients.
Nevertheless, LSTMs have a rather complex design with three multiplicative
gates, that might impair their efficient implementation. An attempt to simplify
LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just
two multiplicative gates.
This paper builds on these efforts by further revising GRUs and proposing a
simplified architecture potentially more suitable for speech recognition. The
contribution of this work is two-fold. First, we suggest to remove the reset
gate in the GRU design, resulting in a more efficient single-gate architecture.
Second, we propose to replace tanh with ReLU activations in the state update
equations. Results show that, in our implementation, the revised architecture
reduces the per-epoch training time with more than 30% and consistently
improves recognition performance across different tasks, input features, and
noisy conditions when compared to a standard GRU.
| 2,017 | Computation and Language |
The Dependence of Frequency Distributions on Multiple Meanings of Words,
Codes and Signs | The dependence of the frequency distributions due to multiple meanings of
words in a text is investigated by deleting letters. By coding the words with
fewer letters the number of meanings per coded word increases. This increase is
measured and used as an input in a predictive theory. For a text written in
English, the word-frequency distribution is broad and fat-tailed, whereas if
the words are only represented by their first letter the distribution becomes
exponential. Both distribution are well predicted by the theory, as is the
whole sequence obtained by consecutively representing the words by the first
L=6,5,4,3,2,1 letters. Comparisons of texts written by Chinese characters and
the same texts written by letter-codes are made and the similarity of the
corresponding frequency-distributions are interpreted as a consequence of the
multiple meanings of Chinese characters. This further implies that the
difference of the shape for word-frequencies for an English text written by
letters and a Chinese text written by Chinese characters is due to the coding
and not to the language per se.
| 2,018 | Computation and Language |
Building Chatbots from Forum Data: Model Selection Using Question
Answering Metrics | We propose to use question answering (QA) data from Web forums to train
chatbots from scratch, i.e., without dialog training data. First, we extract
pairs of question and answer sentences from the typically much longer texts of
questions and answers in a forum. We then use these shorter texts to train
seq2seq models in a more efficient way. We further improve the parameter
optimization using a new model selection strategy based on QA measures.
Finally, we propose to use extrinsic evaluation with respect to a QA task as an
automatic evaluation method for chatbots. The evaluation shows that the model
achieves a MAP of 63.5% on the extrinsic task. Moreover, it can answer
correctly 49.5% of the questions when they are similar to questions asked in
the forum, and 47.3% of the questions when they are more conversational in
style.
| 2,017 | Computation and Language |
Compiling and Processing Historical and Contemporary Portuguese Corpora | This technical report describes the framework used for processing three large
Portuguese corpora. Two corpora contain texts from newspapers, one published in
Brazil and the other published in Portugal. The third corpus is Colonia, a
historical Portuguese collection containing texts written between the 16th and
the early 20th century. The report presents pre-processing methods,
segmentation, and annotation of the corpora as well as indexing and querying
methods. Finally, it presents published research papers using the corpora.
| 2,017 | Computation and Language |
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy
Detection | Modeling hypernymy, such as poodle is-a dog, is an important generalization
aid to many NLP tasks, such as entailment, coreference, relation extraction,
and question answering. Supervised learning from labeled hypernym sources, such
as WordNet, limits the coverage of these models, which can be addressed by
learning hypernyms from unlabeled text. Existing unsupervised methods either do
not scale to large vocabularies or yield unacceptably poor accuracy. This paper
introduces distributional inclusion vector embedding (DIVE), a
simple-to-implement unsupervised method of hypernym discovery via per-word
non-negative vector embeddings which preserve the inclusion property of word
contexts in a low-dimensional and interpretable space. In experimental
evaluations more comprehensive than any previous literature of which we are
aware-evaluating on 11 datasets using multiple existing as well as newly
proposed scoring functions-we find that our method provides up to double the
precision of previous unsupervised embeddings, and the highest average
performance, using a much more compact word representation, and yielding many
new state-of-the-art results.
| 2,018 | Computation and Language |
Minimal Dependency Translation: a Framework for Computer-Assisted
Translation for Under-Resourced Languages | This paper introduces Minimal Dependency Translation (MDT), an ongoing
project to develop a rule-based framework for the creation of rudimentary
bilingual lexicon-grammars for machine translation and computer-assisted
translation into and out of under-resourced languages as well as initial steps
towards an implementation of MDT for English-to-Amharic translation. The basic
units in MDT, called groups, are headed multi-item sequences. In addition to
wordforms, groups may contain lexemes, syntactic-semantic categories, and
grammatical features. Each group is associated with one or more translations,
each of which is a group in a target language. During translation, constraint
satisfaction is used to select a set of source-language groups for the input
sentence and to sequence the words in the associated target-language groups.
| 2,017 | Computation and Language |
Identifying Nominals with No Head Match Co-references Using Deep
Learning | Identifying nominals with no head match is a long-standing challenge in
coreference resolution with current systems performing significantly worse than
humans. In this paper we present a new neural network architecture which
outperforms the current state-of-the-art system on the English portion of the
CoNLL 2012 Shared Task. This is done by using a logistic regression on features
produced by two submodels, one of which is has the architecture proposed in
[CM16a] while the other combines domain specific embeddings of the antecedent
and the mention. We also propose some simple additional features which seem to
improve performance for all models substantially, increasing F1 by almost 4% on
basic logistic regression and other complex models.
| 2,017 | Computation and Language |
Event Identification as a Decision Process with Non-linear
Representation of Text | We propose scale-free Identifier Network(sfIN), a novel model for event
identification in documents. In general, sfIN first encodes a document into
multi-scale memory stacks, then extracts special events via conducting
multi-scale actions, which can be considered as a special type of sequence
labelling. The design of large scale actions makes it more efficient processing
a long document. The whole model is trained with both supervised learning and
reinforcement learning.
| 2,017 | Computation and Language |
Annotation and Detection of Emotion in Text-based Dialogue Systems with
CNN | Knowledge of users' emotion states helps improve human-computer interaction.
In this work, we presented EmoNet, an emotion detector of Chinese daily
dialogues based on deep convolutional neural networks. In order to maintain the
original linguistic features, such as the order, commonly used methods like
segmentation and keywords extraction were not adopted, instead we increased the
depth of CNN and tried to let CNN learn inner linguistic relationships. Our
main contribution is that we presented a new model and a new pipeline which can
be used in multi-language environment to solve sentimental problems.
Experimental results shows EmoNet has a great capacity in learning the emotion
of dialogues and achieves a better result than other state of art detectors do.
| 2,017 | Computation and Language |
Is Structure Necessary for Modeling Argument Expectations in
Distributional Semantics? | Despite the number of NLP studies dedicated to thematic fit estimation,
little attention has been paid to the related task of composing and updating
verb argument expectations. The few exceptions have mostly modeled this
phenomenon with structured distributional models, implicitly assuming a
similarly structured representation of events. Recent experimental evidence,
however, suggests that human processing system could also exploit an
unstructured "bag-of-arguments" type of event representation to predict
upcoming input. In this paper, we re-implement a traditional structured model
and adapt it to compare the different hypotheses concerning the degree of
structure in our event knowledge, evaluating their relative performance in the
task of the argument expectations update.
| 2,017 | Computation and Language |
MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language
Processing | Multilinguality is gradually becoming ubiquitous in the sense that more and
more researchers have successfully shown that using additional languages help
improve the results in many Natural Language Processing tasks. Multilingual
Multiway Corpora (MMC) contain the same sentence in multiple languages. Such
corpora have been primarily used for Multi-Source and Pivot Language Machine
Translation but are also useful for developing multilingual sequence taggers by
transfer learning. While these corpora are available, they are not organized
for multilingual experiments and researchers need to write boilerplate code
every time they want to use said corpora. Moreover, because there is no
official MMC collection it becomes difficult to compare against existing
approaches. As such we present our work on creating a unified and
systematically organized repository of MMC spanning a large number of
languages. We also provide training, development and test splits for corpora
where official splits are unavailable. We hope that this will help speed up the
pace of multilingual NLP research and ensure that NLP researchers obtain
results that are more trustable since they can be compared easily. We indicate
corpora sources, extraction procedures if any and relevant statistics. We also
make our collection public for research purposes.
| 2,019 | Computation and Language |
Towards an Inferential Lexicon of Event Selecting Predicates for French | We present a manually constructed seed lexicon encoding the inferential
profiles of French event selecting predicates across different uses. The
inferential profile (Karttunen, 1971a) of a verb is designed to capture the
inferences triggered by the use of this verb in context. It reflects the
influence of the clause-embedding verb on the factuality of the event described
by the embedded clause. The resource developed provides evidence for the
following three hypotheses: (i) French implicative verbs have an aspect
dependent profile (their inferential profile varies with outer aspect), while
factive verbs have an aspect independent profile (they keep the same
inferential profile with both imperfective and perfective aspect); (ii)
implicativity decreases with imperfective aspect: the inferences triggered by
French implicative verbs combined with perfective aspect are often weakened
when the same verbs are combined with imperfective aspect; (iii) implicativity
decreases with an animate (deep) subject: the inferences triggered by a verb
which is implicative with an inanimate subject are weakened when the same verb
is used with an animate subject. The resource additionally shows that verbs
with different inferential profiles display clearly distinct sub-categorisation
patterns. In particular, verbs that have both factive and implicative readings
are shown to prefer infinitival clauses in their implicative reading, and
tensed clauses in their factive reading.
| 2,017 | Computation and Language |
Improving Lexical Choice in Neural Machine Translation | We explore two solutions to the problem of mistranslating rare words in
neural machine translation. First, we argue that the standard output layer,
which computes the inner product of a vector representing the context with all
possible output word embeddings, rewards frequent words disproportionately, and
we propose to fix the norms of both vectors to a constant value. Second, we
integrate a simple lexical module which is jointly trained with the rest of the
model. We evaluate our approaches on eight language pairs with data sizes
ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU,
surpassing phrase-based translation in nearly all settings.
| 2,018 | Computation and Language |
Transferring Semantic Roles Using Translation and Syntactic Information | Our paper addresses the problem of annotation projection for semantic role
labeling for resource-poor languages using supervised annotations from a
resource-rich language through parallel data. We propose a transfer method that
employs information from source and target syntactic dependencies as well as
word alignment density to improve the quality of an iterative bootstrapping
method. Our experiments yield a $3.5$ absolute labeled F-score improvement over
a standard annotation projection method.
| 2,017 | Computation and Language |
Cross-Language Question Re-Ranking | We study how to find relevant questions in community forums when the language
of the new questions is different from that of the existing questions in the
forum. In particular, we explore the Arabic-English language pair. We compare a
kernel-based system with a feed-forward neural network in a scenario where a
large parallel corpus is available for training a machine translation system,
bilingual dictionaries, and cross-language word embeddings. We observe that
both approaches degrade the performance of the system when working on the
translated text, especially the kernel-based system, which depends heavily on a
syntactic kernel. We address this issue using a cross-language tree kernel,
which compares the original Arabic tree to the English trees of the related
questions. We show that this kernel almost closes the performance gap with
respect to the monolingual system. On the neural network side, we use the
parallel corpus to train cross-language embeddings, which we then use to
represent the Arabic input and the English related questions in the same space.
The results also improve to close to those of the monolingual neural network.
Overall, the kernel system shows a better performance compared to the neural
network in all cases.
| 2,017 | Computation and Language |
Semantic Sentiment Analysis of Twitter Data | Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.
| 2,017 | Computation and Language |
Discourse Structure in Machine Translation Evaluation | In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.
| 2,017 | Computation and Language |
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl | We present DepCC, the largest-to-date linguistically analyzed corpus in
English including 365 million documents, composed of 252 billion tokens and 7.5
billion of named entity occurrences in 14.3 billion sentences from a web-scale
crawl of the \textsc{Common Crawl} project. The sentences are processed with a
dependency parser and with a named entity tagger and contain provenance
information, enabling various applications ranging from training syntax-based
word embeddings to open information extraction and question answering. We built
an index of all sentences and their linguistic meta-data enabling quick search
across the corpus. We demonstrate the utility of this corpus on the verb
similarity task by showing that a distributional model trained on our corpus
yields better results than models trained on smaller corpora, like Wikipedia.
This distributional model outperforms the state of art models of verb
similarity trained on smaller corpora on the SimVerb3500 dataset.
| 2,018 | Computation and Language |
Enhanced Neural Machine Translation by Learning from Draft | Neural machine translation (NMT) has recently achieved impressive results. A
potential problem of the existing NMT algorithm, however, is that the decoding
is conducted from left to right, without considering the right context. This
paper proposes an two-stage approach to solve the problem. In the first stage,
a conventional attention-based NMT system is used to produce a draft
translation, and in the second stage, a novel double-attention NMT system is
used to refine the translation, by looking at the original input as well as the
draft translation. This drafting-and-refinement can obtain the right-context
information from the draft, hence producing more consistent translations. We
evaluated this approach using two Chinese-English translation tasks, one with
44k pairs and 1M pairs respectively. The experiments showed that our approach
achieved positive improvements over the conventional NMT system: the
improvements are 2.4 and 0.9 BLEU points on the small-scale and large-scale
tasks, respectively.
| 2,017 | Computation and Language |
Counterfactual Language Model Adaptation for Suggesting Phrases | Mobile devices use language models to suggest words and phrases for use in
text entry. Traditional language models are based on contextual word frequency
in a static corpus of text. However, certain types of phrases, when offered to
writers as suggestions, may be systematically chosen more often than their
frequency would predict. In this paper, we propose the task of generating
suggestions that writers accept, a related but distinct task to making accurate
predictions. Although this task is fundamentally interactive, we propose a
counterfactual setting that permits offline training and evaluation. We find
that even a simple language model can capture text characteristics that improve
acceptability.
| 2,017 | Computation and Language |
Syntactic and Semantic Features For Code-Switching Factored Language
Models | This paper presents our latest investigations on different features for
factored language models for Code-Switching speech and their effect on
automatic speech recognition (ASR) performance. We focus on syntactic and
semantic features which can be extracted from Code-Switching text data and
integrate them into factored language models. Different possible factors, such
as words, part-of-speech tags, Brown word clusters, open class words and
clusters of open class word embeddings are explored. The experimental results
reveal that Brown word clusters, part-of-speech tags and open-class words are
the most effective at reducing the perplexity of factored language models on
the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model
containing Brown word clusters and part-of-speech tags and the model also
including clusters of open class word embeddings yield the best mixed error
rate results. In summary, the best language model can significantly reduce the
perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed
error rate by up to 3.4% relative.
| 2,017 | Computation and Language |
Semantic speech retrieval with a visually grounded model of
untranscribed speech | There is growing interest in models that can learn from unlabelled speech
paired with visual context. This setting is relevant for low-resource speech
processing, robotics, and human language acquisition research. Here we study
how a visually grounded speech model, trained on images of scenes paired with
spoken captions, captures aspects of semantics. We use an external image tagger
to generate soft text labels from images, which serve as targets for a neural
model that maps untranscribed speech to (semantic) keyword labels. We introduce
a newly collected data set of human semantic relevance judgements and an
associated task, semantic speech retrieval, where the goal is to search for
spoken utterances that are semantically relevant to a given text query. Without
seeing any text, the model trained on parallel speech and images achieves a
precision of almost 60% on its top ten semantic retrievals. Compared to a
supervised model trained on transcriptions, our model matches human judgements
better by some measures, especially in retrieving non-verbatim semantic
matches. We perform an extensive analysis of the model and its resulting
representations.
| 2,019 | Computation and Language |
Machine Learning Based Detection of Clickbait Posts in Social Media | Clickbait (headlines) make use of misleading titles that hide critical
information from or exaggerate the content on the landing target pages to
entice clicks. As clickbaits often use eye-catching wording to attract viewers,
target contents are often of low quality. Clickbaits are especially widespread
on social media such as Twitter, adversely impacting user experience by causing
immense dissatisfaction. Hence, it has become increasingly important to put
forward a widely applicable approach to identify and detect clickbaits. In this
paper, we make use of a dataset from the clickbait challenge 2017
(clickbait-challenge.com) comprising of over 21,000 headlines/titles, each of
which is annotated by at least five judgments from crowdsourcing on how
clickbait it is. We attempt to build an effective computational clickbait
detection model on this dataset. We first considered a total of 331 features,
filtered out many features to avoid overfitting and improve the running time of
learning, and eventually selected the 60 most important features for our final
model. Using these features, Random Forest Regression achieved the following
results: MSE=0.035 MSE, Accuracy=0.82, and F1-sore=0.61 on the clickbait class.
| 2,017 | Computation and Language |
On the Effective Use of Pretraining for Natural Language Inference | Neural networks have excelled at many NLP tasks, but there remain open
questions about the performance of pretrained distributed word representations
and their interaction with weight initialization and other hyperparameters. We
address these questions empirically using attention-based sequence-to-sequence
models for natural language inference (NLI). Specifically, we compare three
types of embeddings: random, pretrained (GloVe, word2vec), and retrofitted
(pretrained plus WordNet information). We show that pretrained embeddings
outperform both random and retrofitted ones in a large NLI corpus. Further
experiments on more controlled data sets shed light on the contexts for which
retrofitted embeddings can be useful. We also explore two principled approaches
to initializing the rest of the model parameters, Gaussian and orthogonal,
showing that the latter yields gains of up to 2.9% in the NLI task.
| 2,017 | Computation and Language |
Indowordnets help in Indian Language Machine Translation | Being less resource languages, Indian-Indian and English-Indian language MT
system developments faces the difficulty to translate various lexical
phenomena. In this paper, we present our work on a comparative study of 440
phrase-based statistical trained models for 110 language pairs across 11 Indian
languages. We have developed 110 baseline Statistical Machine Translation
systems. Then we have augmented the training corpus with Indowordnet synset
word entries of lexical database and further trained 110 models on top of the
baseline system. We have done a detailed performance comparison using various
evaluation metrics such as BLEU score, METEOR and TER. We observed significant
improvement in evaluations of translation quality across all the 440 models
after using the Indowordnet. These experiments give a detailed insight in two
ways : (1) usage of lexical database with synset mapping for resource poor
languages (2) efficient usage of Indowordnet sysnset mapping. More over, synset
mapped lexical entries helped the SMT system to handle the ambiguity to a great
extent during the translation.
| 2,017 | Computation and Language |
Morphology Generation for Statistical Machine Translation | When translating into morphologically rich languages, Statistical MT
approaches face the problem of data sparsity. The severity of the sparseness
problem will be high when the corpus size of morphologically richer language is
less. Even though we can use factored models to correctly generate
morphological forms of words, the problem of data sparseness limits their
performance. In this paper, we describe a simple and effective solution which
is based on enriching the input corpora with various morphological forms of
words. We use this method with the phrase-based and factor-based experiments on
two morphologically rich languages: Hindi and Marathi when translating from
English. We evaluate the performance of our experiments both in terms automatic
evaluation and subjective evaluation such as adequacy and fluency. We observe
that the morphology injection method helps in improving the quality of
translation. We further analyze that the morph injection method helps in
handling the data sparseness problem to a great level.
| 2,017 | Computation and Language |
Machine Translation Evaluation with Neural Networks | We present a framework for machine translation evaluation using neural
networks in a pairwise setting, where the goal is to select the better
translation from a pair of hypotheses, given the reference translation. In this
framework, lexical, syntactic and semantic information from the reference and
the two hypotheses is embedded into compact distributed vector representations,
and fed into a multi-layer neural network that models nonlinear interactions
between each of the hypotheses and the reference, as well as between the two
hypotheses. We experiment with the benchmark datasets from the WMT Metrics
shared task, on which we obtain the best results published so far, with the
basic network configuration. We also perform a series of experiments to analyze
and understand the contribution of the different components of the network. We
evaluate variants and extensions, including fine-tuning of the semantic
embeddings, and sentence-based representations modeled with convolutional and
recurrent neural networks. In summary, the proposed framework is flexible and
generalizable, allows for efficient learning and scoring, and provides an MT
evaluation metric that correlates with human judgments, and is on par with the
state of the art.
| 2,017 | Computation and Language |
Phrase Pair Mappings for Hindi-English Statistical Machine Translation | In this paper, we present our work on the creation of lexical resources for
the Machine Translation between English and Hindi. We describes the development
of phrase pair mappings for our experiments and the comparative performance
evaluation between different trained models on top of the baseline Statistical
Machine Translation system. We focused on augmenting the parallel corpus with
more vocabulary as well as with various inflected forms by exploring different
ways. We have augmented the training corpus with various lexical resources such
as lexical words, synset words, function words and verb phrases. We have
described the case studies, automatic and subjective evaluations, detailed
error analysis for both the English to Hindi and Hindi to English machine
translation systems. We further analyzed that, there is an incremental growth
in the quality of machine translation with the usage of various lexical
resources. Thus lexical resources do help uplift the translation quality of
resource poor langugaes.
| 2,017 | Computation and Language |
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages | We present BPEmb, a collection of pre-trained subword unit embeddings in 275
languages, based on Byte-Pair Encoding (BPE). In an evaluation using
fine-grained entity typing as testbed, BPEmb performs competitively, and for
some languages bet- ter than alternative subword approaches, while requiring
vastly fewer resources and no tokenization. BPEmb is available at
https://github.com/bheinzerling/bpemb
| 2,017 | Computation and Language |
A Semantic Relevance Based Neural Network for Text Summarization and
Text Simplification | Text summarization and text simplification are two major ways to simplify the
text for poor readers, including children, non-native speakers, and the
functionally illiterate. Text summarization is to produce a brief summary of
the main ideas of the text, while text simplification aims to reduce the
linguistic complexity of the text and retain the original meaning. Recently,
most approaches for text summarization and text simplification are based on the
sequence-to-sequence model, which achieves much success in many text generation
tasks. However, although the generated simplified texts are similar to source
texts literally, they have low semantic relevance. In this work, our goal is to
improve semantic relevance between source texts and simplified texts for text
summarization and text simplification. We introduce a Semantic Relevance Based
neural model to encourage high semantic similarity between texts and summaries.
In our model, the source text is represented by a gated attention encoder,
while the summary representation is produced by a decoder. Besides, the
similarity score between the representations is maximized during training. Our
experiments show that the proposed model outperforms the state-of-the-art
systems on two benchmark corpus.
| 2,017 | Computation and Language |
Czech Text Document Corpus v 2.0 | This paper introduces "Czech Text Document Corpus v 2.0", a collection of
text documents for automatic document classification in Czech language. It is
composed of the text documents provided by the Czech News Agency and is freely
available for research purposes at http://ctdc.kiv.zcu.cz/. This corpus was
created in order to facilitate a straightforward comparison of the document
classification approaches on Czech data. It is particularly dedicated to
evaluation of multi-label document classification approaches, because one
document is usually labelled with more than one label. Besides the information
about the document classes, the corpus is also annotated at the morphological
layer. This paper further shows the results of selected state-of-the-art
methods on this corpus to offer the possibility of an easy comparison with
these approaches.
| 2,018 | Computation and Language |
Bilingual Words and Phrase Mappings for Marathi and Hindi SMT | Lack of proper linguistic resources is the major challenges faced by the
Machine Translation system developments when dealing with the resource poor
languages. In this paper, we describe effective ways to utilize the lexical
resources to improve the quality of statistical machine translation. Our
research on the usage of lexical resources mainly focused on two ways, such as;
augmenting the parallel corpus with more vocabulary and to provide various word
forms. We have augmented the training corpus with various lexical resources
such as lexical words, function words, kridanta pairs and verb phrases. We have
described the case studies, evaluations and detailed error analysis for both
Marathi to Hindi and Hindi to Marathi machine translation systems. From the
evaluations we observed that, there is an incremental growth in the quality of
machine translation as the usage of various lexical resources increases.
Moreover, usage of various lexical resources helps to improve the coverage and
quality of machine translation where limited parallel corpus is available.
| 2,017 | Computation and Language |
Learning Word Embeddings for Hyponymy with Entailment-Based
Distributional Semantics | Lexical entailment, such as hyponymy, is a fundamental issue in the semantics
of natural language. This paper proposes distributional semantic models which
efficiently learn word embeddings for entailment, using a recently-proposed
framework for modelling entailment in a vector-space. These models postulate a
latent vector for a pseudo-phrase containing two neighbouring word vectors. We
investigate both modelling words as the evidence they contribute about this
phrase vector, or as the posterior distribution of a one-word phrase vector,
and find that the posterior vectors perform better. The resulting word
embeddings outperform the best previous results on predicting hyponymy between
words, in unsupervised and semi-supervised experiments.
| 2,017 | Computation and Language |
On the Challenges of Sentiment Analysis for Dynamic Events | With the proliferation of social media over the last decade, determining
people's attitude with respect to a specific topic, document, interaction or
events has fueled research interest in natural language processing and
introduced a new channel called sentiment and emotion analysis. For instance,
businesses routinely look to develop systems to automatically understand their
customer conversations by identifying the relevant content to enhance marketing
their products and managing their reputations. Previous efforts to assess
people's sentiment on Twitter have suggested that Twitter may be a valuable
resource for studying political sentiment and that it reflects the offline
political landscape. According to a Pew Research Center report, in January 2016
44 percent of US adults stated having learned about the presidential election
through social media. Furthermore, 24 percent reported use of social media
posts of the two candidates as a source of news and information, which is more
than the 15 percent who have used both candidates' websites or emails combined.
The first presidential debate between Trump and Hillary was the most tweeted
debate ever with 17.1 million tweets.
| 2,017 | Computation and Language |
Low-resource bilingual lexicon extraction using graph based word
embeddings | In this work we focus on the task of automatically extracting bilingual
lexicon for the language pair Spanish-Nahuatl. This is a low-resource setting
where only a small amount of parallel corpus is available. Most of the
downstream methods do not work well under low-resources conditions. This is
specially true for the approaches that use vectorial representations like
Word2Vec. Our proposal is to construct bilingual word vectors from a graph.
This graph is generated using translation pairs obtained from an unsupervised
word alignment method.
We show that, in a low-resource setting, these type of vectors are successful
in representing words in a bilingual semantic space. Moreover, when a linear
transformation is applied to translate words from one language to another, our
graph based representations considerably outperform the popular setting that
uses Word2Vec.
| 2,017 | Computation and Language |
Low-Rank RNN Adaptation for Context-Aware Language Modeling | A context-aware language model uses location, user and/or domain metadata
(context) to adapt its predictions. In neural language models, context
information is typically represented as an embedding and it is given to the RNN
as an additional input, which has been shown to be useful in many applications.
We introduce a more powerful mechanism for using context to adapt an RNN by
letting the context vector control a low-rank transformation of the recurrent
layer weight matrix. Experiments show that allowing a greater fraction of the
model parameters to be adjusted has benefits in terms of perplexity and
classification for several different types of context.
| 2,018 | Computation and Language |
Topic Modeling based on Keywords and Context | Current topic models often suffer from discovering topics not matching human
intuition, unnatural switching of topics within documents and high
computational demands. We address these concerns by proposing a topic model and
an inference algorithm based on automatically identifying characteristic
keywords for topics. Keywords influence topic-assignments of nearby words. Our
algorithm learns (key)word-topic scores and it self-regulates the number of
topics. Inference is simple and easily parallelizable. Qualitative analysis
yields comparable results to state-of-the-art models (eg. LDA), but with
different strengths and weaknesses. Quantitative analysis using 9 datasets
shows gains in terms of classification accuracy, PMI score, computational
performance and consistency of topic assignments within documents, while most
often using less topics.
| 2,018 | Computation and Language |
Group Sparse CNNs for Question Classification with Answer Sets | Question classification is an important task with wide applications. However,
traditional techniques treat questions as general sentences, ignoring the
corresponding answer data. In order to consider answer information into
question modeling, we first introduce novel group sparse autoencoders which
refine question representation by utilizing group information in the answer
set. We then propose novel group sparse CNNs which naturally learn question
representation with respect to their answers by implanting group sparse
autoencoders into traditional CNNs. The proposed model significantly outperform
strong baselines on four datasets.
| 2,017 | Computation and Language |
OSU Multimodal Machine Translation System Report | This paper describes Oregon State University's submissions to the shared
WMT'17 task "multimodal translation task I". In this task, all the sentence
pairs are image captions in different languages. The key difference between
this task and conventional machine translation is that we have corresponding
images as additional information for each sentence pair. In this paper, we
introduce a simple but effective system which takes an image shared between
different languages, feeding it into the both encoding and decoding side. We
report our system's performance for English-French and English-German with
Flickr30K (in-domain) and MSCOCO (out-of-domain) datasets. Our system achieves
the best performance in TER for English-German for MSCOCO dataset.
| 2,017 | Computation and Language |
Multi-Document Summarization using Distributed Bag-of-Words Model | As the number of documents on the web is growing exponentially,
multi-document summarization is becoming more and more important since it can
provide the main ideas in a document set in short time. In this paper, we
present an unsupervised centroid-based document-level reconstruction framework
using distributed bag of words model. Specifically, our approach selects
summary sentences in order to minimize the reconstruction error between the
summary and the documents. We apply sentence selection and beam search, to
further improve the performance of our model. Experimental results on two
different datasets show significant performance gains compared with the
state-of-the-art baselines.
| 2,018 | Computation and Language |
Smarnet: Teaching Machines to Read and Comprehend Like Human | Machine Comprehension (MC) is a challenging task in Natural Language
Processing field, which aims to guide the machine to comprehend a passage and
answer the given question. Many existing approaches on MC task are suffering
the inefficiency in some bottlenecks, such as insufficient lexical
understanding, complex question-passage interaction, incorrect answer
extraction and so on. In this paper, we address these problems from the
viewpoint of how humans deal with reading tests in a scientific way.
Specifically, we first propose a novel lexical gating mechanism to dynamically
combine the words and characters representations. We then guide the machines to
read in an interactive way with attention mechanism and memory network. Finally
we add a checking layer to refine the answer for insurance. The extensive
experiments on two popular datasets SQuAD and TriviaQA show that our method
exceeds considerable performance than most state-of-the-art solutions at the
time of submission.
| 2,017 | Computation and Language |
The IIT Bombay English-Hindi Parallel Corpus | We present the IIT Bombay English-Hindi Parallel Corpus. The corpus is a
compilation of parallel corpora previously available in the public domain as
well as new parallel corpora we collected. The corpus contains 1.49 million
parallel segments, of which 694k segments were not previously available in the
public domain. The corpus has been pre-processed for machine translation, and
we report baseline phrase-based SMT and NMT translation results on this corpus.
This corpus has been used in two editions of shared tasks at the Workshop on
Asian Language Translation (2016 and 2017). The corpus is freely available for
non-commercial research. To the best of our knowledge, this is the largest
publicly available English-Hindi parallel corpus.
| 2,018 | Computation and Language |
Clickbait detection using word embeddings | Clickbait is a pejorative term describing web content that is aimed at
generating online advertising revenue, especially at the expense of quality or
accuracy, relying on sensationalist headlines or eye-catching thumbnail
pictures to attract click-throughs and to encourage forwarding of the material
over online social networks. We use distributed word representations of the
words in the title as features to identify clickbaits in online news media. We
train a machine learning model using linear regression to predict the cickbait
score of a given tweet. Our methods achieve an F1-score of 64.98\% and an MSE
of 0.0791. Compared to other methods, our method is simple, fast to train, does
not require extensive feature engineering and yet moderately effective.
| 2,017 | Computation and Language |
Natural Language Inference from Multiple Premises | We define a novel textual entailment task that requires inference over
multiple premise sentences. We present a new dataset for this task that
minimizes trivial lexical inferences, emphasizes knowledge of everyday events,
and presents a more challenging setting for textual entailment. We evaluate
several strong neural baselines and analyze how the multiple premise task
differs from standard textual entailment.
| 2,017 | Computation and Language |
Page Stream Segmentation with Convolutional Neural Nets Combining
Textual and Visual Features | In recent years, (retro-)digitizing paper-based files became a major
undertaking for private and public archives as well as an important task in
electronic mailroom applications. As a first step, the workflow involves
scanning and Optical Character Recognition (OCR) of documents. Preservation of
document contexts of single page scans is a major requirement in this context.
To facilitate workflows involving very large amounts of paper scans, page
stream segmentation (PSS) is the task to automatically separate a stream of
scanned images into multi-page documents. In a digitization project together
with a German federal archive, we developed a novel approach based on
convolutional neural networks (CNN) combining image and text features to
achieve optimal document separation results. Evaluation shows that our PSS
architecture achieves an accuracy up to 93 % which can be regarded as a new
state-of-the-art for this task.
| 2,018 | Computation and Language |
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for
Multilingual Sentiment Analysis | The surge of social media use brings huge demand of multilingual sentiment
analysis (MSA) for unveiling cultural difference. So far, traditional methods
resorted to machine translation---translating texts in other languages to
English, and then adopt the methods once worked in English. However, this
paradigm is conditioned by the quality of machine translation. In this paper,
we propose a new deep learning paradigm to assimilate the differences between
languages for MSA. We first pre-train monolingual word embeddings separately,
then map word embeddings in different spaces into a shared embedding space, and
then finally train a parameter-sharing deep neural network for MSA. The
experimental results show that our paradigm is effective. Especially, our CNN
model outperforms a state-of-the-art baseline by around 2.1% in terms of
classification accuracy.
| 2,017 | Computation and Language |
Multitask training with unlabeled data for end-to-end sign language
fingerspelling recognition | We address the problem of automatic American Sign Language fingerspelling
recognition from video. Prior work has largely relied on frame-level labels,
hand-crafted features, or other constraints, and has been hampered by the
scarcity of data for this task. We introduce a model for fingerspelling
recognition that addresses these issues. The model consists of an
auto-encoder-based feature extractor and an attention-based neural
encoder-decoder, which are trained jointly. The model receives a sequence of
image frames and outputs the fingerspelled word, without relying on any
frame-level training labels or hand-crafted features. In addition, the
auto-encoder subcomponent makes it possible to leverage unlabeled data to
improve the feature learning. The model achieves 11.6% and 4.4% absolute letter
accuracy improvement respectively in signer-independent and signer-adapted
fingerspelling recognition over previous approaches that required frame-level
training labels.
| 2,019 | Computation and Language |
What does Attention in Neural Machine Translation Pay Attention to? | Attention in neural machine translation provides the possibility to encode
relevant parts of the source sentence at each translation step. As a result,
attention is considered to be an alignment model as well. However, there is no
work that specifically studies attention and provides analysis of what is being
learned by attention models. Thus, the question still remains that how
attention is similar or different from the traditional alignment. In this
paper, we provide detailed analysis of attention and compare it to traditional
alignment. We answer the question of whether attention is only capable of
modelling translational equivalent or it captures more information. We show
that attention is different from alignment in some cases and is capturing
useful information other than alignments.
| 2,017 | Computation and Language |
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent
Encoder with Latent Topic Clustering | In this paper, we propose a novel end-to-end neural architecture for ranking
candidate answers, that adapts a hierarchical recurrent neural network and a
latent topic clustering module. With our proposed model, a text is encoded to a
vector representation from an word-level to a chunk-level to effectively
capture the entire meaning. In particular, by adapting the hierarchical
structure, our model shows very small performance degradations in longer text
comprehension while other state-of-the-art recurrent neural network models
suffer from it. Additionally, the latent topic clustering module extracts
semantic information from target samples. This clustering module is useful for
any text related tasks by allowing each data sample to find its nearest topic
cluster, thus helping the neural network model analyze the entire data. We
evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic
domain question answering dataset, which is related to Samsung products. The
proposed model shows state-of-the-art results for ranking question-answer
pairs.
| 2,018 | Computation and Language |
MoNoise: Modeling Noise Using a Modular Normalization System | We propose MoNoise: a normalization model focused on generalizability and
efficiency, it aims at being easily reusable and adaptable. Normalization is
the task of translating texts from a non- canonical domain to a more canonical
domain, in our case: from social media data to standard language. Our proposed
model is based on a modular candidate generation in which each module is
responsible for a different type of normalization action. The most important
generation modules are a spelling correction system and a word embeddings
module. Depending on the definition of the normalization task, a static lookup
list can be crucial for performance. We train a random forest classifier to
rank the candidates, which generalizes well to all different types of
normaliza- tion actions. Most features for the ranking originate from the
generation modules; besides these features, N-gram features prove to be an
important source of information. We show that MoNoise beats the
state-of-the-art on different normalization benchmarks for English and Dutch,
which all define the task of normalization slightly different.
| 2,017 | Computation and Language |
A Very Low Resource Language Speech Corpus for Computational Language
Documentation Experiments | Most speech and language technologies are trained with massive amounts of
speech and text information. However, most of the world languages do not have
such resources or stable orthography. Systems constructed under these almost
zero resource conditions are not only promising for speech technology but also
for computational language documentation. The goal of computational language
documentation is to help field linguists to (semi-)automatically analyze and
annotate audio recordings of endangered and unwritten languages. Example tasks
are automatic phoneme discovery or lexicon discovery from the speech signal.
This paper presents a speech corpus collected during a realistic language
documentation process. It is made up of 5k speech utterances in Mboshi (Bantu
C25) aligned to French text translations. Speech transcriptions are also made
available: they correspond to a non-standard graphemic form close to the
language phonology. We present how the data was collected, cleaned and
processed and we illustrate its use through a zero-resource task: spoken term
discovery. The dataset is made available to the community for reproducible
computational language documentation experiments and their evaluation.
| 2,018 | Computation and Language |
Confidence through Attention | Attention distributions of the generated translations are a useful bi-product
of attention-based recurrent neural network translation models and can be
treated as soft alignments between the input and output tokens. In this work,
we use attention distributions as a confidence metric for output translations.
We present two strategies of using the attention distributions: filtering out
bad translations from a large back-translated corpus, and selecting the best
translation in a hybrid setup of two different translation systems. While
manual evaluation indicated only a weak correlation between our confidence
score and human judgments, the use-cases showed improvements of up to 2.22 BLEU
points for filtering and 0.99 points for hybrid translation, tested on
English<->German and English<->Latvian translation.
| 2,017 | Computation and Language |
The Galactic Dependencies Treebanks: Getting More Data by Synthesizing
New Languages | We release Galactic Dependencies 1.0---a large set of synthetic languages not
found on Earth, but annotated in Universal Dependencies format. This new
resource aims to provide training and development data for NLP methods that aim
to adapt to unfamiliar languages. Each synthetic treebank is produced from a
real treebank by stochastically permuting the dependents of nouns and/or verbs
to match the word order of other real languages. We discuss the usefulness,
realism, parsability, perplexity, and diversity of the synthetic languages. As
a simple demonstration of the use of Galactic Dependencies, we consider
single-source transfer, which attempts to parse a real target language using a
parser trained on a "nearby" source language. We find that including synthetic
source languages somewhat increases the diversity of the source pool, which
significantly improves results for most target languages.
| 2,016 | Computation and Language |
Fine-Grained Prediction of Syntactic Typology: Discovering Latent
Structure with Supervised Learning | We show how to predict the basic word-order facts of a novel language given
only a corpus of part-of-speech (POS) sequences. We predict how often direct
objects follow their verbs, how often adjectives follow their nouns, and in
general the directionalities of all dependency relations. Such typological
properties could be helpful in grammar induction. While such a problem is
usually regarded as unsupervised learning, our innovation is to treat it as
supervised learning, using a large collection of realistic synthetic languages
as training data. The supervised learner must identify surface features of a
language's POS sequence (hand-engineered or neural features) that correlate
with the language's deeper structure (latent trees). In the experiment, we
show: 1) Given a small set of real languages, it helps to add many synthetic
languages to the training data. 2) Our system is robust even when the POS
sequences include noise. 3) Our system on this task outperforms a grammar
induction baseline by a large margin.
| 2,017 | Computation and Language |
Decision support from financial disclosures with deep neural networks
and transfer learning | Company disclosures greatly aid in the process of financial decision-making;
therefore, they are consulted by financial investors and automated traders
before exercising ownership in stocks. While humans are usually able to
correctly interpret the content, the same is rarely true of computerized
decision support systems, which struggle with the complexity and ambiguity of
natural language. A possible remedy is represented by deep learning, which
overcomes several shortcomings of traditional methods of text mining. For
instance, recurrent neural networks, such as long short-term memories, employ
hierarchical structures, together with a large number of hidden layers, to
automatically extract features from ordered sequences of words and capture
highly non-linear relationships such as context-dependent meanings. However,
deep learning has only recently started to receive traction, possibly because
its performance is largely untested. Hence, this paper studies the use of deep
neural networks for financial decision support. We additionally experiment with
transfer learning, in which we pre-train the network on a different corpus with
a length of 139.1 million words. Our results reveal a higher directional
accuracy as compared to traditional machine learning when predicting stock
price movements in response to financial disclosures. Our work thereby helps to
highlight the business value of deep learning and provides recommendations to
practitioners and executives.
| 2,017 | Computation and Language |
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset | We develop a high-quality multi-turn dialog dataset, DailyDialog, which is
intriguing in several aspects. The language is human-written and less noisy.
The dialogues in the dataset reflect our daily communication way and cover
various topics about our daily life. We also manually label the developed
dataset with communication intention and emotion information. Then, we evaluate
existing approaches on DailyDialog dataset and hope it benefit the research
field of dialog systems.
| 2,017 | Computation and Language |
Word Translation Without Parallel Data | State-of-the-art methods for learning cross-lingual word embeddings have
relied on bilingual dictionaries or parallel corpora. Recent studies showed
that the need for parallel data supervision can be alleviated with
character-level information. While these methods showed encouraging results,
they are not on par with their supervised counterparts and are limited to pairs
of languages sharing a common alphabet. In this work, we show that we can build
a bilingual dictionary between two languages without using any parallel
corpora, by aligning monolingual word embedding spaces in an unsupervised way.
Without using any character information, our model even outperforms existing
supervised methods on cross-lingual tasks for some language pairs. Our
experiments demonstrate that our method works very well also for distant
language pairs, like English-Russian or English-Chinese. We finally describe
experiments on the English-Esperanto low-resource language pair, on which there
only exists a limited amount of parallel data, to show the potential impact of
our method in fully unsupervised machine translation. Our code, embeddings and
dictionaries are publicly available.
| 2,018 | Computation and Language |
Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion
Lexicon | Sentiment analysis aims to uncover emotions conveyed through information. In
its simplest form, it is performed on a polarity basis, where the goal is to
classify information with positive or negative emotion. Recent research has
explored more nuanced ways to capture emotions that go beyond polarity. For
these methods to work, they require a critical resource: a lexicon that is
appropriate for the task at hand, in terms of the range of emotions it captures
diversity. In the past, sentiment analysis lexicons have been created by
experts, such as linguists and behavioural scientists, with strict rules.
Lexicon evaluation was also performed by experts or gold standards. In our
paper, we propose a crowdsourcing method for lexicon acquisition, which is
scalable, cost-effective, and doesn't require experts or gold standards. We
also compare crowd and expert evaluations of the lexicon, to assess the overall
lexicon quality, and the evaluation capabilities of the crowd.
| 2,017 | Computation and Language |
DisSent: Sentence Representation Learning from Explicit Discourse
Relations | Learning effective representations of sentences is one of the core missions
of natural language understanding. Existing models either train on a vast
amount of text, or require costly, manually curated sentence relation datasets.
We show that with dependency parsing and rule-based rubrics, we can curate a
high quality sentence relation task by leveraging explicit discourse relations.
We show that our curated dataset provides an excellent signal for learning
vector representations of sentence meaning, representing relations that can
only be determined when the meanings of two sentences are combined. We
demonstrate that the automatically curated corpus allows a bidirectional LSTM
sentence encoder to yield high quality sentence embeddings and can serve as a
supervised fine-tuning dataset for larger models such as BERT. Our fixed
sentence embeddings achieve high performance on a variety of transfer tasks,
including SentEval, and we achieve state-of-the-art results on Penn Discourse
Treebank's implicit relation prediction task.
| 2,019 | Computation and Language |
Using Context Events in Neural Network Models for Event Temporal Status
Identification | Focusing on the task of identifying event temporal status, we find that
events directly or indirectly governing the target event in a dependency tree
are most important contexts. Therefore, we extract dependency chains containing
context events and use them as input in neural network models, which
consistently outperform previous models using local context words as input.
Visualization verifies that the dependency chain representation can effectively
capture the context events which are closely related to the target event and
play key roles in predicting event temporal status.
| 2,017 | Computation and Language |
Revisiting the Design Issues of Local Models for Japanese
Predicate-Argument Structure Analysis | The research trend in Japanese predicate-argument structure (PAS) analysis is
shifting from pointwise prediction models with local features to global models
designed to search for globally optimal solutions. However, the existing global
models tend to employ only relatively simple local features; therefore, the
overall performance gains are rather limited. The importance of designing a
local model is demonstrated in this study by showing that the performance of a
sophisticated local model can be considerably improved with recent feature
embedding methods and a feature combination learning based on a neural network,
outperforming the state-of-the-art global models in $F_1$ on a common benchmark
dataset.
| 2,017 | Computation and Language |
Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR | This thesis introduces the sequence to sequence model with Luong's attention
mechanism for end-to-end ASR. It also describes various neural network
algorithms including Batch normalization, Dropout and Residual network which
constitute the convolutional attention-based seq2seq neural network. Finally
the proposed model proved its effectiveness for speech recognition achieving
15.8% phoneme error rate on TIMIT dataset.
| 2,017 | Computation and Language |
Auto Analysis of Customer Feedback using CNN and GRU Network | Analyzing customer feedback is the best way to channelize the data into new
marketing strategies that benefit entrepreneurs as well as customers. Therefore
an automated system which can analyze the customer behavior is in great demand.
Users may write feedbacks in any language, and hence mining appropriate
information often becomes intractable. Especially in a traditional
feature-based supervised model, it is difficult to build a generic system as
one has to understand the concerned language for finding the relevant features.
In order to overcome this, we propose deep Convolutional Neural Network (CNN)
and Recurrent Neural Network (RNN) based approaches that do not require
handcrafting of features. We evaluate these techniques for analyzing customer
feedback sentences in four languages, namely English, French, Japanese and
Spanish. Our empirical analysis shows that our models perform well in all the
four languages on the setups of IJCNLP Shared Task on Customer Feedback
Analysis. Our model achieved the second rank in French, with an accuracy of
71.75% and third ranks for all the other languages.
| 2,017 | Computation and Language |
End-to-end Network for Twitter Geolocation Prediction and Hashing | We propose an end-to-end neural network to predict the geolocation of a
tweet. The network takes as input a number of raw Twitter metadata such as the
tweet message and associated user account information. Our model is language
independent, and despite minimal feature engineering, it is interpretable and
capable of learning location indicative words and timing patterns. Compared to
state-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we
propose extensions to the model to compress representation learnt by the
network into binary codes. Experiments show that it produces compact codes
compared to benchmark hashing algorithms. An implementation of the model is
released publicly.
| 2,017 | Computation and Language |
Complex Word Identification: Challenges in Data Annotation and System
Performance | This paper revisits the problem of complex word identification (CWI)
following up the SemEval CWI shared task. We use ensemble classifiers to
investigate how well computational methods can discriminate between complex and
non-complex words. Furthermore, we analyze the classification performance to
understand what makes lexical complexity challenging. Our findings show that
most systems performed poorly on the SemEval CWI dataset, and one of the
reasons for that is the way in which human annotation was performed.
| 2,017 | Computation and Language |
Learning Phrase Embeddings from Paraphrases with GRUs | Learning phrase representations has been widely explored in many Natural
Language Processing (NLP) tasks (e.g., Sentiment Analysis, Machine Translation)
and has shown promising improvements. Previous studies either learn
non-compositional phrase representations with general word embedding learning
techniques or learn compositional phrase representations based on syntactic
structures, which either require huge amounts of human annotations or cannot be
easily generalized to all phrases. In this work, we propose to take advantage
of large-scaled paraphrase database and present a pair-wise gated recurrent
units (pairwise-GRU) framework to generate compositional phrase
representations. Our framework can be re-used to generate representations for
any phrases. Experimental results show that our framework achieves
state-of-the-art results on several phrase similarity tasks.
| 2,017 | Computation and Language |
Clickbait Detection in Tweets Using Self-attentive Network | Clickbait detection in tweets remains an elusive challenge. In this paper, we
describe the solution for the Zingel Clickbait Detector at the Clickbait
Challenge 2017, which is capable of evaluating each tweet's level of click
baiting. We first reformat the regression problem as a multi-classification
problem, based on the annotation scheme. To perform multi-classification, we
apply a token-level, self-attentive mechanism on the hidden states of
bi-directional Gated Recurrent Units (biGRU), which enables the model to
generate tweets' task-specific vector representations by attending to important
tokens. The self-attentive neural network can be trained end-to-end, without
involving any manual feature engineering. Our detector ranked first in the
final evaluation of Clickbait Challenge 2017.
| 2,017 | Computation and Language |
NoReC: The Norwegian Review Corpus | This paper presents the Norwegian Review Corpus (NoReC), created for training
and evaluating models for document-level sentiment analysis. The full-text
reviews have been collected from major Norwegian news sources and cover a range
of different domains, including literature, movies, video games, restaurants,
music and theater, in addition to product reviews across a range of categories.
Each review is labeled with a manually assigned score of 1-6, as provided by
the rating of the original author. This first release of the corpus comprises
more than 35,000 reviews. It is distributed using the CoNLL-U format,
pre-processed using UDPipe, along with a rich set of metadata. The work
reported in this paper forms part of the SANT initiative (Sentiment Analysis
for Norwegian Text), a project seeking to provide resources and tools for
sentiment analysis and opinion mining for Norwegian. As resources for sentiment
analysis have so far been unavailable for Norwegian, NoReC represents a highly
valuable and sought-after addition to Norwegian language technology.
| 2,017 | Computation and Language |
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in
Social Media | With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.
| 2,017 | Computation and Language |
BKTreebank: Building a Vietnamese Dependency Treebank | Dependency treebank is an important resource in any language. In this paper,
we present our work on building BKTreebank, a dependency treebank for
Vietnamese. Important points on designing POS tagset, dependency relations, and
annotation guidelines are discussed. We describe experiments on POS tagging and
dependency parsing on the treebank. Experimental results show that the treebank
is a useful resource for Vietnamese language processing.
| 2,018 | Computation and Language |
Aligning Script Events with Narrative Texts | Script knowledge plays a central role in text understanding and is relevant
for a variety of downstream tasks. In this paper, we consider two recent
datasets which provide a rich and general representation of script events in
terms of paraphrase sets. We introduce the task of mapping event mentions in
narrative texts to such script event types, and present a model for this task
that exploits rich linguistic representations as well as information on
temporal ordering. The results of our experiments demonstrate that this complex
task is indeed feasible.
| 2,019 | Computation and Language |
A retrieval-based dialogue system utilizing utterance and context
embeddings | Finding semantically rich and computer-understandable representations for
textual dialogues, utterances and words is crucial for dialogue systems (or
conversational agents), as their performance mostly depends on understanding
the context of conversations. Recent research aims at finding distributed
vector representations (embeddings) for words, such that semantically similar
words are relatively close within the vector-space. Encoding the "meaning" of
text into vectors is a current trend, and text can range from words, phrases
and documents to actual human-to-human conversations. In recent research
approaches, responses have been generated utilizing a decoder architecture,
given the vector representation of the current conversation. In this paper, the
utilization of embeddings for answer retrieval is explored by using
Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
(ANN) model, to find similar conversations in a corpus and rank possible
candidates. Experimental results on the well-known Ubuntu Corpus (in English)
and a customer service chat dataset (in Dutch) show that, in combination with a
candidate selection method, retrieval-based approaches outperform generative
ones and reveal promising future research directions towards the usability of
such a system.
| 2,017 | Computation and Language |
Convolutional Neural Networks for Sentiment Classification on Business
Reviews | Recently Convolutional Neural Networks (CNNs) models have proven remarkable
results for text classification and sentiment analysis. In this paper, we
present our approach on the task of classifying business reviews using word
embeddings on a large-scale dataset provided by Yelp: Yelp 2017 challenge
dataset. We compare word-based CNN using several pre-trained word embeddings
and end-to-end vector representations for text reviews classification. We
conduct several experiments to capture the semantic relationship between
business reviews and we use deep learning techniques that prove that the
obtained results are competitive with traditional methods.
| 2,017 | Computation and Language |
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in
Medical Abstracts | We present PubMed 200k RCT, a new dataset based on PubMed for sequential
sentence classification. The dataset consists of approximately 200,000
abstracts of randomized controlled trials, totaling 2.3 million sentences. Each
sentence of each abstract is labeled with their role in the abstract using one
of the following classes: background, objective, method, result, or conclusion.
The purpose of releasing this dataset is twofold. First, the majority of
datasets for sequential short-text classification (i.e., classification of
short texts that appear in sequences) are small: we hope that releasing a new
large dataset will help develop more accurate algorithms for this task. Second,
from an application perspective, researchers need better tools to efficiently
skim through the literature. Automatically classifying each sentence in an
abstract would help researchers read abstracts more efficiently, especially in
fields where abstracts may be long, such as the medical field.
| 2,017 | Computation and Language |
CASICT Tibetan Word Segmentation System for MLWS2017 | We participated in the MLWS 2017 on Tibetan word segmentation task, our
system is trained in a unrestricted way, by introducing a baseline system and
76w tibetan segmented sentences of ours. In the system character sequence is
processed by the baseline system into word sequence, then a subword unit (BPE
algorithm) split rare words into subwords with its corresponding features,
after that a neural network classifier is adopted to token each subword into
"B,M,E,S" label, in decoding step a simple rule is used to recover a final word
sequence. The candidate system for submition is selected by evaluating the
F-score in dev set pre-extracted from the 76w sentences. Experiment shows that
this method can fix segmentation errors of baseline system and result in a
significant performance gain.
| 2,017 | Computation and Language |
Paying Attention to Multi-Word Expressions in Neural Machine Translation | Processing of multi-word expressions (MWEs) is a known problem for any
natural language processing task. Even neural machine translation (NMT)
struggles to overcome it. This paper presents results of experiments on
investigating NMT attention allocation to the MWEs and improving automated
translation of sentences that contain MWEs in English->Latvian and
English->Czech NMT systems. Two improvement strategies were explored -(1)
bilingual pairs of automatically extracted MWE candidates were added to the
parallel corpus used to train the NMT system, and (2) full sentences containing
the automatically extracted MWE candidates were added to the parallel corpus.
Both approaches allowed to increase automated evaluation results. The best
result - 0.99 BLEU point increase - has been reached with the first approach,
while with the second approach minimal improvements achieved. We also provide
open-source software and tools used for MWE extraction and alignment
inspection.
| 2,017 | Computation and Language |
Specialising Word Vectors for Lexical Entailment | We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing
method that transforms any input word vector space to emphasise the asymmetric
relation of lexical entailment (LE), also known as the IS-A or
hyponymy-hypernymy relation. By injecting external linguistic constraints
(e.g., WordNet links) into the initial vector space, the LE specialisation
procedure brings true hyponymy-hypernymy pairs closer together in the
transformed Euclidean space. The proposed asymmetric distance measure adjusts
the norms of word vectors to reflect the actual WordNet-style hierarchy of
concepts. Simultaneously, a joint objective enforces semantic similarity using
the symmetric cosine distance, yielding a vector space specialised for both
lexical relations at once. LEAR specialisation achieves state-of-the-art
performance in the tasks of hypernymy directionality, hypernymy detection, and
graded lexical entailment, demonstrating the effectiveness and robustness of
the proposed asymmetric specialisation model.
| 2,018 | Computation and Language |
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and
CNN | This article presents classifiers based on SVM and Convolutional Neural
Networks (CNN) for the TASS 2017 challenge on tweets sentiment analysis. The
classifier with the best performance in general uses a combination of SVM and
CNN. The use of word embeddings was particularly useful for improving the
classifiers performance.
| 2,017 | Computation and Language |
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz
Interface for Collecting Human-Robot Dialogue | We describe the adaptation and refinement of a graphical user interface
designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot
dialogue data. The data collected will be used to develop a dialogue system for
robot navigation. Building on an interface previously used in the development
of dialogue systems for virtual agents and video playback, we add templates
with open parameters which allow the wizard to quickly produce a wide variety
of utterances. Our research demonstrates that this approach to data collection
is viable as an intermediate step in developing a dialogue system for physical
robots in remote locations from their users - a domain in which the human and
robot need to regularly verify and update a shared understanding of the
physical environment. We show that our WoZ interface and the fixed set of
utterances and templates therein provide for a natural pace of dialogue with
good coverage of the navigation domain.
| 2,017 | Computation and Language |
Constructing Datasets for Multi-hop Reading Comprehension Across
Documents | Most Reading Comprehension methods limit themselves to queries which can be
answered using a single sentence, paragraph, or document. Enabling models to
combine disjoint pieces of textual evidence would extend the scope of machine
comprehension methods, but currently there exist no resources to train and test
this capability. We propose a novel task to encourage the development of models
for text understanding across multiple documents and to investigate the limits
of existing methods. In our task, a model learns to seek and combine evidence -
effectively performing multi-hop (alias multi-step) inference. We devise a
methodology to produce datasets for this task, given a collection of
query-answer pairs and thematically linked documents. Two datasets from
different domains are induced, and we identify potential pitfalls and devise
circumvention strategies. We evaluate two previously proposed competitive
models and find that one can integrate information across documents. However,
both models struggle to select relevant information, as providing documents
guaranteed to be relevant greatly improves their performance. While the models
outperform several strong baselines, their best accuracy reaches 42.9% compared
to human performance at 74.0% - leaving ample room for improvement.
| 2,018 | Computation and Language |
Unsupervised Sentence Representations as Word Information Series:
Revisiting TF--IDF | Sentence representation at the semantic level is a challenging task for
Natural Language Processing and Artificial Intelligence. Despite the advances
in word embeddings (i.e. word vector representations), capturing sentence
meaning is an open question due to complexities of semantic interactions among
words. In this paper, we present an embedding method, which is aimed at
learning unsupervised sentence representations from unlabeled text. We propose
an unsupervised method that models a sentence as a weighted series of word
embeddings. The weights of the word embeddings are fitted by using Shannon's
word entropies provided by the Term Frequency--Inverse Document Frequency
(TF--IDF) transform. The hyperparameters of the model can be selected according
to the properties of data (e.g. sentence length and textual gender).
Hyperparameter selection involves word embedding methods and dimensionalities,
as well as weighting schemata. Our method offers advantages over existing
methods: identifiable modules, short-term training, online inference of
(unseen) sentence representations, as well as independence from domain,
external knowledge and language resources. Results showed that our model
outperformed the state of the art in well-known Semantic Textual Similarity
(STS) benchmarks. Moreover, our model reached state-of-the-art performance when
compared to supervised and knowledge-based STS systems.
| 2,017 | Computation and Language |
Basic tasks of sentiment analysis | Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about.
| 2,017 | Computation and Language |
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for
Keyword Spotting | We describe Honk, an open-source PyTorch reimplementation of convolutional
neural networks for keyword spotting that are included as examples in
TensorFlow. These models are useful for recognizing "command triggers" in
speech-based interfaces (e.g., "Hey Siri"), which serve as explicit cues for
audio recordings of utterances that are sent to the cloud for full speech
recognition. Evaluation on Google's recently released Speech Commands Dataset
shows that our reimplementation is comparable in accuracy and provides a
starting point for future work on the keyword spotting task.
| 2,017 | Computation and Language |
Towards a Seamless Integration of Word Senses into Downstream NLP
Applications | Lexical ambiguity can impede NLP systems from accurate understanding of
semantics. Despite its potential benefits, the integration of sense-level
information into NLP systems has remained understudied. By incorporating a
novel disambiguation algorithm into a state-of-the-art classification model, we
create a pipeline to integrate sense-level information into downstream NLP
applications. We show that a simple disambiguation of the input text can lead
to consistent performance improvement on multiple topic categorization and
polarity detection datasets, particularly when the fine granularity of the
underlying sense inventory is reduced and the document is sufficiently large.
Our results also point to the need for sense representation research to focus
more on in vivo evaluations which target the performance in downstream NLP
applications rather than artificial benchmarks.
| 2,017 | Computation and Language |
Build Fast and Accurate Lemmatization for Arabic | In this paper we describe the complexity of building a lemmatizer for Arabic
which has a rich and complex derivational morphology, and we discuss the need
for a fast and accurate lammatization to enhance Arabic Information Retrieval
(IR) results. We also introduce a new data set that can be used to test
lemmatization accuracy, and an efficient lemmatization algorithm that
outperforms state-of-the-art Arabic lemmatization in terms of accuracy and
speed. We share the data set and the code for public.
| 2,017 | Computation and Language |
Annotating High-Level Structures of Short Stories and Personal Anecdotes | Stories are a vital form of communication in human culture; they are employed
daily to persuade, to elicit sympathy, or to convey a message. Computational
understanding of human narratives, especially high-level narrative structures,
remain limited to date. Multiple literary theories for narrative structures
exist, but operationalization of the theories has remained a challenge. We
developed an annotation scheme by consolidating and extending existing
narratological theories, including Labov and Waletsky's (1967) functional
categorization scheme and Freytag's (1863) pyramid of dramatic tension, and
present 360 annotated short stories collected from online sources. In the
future, this research will support an approach that enables systems to
intelligently sustain complex communications with humans.
| 2,018 | Computation and Language |
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