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17,002 | Men Are from Mars, Women Are from Venus: Evaluation and Modelling of
Verbal Associations | cs.CL | We present a quantitative analysis of human word association pairs and study
the types of relations presented in the associations. We put our main focus on
the correlation between response types and respondent characteristics such as
occupation and gender by contrasting syntagmatic and paradigmatic associations.
Finall... | computer science |
17,003 | Enforcing Constraints on Outputs with Unconstrained Inference | cs.CL | Increasingly, practitioners apply neural networks to complex problems in
natural language processing (NLP), such as syntactic parsing, that have rich
output structures. Many such applications require deterministic constraints on
the output values; for example, requiring that the sequential outputs encode a
valid tree. ... | computer science |
17,004 | Temporal dynamics of semantic relations in word embeddings: an
application to predicting armed conflict participants | cs.CL | This paper deals with using word embedding models to trace the temporal
dynamics of semantic relations between pairs of words. The set-up is similar to
the well-known analogies task, but expanded with a time dimension. To this end,
we apply incremental updating of the models with new training texts, including
increment... | computer science |
17,005 | Determining Semantic Textual Similarity using Natural Deduction Proofs | cs.CL | Determining semantic textual similarity is a core research subject in natural
language processing. Since vector-based models for sentence representation
often use shallow information, capturing accurate semantics is difficult. By
contrast, logical semantic representations capture deeper levels of sentence
semantics, bu... | computer science |
17,006 | Detecting and Explaining Causes From Text For a Time Series Event | cs.CL | Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an ex... | computer science |
17,007 | Strawman: an Ensemble of Deep Bag-of-Ngrams for Sentiment Analysis | cs.CL | This paper describes a builder entry, named "strawman", to the sentence-level
sentiment analysis task of the "Build It, Break It" shared task of the First
Workshop on Building Linguistically Generalizable NLP Systems. The goal of a
builder is to provide an automated sentiment analyzer that would serve as a
target for b... | computer science |
17,008 | Effective Inference for Generative Neural Parsing | cs.CL | Generative neural models have recently achieved state-of-the-art results for
constituency parsing. However, without a feasible search procedure, their use
has so far been limited to reranking the output of external parsers in which
decoding is more tractable. We describe an alternative to the conventional
action-level ... | computer science |
17,009 | ASDA : Analyseur Syntaxique du Dialecte Alg{é}rien dans un but
d'analyse s{é}mantique | cs.CL | Opinion mining and sentiment analysis in social media is a research issue
having a great interest in the scientific community. However, before begin this
analysis, we are faced with a set of problems. In particular, the problem of
the richness of languages and dialects within these media. To address this
problem, we pr... | computer science |
17,010 | Adapting Sequence Models for Sentence Correction | cs.CL | In a controlled experiment of sequence-to-sequence approaches for the task of
sentence correction, we find that character-based models are generally more
effective than word-based models and models that encode subword information via
convolutions, and that modeling the output data as a series of diffs improves
effectiv... | computer science |
17,011 | Learning to Predict Charges for Criminal Cases with Legal Basis | cs.CL | The charge prediction task is to determine appropriate charges for a given
case, which is helpful for legal assistant systems where the user input is fact
description. We argue that relevant law articles play an important role in this
task, and therefore propose an attention-based neural network method to jointly
model... | computer science |
17,012 | Improving coreference resolution with automatically predicted prosodic
information | cs.CL | Adding manually annotated prosodic information, specifically pitch accents
and phrasing, to the typical text-based feature set for coreference resolution
has previously been shown to have a positive effect on German data. Practical
applications on spoken language, however, would rely on automatically predicted
prosodic... | computer science |
17,013 | Online Deception Detection Refueled by Real World Data Collection | cs.CL | The lack of large realistic datasets presents a bottleneck in online
deception detection studies. In this paper, we apply a data collection method
based on social network analysis to quickly identify high-quality deceptive and
truthful online reviews from Amazon. The dataset contains more than 10,000
deceptive reviews ... | computer science |
17,014 | A Weakly Supervised Approach to Train Temporal Relation Classifiers and
Acquire Regular Event Pairs Simultaneously | cs.CL | Capabilities of detecting temporal relations between two events can benefit
many applications. Most of existing temporal relation classifiers were trained
in a supervised manner. Instead, we explore the observation that regular event
pairs show a consistent temporal relation despite of their various contexts,
and these... | computer science |
17,015 | Bilingual Document Alignment with Latent Semantic Indexing | cs.CL | We apply cross-lingual Latent Semantic Indexing to the Bilingual Document
Alignment Task at WMT16. Reduced-rank singular value decomposition of a
bilingual term-document matrix derived from known English/French page pairs in
the training data allows us to map monolingual documents into a joint semantic
space. Two varia... | computer science |
17,016 | Sentiment Analysis on Financial News Headlines using Training Dataset
Augmentation | cs.CL | This paper discusses the approach taken by the UWaterloo team to arrive at a
solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of
SemEval 2017. The paper describes the document vectorization and sentiment
score prediction techniques used, as well as the design and implementation
decisions taken w... | computer science |
17,017 | Curriculum Learning and Minibatch Bucketing in Neural Machine
Translation | cs.CL | We examine the effects of particular orderings of sentence pairs on the
on-line training of neural machine translation (NMT). We focus on two types of
such orderings: (1) ensuring that each minibatch contains sentences similar in
some aspect and (2) gradual inclusion of some sentence types as the training
progresses (s... | computer science |
17,018 | Learning Language Representations for Typology Prediction | cs.CL | One central mystery of neural NLP is what neural models "know" about their
subject matter. When a neural machine translation system learns to translate
from one language to another, does it learn the syntax or semantics of the
languages? Can this knowledge be extracted from the system to fill holes in
human scientific ... | computer science |
17,019 | Joint Named Entity Recognition and Stance Detection in Tweets | cs.CL | Named entity recognition (NER) is a well-established task of information
extraction which has been studied for decades. More recently, studies reporting
NER experiments on social media texts have emerged. On the other hand, stance
detection is a considerably new research topic usually considered within the
scope of sen... | computer science |
17,020 | Skill2vec: Machine Learning Approaches for Determining the Relevant
Skill from Job Description | cs.CL | Un-supervise learned word embeddings have seen tremendous success in numerous
Natural Language Processing (NLP) tasks in recent years. The main contribution
of this paper is to develop a technique called Skill2vec, which applies machine
learning techniques in recruitment to enhance the search strategy to find the
candi... | computer science |
17,021 | Low-Resource Neural Headline Generation | cs.CL | Recent neural headline generation models have shown great results, but are
generally trained on very large datasets. We focus our efforts on improving
headline quality on smaller datasets by the means of pretraining. We propose
new methods that enable pre-training all the parameters of the model and
utilize all availab... | computer science |
17,022 | Combining Thesaurus Knowledge and Probabilistic Topic Models | cs.CL | In this paper we present the approach of introducing thesaurus knowledge into
probabilistic topic models. The main idea of the approach is based on the
assumption that the frequencies of semantically related words and phrases,
which are met in the same texts, should be enhanced: this action leads to their
larger contri... | computer science |
17,023 | Linguistically Motivated Vocabulary Reduction for Neural Machine
Translation from Turkish to English | cs.CL | The necessity of using a fixed-size word vocabulary in order to control the
model complexity in state-of-the-art neural machine translation (NMT) systems
is an important bottleneck on performance, especially for morphologically rich
languages. Conventional methods that aim to overcome this problem by using
sub-word or ... | computer science |
17,024 | Regularization techniques for fine-tuning in neural machine translation | cs.CL | We investigate techniques for supervised domain adaptation for neural machine
translation where an existing model trained on a large out-of-domain dataset is
adapted to a small in-domain dataset. In this scenario, overfitting is a major
challenge. We investigate a number of techniques to reduce overfitting and
improve ... | computer science |
17,025 | SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and
Cross-lingual Focused Evaluation | cs.CL | Semantic Textual Similarity (STS) measures the meaning similarity of
sentences. Applications include machine translation (MT), summarization,
generation, question answering (QA), short answer grading, semantic search,
dialog and conversational systems. The STS shared task is a venue for assessing
the current state-of-t... | computer science |
17,026 | The Code2Text Challenge: Text Generation in Source Code Libraries | cs.CL | We propose a new shared task for tactical data-to-text generation in the
domain of source code libraries. Specifically, we focus on text generation of
function descriptions from example software projects. Data is drawn from
existing resources used for studying the related problem of semantic parser
induction (Richardso... | computer science |
17,027 | Enhancing the Input Representation: From Complexity to Simplicity | cs.CL | We introduce an efficient algorithm for mining informative combinations of
attribute-values for a given task. We use informative attribute-values to
enhance the input representation of data. We apply our approach to coreference
resolution using a simple set of attributes like syntactic roles and string
match. With the ... | computer science |
17,028 | An Investigation into the Pedagogical Features of Documents | cs.CL | Characterizing the content of a technical document in terms of its learning
utility can be useful for applications related to education, such as generating
reading lists from large collections of documents. We refer to this learning
utility as the "pedagogical value" of the document to the learner. While
pedagogical va... | computer science |
17,029 | Improving Part-of-Speech Tagging for NLP Pipelines | cs.CL | This paper outlines the results of sentence level linguistics based rules for
improving part-of-speech tagging. It is well known that the performance of
complex NLP systems is negatively affected if one of the preliminary stages is
less than perfect. Errors in the initial stages in the pipeline have a
snowballing effec... | computer science |
17,030 | A Continuously Growing Dataset of Sentential Paraphrases | cs.CL | A major challenge in paraphrase research is the lack of parallel corpora. In
this paper, we present a new method to collect large-scale sentential
paraphrases from Twitter by linking tweets through shared URLs. The main
advantage of our method is its simplicity, as it gets rid of the classifier or
human in the loop nee... | computer science |
17,031 | A Generative Parser with a Discriminative Recognition Algorithm | cs.CL | Generative models defining joint distributions over parse trees and sentences
are useful for parsing and language modeling, but impose restrictions on the
scope of features and are often outperformed by discriminative models. We
propose a framework for parsing and language modeling which marries a
generative model with... | computer science |
17,032 | Deriving Verb Predicates By Clustering Verbs with Arguments | cs.CL | Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora... | computer science |
17,033 | Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling | cs.CL | In textual information extraction and other sequence labeling tasks it is now
common to use recurrent neural networks (such as LSTM) to form rich embedded
representations of long-term input co-occurrence patterns. Representation of
output co-occurrence patterns is typically limited to a hand-designed graphical
model, s... | computer science |
17,034 | Analyzing Neural MT Search and Model Performance | cs.CL | In this paper, we offer an in-depth analysis about the modeling and search
performance. We address the question if a more complex search algorithm is
necessary. Furthermore, we investigate the question if more complex models
which might only be applicable during rescoring are promising.
By separating the search space... | computer science |
17,035 | Dynamic Data Selection for Neural Machine Translation | cs.CL | Intelligent selection of training data has proven a successful technique to
simultaneously increase training efficiency and translation performance for
phrase-based machine translation (PBMT). With the recent increase in popularity
of neural machine translation (NMT), we explore in this paper to what extent
and how NMT... | computer science |
17,036 | The University of Edinburgh's Neural MT Systems for WMT17 | cs.CL | This paper describes the University of Edinburgh's submissions to the WMT17
shared news translation and biomedical translation tasks. We participated in 12
translation directions for news, translating between English and Czech, German,
Latvian, Russian, Turkish and Chinese. For the biomedical task we submitted
systems ... | computer science |
17,037 | Combining Generative and Discriminative Approaches to Unsupervised
Dependency Parsing via Dual Decomposition | cs.CL | Unsupervised dependency parsing aims to learn a dependency parser from
unannotated sentences. Existing work focuses on either learning generative
models using the expectation-maximization algorithm and its variants, or
learning discriminative models using the discriminative clustering algorithm.
In this paper, we propo... | computer science |
17,038 | Dependency Grammar Induction with Neural Lexicalization and Big Training
Data | cs.CL | We study the impact of big models (in terms of the degree of lexicalization)
and big data (in terms of the training corpus size) on dependency grammar
induction. We experimented with L-DMV, a lexicalized version of Dependency
Model with Valence and L-NDMV, our lexicalized extension of the Neural
Dependency Model with V... | computer science |
17,039 | Enterprise to Computer: Star Trek chatbot | cs.CL | Human interactions and human-computer interactions are strongly influenced by
style as well as content. Adding a persona to a chatbot makes it more
human-like and contributes to a better and more engaging user experience. In
this work, we propose a design for a chatbot that captures the "style" of Star
Trek by incorpor... | computer science |
17,040 | Towards Semantic Modeling of Contradictions and Disagreements: A Case
Study of Medical Guidelines | cs.CL | We introduce a formal distinction between contradictions and disagreements in
natural language texts, motivated by the need to formally reason about
contradictory medical guidelines. This is a novel and potentially very useful
distinction, and has not been discussed so far in NLP and logic. We also
describe a NLP syste... | computer science |
17,041 | Domain Aware Neural Dialog System | cs.CL | We investigate the task of building a domain aware chat system which
generates intelligent responses in a conversation comprising of different
domains. The domain, in this case, is the topic or theme of the conversation.
To achieve this, we present DOM-Seq2Seq, a domain aware neural network model
based on the novel tec... | computer science |
17,042 | Exploiting Linguistic Resources for Neural Machine Translation Using
Multi-task Learning | cs.CL | Linguistic resources such as part-of-speech (POS) tags have been extensively
used in statistical machine translation (SMT) frameworks and have yielded
better performances. However, usage of such linguistic annotations in neural
machine translation (NMT) systems has been left under-explored.
In this work, we show that... | computer science |
17,043 | CRF Autoencoder for Unsupervised Dependency Parsing | cs.CL | Unsupervised dependency parsing, which tries to discover linguistic
dependency structures from unannotated data, is a very challenging task. Almost
all previous work on this task focuses on learning generative models. In this
paper, we develop an unsupervised dependency parsing model based on the CRF
autoencoder. The e... | computer science |
17,044 | Recurrent Neural Network-Based Sentence Encoder with Gated Attention for
Natural Language Inference | cs.CL | The RepEval 2017 Shared Task aims to evaluate natural language understanding
models for sentence representation, in which a sentence is represented as a
fixed-length vector with neural networks and the quality of the representation
is tested with a natural language inference task. This paper describes our
system (alpha... | computer science |
17,045 | Massively Multilingual Neural Grapheme-to-Phoneme Conversion | cs.CL | Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and
automatic speech recognition systems. Most g2p systems are monolingual: they
require language-specific data or handcrafting of rules. Such systems are
difficult to extend to low resource languages, for which data and handcrafted
rules are not avai... | computer science |
17,046 | Predicting the Law Area and Decisions of French Supreme Court Cases | cs.CL | In this paper, we investigate the application of text classification methods
to predict the law area and the decision of cases judged by the French Supreme
Court. We also investigate the influence of the time period in which a ruling
was made over the textual form of the case description and the extent to which
it is n... | computer science |
17,047 | Automatic Question-Answering Using A Deep Similarity Neural Network | cs.CL | Automatic question-answering is a classical problem in natural language
processing, which aims at designing systems that can automatically answer a
question, in the same way as human does. In this work, we propose a deep
learning based model for automatic question-answering. First the questions and
answers are embedded... | computer science |
17,048 | Referenceless Quality Estimation for Natural Language Generation | cs.CL | Traditional automatic evaluation measures for natural language generation
(NLG) use costly human-authored references to estimate the quality of a system
output. In this paper, we propose a referenceless quality estimation (QE)
approach based on recurrent neural networks, which predicts a quality score for
a NLG system ... | computer science |
17,049 | A Syllable-based Technique for Word Embeddings of Korean Words | cs.CL | Word embedding has become a fundamental component to many NLP tasks such as
named entity recognition and machine translation. However, popular models that
learn such embeddings are unaware of the morphology of words, so it is not
directly applicable to highly agglutinative languages such as Korean. We
propose a syllabl... | computer science |
17,050 | Extractive Multi Document Summarization using Dynamical Measurements of
Complex Networks | cs.CL | Due to the large amount of textual information available on Internet, it is
of paramount relevance to use techniques that find relevant and concise
content. A typical task devoted to the identification of informative sentences
in documents is the so called extractive document summarization task. In this
paper, we use c... | computer science |
17,051 | Neural Machine Translation with Word Predictions | cs.CL | In the encoder-decoder architecture for neural machine translation (NMT), the
hidden states of the recurrent structures in the encoder and decoder carry the
crucial information about the sentence.These vectors are generated by
parameters which are updated by back-propagation of translation errors through
time. We argue... | computer science |
17,052 | A Comparison of Neural Models for Word Ordering | cs.CL | We compare several language models for the word-ordering task and propose a
new bag-to-sequence neural model based on attention-based sequence-to-sequence
models. We evaluate the model on a large German WMT data set where it
significantly outperforms existing models. We also describe a novel search
strategy for LM-base... | computer science |
17,053 | Translating Phrases in Neural Machine Translation | cs.CL | Phrases play an important role in natural language understanding and machine
translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is
difficult to integrate them into current neural machine translation (NMT) which
reads and generates sentences word by word. In this work, we propose a method
to transla... | computer science |
17,054 | Memory-augmented Neural Machine Translation | cs.CL | Neural machine translation (NMT) has achieved notable success in recent
times, however it is also widely recognized that this approach has limitations
with handling infrequent words and word pairs. This paper presents a novel
memory-augmented NMT (M-NMT) architecture, which stores knowledge about how
words (usually inf... | computer science |
17,055 | ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in
Question Answering | cs.CL | Transferring knowledge from a source domain to another domain is useful,
especially when gathering new data is very expensive and time-consuming. Deep
networks have been well-studied for question answering tasks in recent years;
however, no prominent research for transfer learning through deep neural
networks exists in... | computer science |
17,056 | Corpus-level Fine-grained Entity Typing | cs.CL | This paper addresses the problem of corpus-level entity typing, i.e.,
inferring from a large corpus that an entity is a member of a class such as
"food" or "artist". The application of entity typing we are interested in is
knowledge base completion, specifically, to learn which classes an entity is a
member of. We prop... | computer science |
17,057 | Mining fine-grained opinions on closed captions of YouTube videos with
an attention-RNN | cs.CL | Video reviews are the natural evolution of written product reviews. In this
paper we target this phenomenon and introduce the first dataset created from
closed captions of YouTube product review videos as well as a new attention-RNN
model for aspect extraction and joint aspect extraction and sentiment
classification. O... | computer science |
17,058 | Neural-based Context Representation Learning for Dialog Act
Classification | cs.CL | We explore context representation learning methods in neural-based models for
dialog act classification. We propose and compare extensively different methods
which combine recurrent neural network architectures and attention mechanisms
(AMs) at different context levels. Our experimental results on two benchmark
dataset... | computer science |
17,059 | Recent Trends in Deep Learning Based Natural Language Processing | cs.CL | Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many
domains. Recently, a variety of model designs and methods have blossomed in the
context of natural language processing (NLP). In this paper, we review
significant dee... | computer science |
17,060 | Identifying Reference Spans: Topic Modeling and Word Embeddings help IR | cs.CL | The CL-SciSumm 2016 shared task introduced an interesting problem: given a
document D and a piece of text that cites D, how do we identify the text spans
of D being referenced by the piece of text? The shared task provided the first
annotated dataset for studying this problem. We present an analysis of our
continued wo... | computer science |
17,061 | Location Name Extraction from Targeted Text Streams using
Gazetteer-based Statistical Language Models | cs.CL | Extracting location names from informal and unstructured texts requires the
identification of referent boundaries and partitioning of compound names in the
presence of variation in location referents. Instead of analyzing semantic,
syntactic, and/or orthographic features, our Location Name Extraction tool
(LNEx) exploi... | computer science |
17,062 | Towards Neural Speaker Modeling in Multi-Party Conversation: The Task,
Dataset, and Models | cs.CL | Neural network-based dialog systems are attracting increasing attention in
both academia and industry. Recently, researchers have begun to realize the
importance of speaker modeling in neural dialog systems, but there lacks
established tasks and datasets. In this paper, we propose speaker
classification as a surrogate ... | computer science |
17,063 | Neural and Statistical Methods for Leveraging Meta-information in
Machine Translation | cs.CL | In this paper, we discuss different methods which use meta information and
richer context that may accompany source language input to improve machine
translation quality. We focus on category information of input text as meta
information, but the proposed methods can be extended to all textual and
non-textual meta info... | computer science |
17,064 | Neural Machine Translation Leveraging Phrase-based Models in a Hybrid
Search | cs.CL | In this paper, we introduce a hybrid search for attention-based neural
machine translation (NMT). A target phrase learned with statistical MT models
extends a hypothesis in the NMT beam search when the attention of the NMT model
focuses on the source words translated by this phrase. Phrases added in this
way are scored... | computer science |
17,065 | Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of
Chinese and Japanese | cs.CL | The character vocabulary can be very large in non-alphabetic languages such
as Chinese and Japanese, which makes neural network models huge to process such
languages. We explored a model for sentiment classification that takes the
embeddings of the radicals of the Chinese characters, i.e, hanzi of Chinese and
kanji of ... | computer science |
17,066 | Making Sense of Word Embeddings | cs.CL | We present a simple yet effective approach for learning word sense
embeddings. In contrast to existing techniques, which either directly learn
sense representations from corpora or rely on sense inventories from lexical
resources, our approach can induce a sense inventory from existing word
embeddings via clustering of... | computer science |
17,067 | N-gram and Neural Language Models for Discriminating Similar Languages | cs.CL | This paper describes our submission (named clac) to the 2016 Discriminating
Similar Languages (DSL) shared task. We participated in the closed Sub-task 1
(Set A) with two separate machine learning techniques. The first approach is a
character based Convolution Neural Network with a bidirectional long short term
memory ... | computer science |
17,068 | Argument Labeling of Explicit Discourse Relations using LSTM Neural
Networks | cs.CL | Argument labeling of explicit discourse relations is a challenging task. The
state of the art systems achieve slightly above 55% F-measure but require
hand-crafted features. In this paper, we propose a Long Short Term Memory
(LSTM) based model for argument labeling. We experimented with multiple
configurations of our m... | computer science |
17,069 | What matters in a transferable neural network model for relation
classification in the biomedical domain? | cs.CL | Lack of sufficient labeled data often limits the applicability of advanced
machine learning algorithms to real life problems. However efficient use of
Transfer Learning (TL) has been shown to be very useful across domains. TL
utilizes valuable knowledge learned in one task (source task), where sufficient
data is availa... | computer science |
17,070 | Unified Neural Architecture for Drug, Disease and Clinical Entity
Recognition | cs.CL | Most existing methods for biomedical entity recognition task rely on explicit
feature engineering where many features either are specific to a particular
task or depends on output of other existing NLP tools. Neural architectures
have been shown across various domains that efforts for explicit feature design
can be red... | computer science |
17,071 | Break it Down for Me: A Study in Automated Lyric Annotation | cs.CL | Comprehending lyrics, as found in songs and poems, can pose a challenge to
human and machine readers alike. This motivates the need for systems that can
understand the ambiguity and jargon found in such creative texts, and provide
commentary to aid readers in reaching the correct interpretation. We introduce
the task o... | computer science |
17,072 | Automatic Identification of AltLexes using Monolingual Parallel Corpora | cs.CL | The automatic identification of discourse relations is still a challenging
task in natural language processing. Discourse connectives, such as "since" or
"but", are the most informative cues to identify explicit relations; however
discourse parsers typically use a closed inventory of such connectives. As a
result, disc... | computer science |
17,073 | Simple and Effective Dimensionality Reduction for Word Embeddings | cs.CL | Word embeddings have become the basic building blocks for several natural
language processing and information retrieval tasks. Pre-trained word
embeddings are used in several downstream applications as well as for
constructing representations for sentences, paragraphs and documents. Recently,
there has been an emphasis... | computer science |
17,074 | Emotion Intensities in Tweets | cs.CL | This paper examines the task of detecting intensity of emotion from text. We
create the first datasets of tweets annotated for anger, fear, joy, and sadness
intensities. We use a technique called best--worst scaling (BWS) that improves
annotation consistency and obtains reliable fine-grained scores. We show that
emotio... | computer science |
17,075 | Improved Abusive Comment Moderation with User Embeddings | cs.CL | Experimenting with a dataset of approximately 1.6M user comments from a Greek
news sports portal, we explore how a state of the art RNN-based moderation
method can be improved by adding user embeddings, user type embeddings, user
biases, or user type biases. We observe improvements in all cases, with user
embeddings le... | computer science |
17,076 | WASSA-2017 Shared Task on Emotion Intensity | cs.CL | We present the first shared task on detecting the intensity of emotion felt
by the speaker of a tweet. We create the first datasets of tweets annotated for
anger, fear, joy, and sadness intensities using a technique called best--worst
scaling (BWS). We show that the annotations lead to reliable fine-grained
intensity s... | computer science |
17,077 | Cross-Sentence N-ary Relation Extraction with Graph LSTMs | cs.CL | Past work in relation extraction has focused on binary relations in single
sentences. Recent NLP inroads in high-value domains have sparked interest in
the more general setting of extracting n-ary relations that span multiple
sentences. In this paper, we explore a general relation extraction framework
based on graph lo... | computer science |
17,078 | Towards Speech Emotion Recognition "in the wild" using Aggregated
Corpora and Deep Multi-Task Learning | cs.CL | One of the challenges in Speech Emotion Recognition (SER) "in the wild" is
the large mismatch between training and test data (e.g. speakers and tasks). In
order to improve the generalisation capabilities of the emotion models, we
propose to use Multi-Task Learning (MTL) and use gender and naturalness as
auxiliary tasks... | computer science |
17,079 | Emotion Detection on TV Show Transcripts with Sequence-based
Convolutional Neural Networks | cs.CL | While there have been significant advances in detecting emotions from speech
and image recognition, emotion detection on text is still under-explored and
remained as an active research field. This paper introduces a corpus for
text-based emotion detection on multiparty dialogue as well as deep neural
models that outper... | computer science |
17,080 | Fluency-Guided Cross-Lingual Image Captioning | cs.CL | Image captioning has so far been explored mostly in English, as most
available datasets are in this language. However, the application of image
captioning should not be restricted by language. Only few studies have been
conducted for image captioning in a cross-lingual setting. Different from these
works that manually ... | computer science |
17,081 | Comparison of Decoding Strategies for CTC Acoustic Models | cs.CL | Connectionist Temporal Classification has recently attracted a lot of
interest as it offers an elegant approach to building acoustic models (AMs) for
speech recognition. The CTC loss function maps an input sequence of observable
feature vectors to an output sequence of symbols. Output symbols are
conditionally independ... | computer science |
17,082 | Statistical Vs Rule Based Machine Translation; A Case Study on Indian
Language Perspective | cs.CL | In this paper we present our work on a case study between Statistical Machien
Transaltion (SMT) and Rule-Based Machine Translation (RBMT) systems on
English-Indian langugae and Indian to Indian langugae perspective. Main
objective of our study is to make a five way performance compariosn; such as,
a) SMT and RBMT b) SM... | computer science |
17,083 | Evaluating Word Embeddings for Sentence Boundary Detection in Speech
Transcripts | cs.CL | This paper is motivated by the automation of neuropsychological tests
involving discourse analysis in the retellings of narratives by patients with
potential cognitive impairment. In this scenario the task of sentence boundary
detection in speech transcripts is important as discourse analysis involves the
application o... | computer science |
17,084 | Learning Chinese Word Representations From Glyphs Of Characters | cs.CL | In this paper, we propose new methods to learn Chinese word representations.
Chinese characters are composed of graphical components, which carry rich
semantics. It is common for a Chinese learner to comprehend the meaning of a
word from these graphical components. As a result, we propose models that
enhance word repre... | computer science |
17,085 | Dialogue Act Segmentation for Vietnamese Human-Human Conversational
Texts | cs.CL | Dialog act identification plays an important role in understanding
conversations. It has been widely applied in many fields such as dialogue
systems, automatic machine translation, automatic speech recognition, and
especially useful in systems with human-computer natural language dialogue
interfaces such as virtual ass... | computer science |
17,086 | Natural Language Processing: State of The Art, Current Trends and
Challenges | cs.CL | Natural language processing (NLP) has recently gained much attention for
representing and analysing human language computationally. It has spread its
applications in various fields such as machine translation, email spam
detection, information extraction, summarization, medical, and question
answering etc. The paper di... | computer science |
17,087 | Towards Syntactic Iberian Polarity Classification | cs.CL | Lexicon-based methods using syntactic rules for polarity classification rely
on parsers that are dependent on the language and on treebank guidelines. Thus,
rules are also dependent and require adaptation, especially in multilingual
scenarios. We tackle this challenge in the context of the Iberian Peninsula,
releasing ... | computer science |
17,088 | Simple Open Stance Classification for Rumour Analysis | cs.CL | Stance classification determines the attitude, or stance, in a (typically
short) text. The task has powerful applications, such as the detection of fake
news or the automatic extraction of attitudes toward entities or events in the
media. This paper describes a surprisingly simple and efficient classification
approach ... | computer science |
17,089 | An Annotated Corpus of Relational Strategies in Customer Service | cs.CL | We create and release the first publicly available commercial customer
service corpus with annotated relational segments. Human-computer data from
three live customer service Intelligent Virtual Agents (IVAs) in the domains of
travel and telecommunications were collected, and reviewers marked all text
that was deemed u... | computer science |
17,090 | Large-Scale Domain Adaptation via Teacher-Student Learning | cs.CL | High accuracy speech recognition requires a large amount of transcribed data
for supervised training. In the absence of such data, domain adaptation of a
well-trained acoustic model can be performed, but even here, high accuracy
usually requires significant labeled data from the target domain. In this work,
we propose ... | computer science |
17,091 | A Question Answering Approach to Emotion Cause Extraction | cs.CL | Emotion cause extraction aims to identify the reasons behind a certain
emotion expressed in text. It is a much more difficult task compared to emotion
classification. Inspired by recent advances in using deep memory networks for
question answering (QA), we propose a new approach which considers emotion
cause identifica... | computer science |
17,092 | Syllable-level Neural Language Model for Agglutinative Language | cs.CL | Language models for agglutinative languages have always been hindered in past
due to myriad of agglutinations possible to any given word through various
affixes. We propose a method to diminish the problem of out-of-vocabulary words
by introducing an embedding derived from syllables and morphemes which
leverages the ag... | computer science |
17,093 | EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion
Intensity | cs.CL | In this paper we describe a deep learning system that has been designed and
built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a
representation learning approach based on inner attention on top of an RNN.
Results show that our model offers good capabilities and is able to
successfully identify emotion... | computer science |
17,094 | Assessing the Stylistic Properties of Neurally Generated Text in
Authorship Attribution | cs.CL | Recent applications of neural language models have led to an increased
interest in the automatic generation of natural language. However impressive,
the evaluation of neurally generated text has so far remained rather informal
and anecdotal. Here, we present an attempt at the systematic assessment of one
aspect of the ... | computer science |
17,095 | Agree to Disagree: Improving Disagreement Detection with Dual GRUs | cs.CL | This paper presents models for detecting agreement/disagreement in online
discussions. In this work we show that by using a Siamese inspired architecture
to encode the discussions, we no longer need to rely on hand-crafted features
to exploit the meta thread structure. We evaluate our model on existing online
discussio... | computer science |
17,096 | Future Word Contexts in Neural Network Language Models | cs.CL | Recently, bidirectional recurrent network language models (bi-RNNLMs) have
been shown to outperform standard, unidirectional, recurrent neural network
language models (uni-RNNLMs) on a range of speech recognition tasks. This
indicates that future word context information beyond the word history can be
useful. However, ... | computer science |
17,097 | Cross-Lingual Dependency Parsing for Closely Related Languages -
Helsinki's Submission to VarDial 2017 | cs.CL | This paper describes the submission from the University of Helsinki to the
shared task on cross-lingual dependency parsing at VarDial 2017. We present
work on annotation projection and treebank translation that gave good results
for all three target languages in the test set. In particular, Slovak seems to
work well wi... | computer science |
17,098 | Neural machine translation for low-resource languages | cs.CL | Neural machine translation (NMT) approaches have improved the state of the
art in many machine translation settings over the last couple of years, but
they require large amounts of training data to produce sensible output. We
demonstrate that NMT can be used for low-resource languages as well, by
introducing more local... | computer science |
17,099 | The Natural Stories Corpus | cs.CL | It is now a common practice to compare models of human language processing by
predicting participant reactions (such as reading times) to corpora consisting
of rich naturalistic linguistic materials. However, many of the corpora used in
these studies are based on naturalistic text and thus do not contain many of
the lo... | computer science |
17,100 | CLaC @ QATS: Quality Assessment for Text Simplification | cs.CL | This paper describes our approach to the 2016 QATS quality assessment shared
task. We trained three independent Random Forest classifiers in order to assess
the quality of the simplified texts in terms of grammaticality, meaning
preservation and simplicity. We used the language model of Google-Ngram as
feature to predi... | computer science |
17,101 | The CLaC Discourse Parser at CoNLL-2016 | cs.CL | This paper describes our submission "CLaC" to the CoNLL-2016 shared task on
shallow discourse parsing. We used two complementary approaches for the task. A
standard machine learning approach for the parsing of explicit relations, and a
deep learning approach for non-explicit relations. Overall, our parser achieves
an F... | computer science |
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