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17,702 | DP-GAN: Diversity-Promoting Generative Adversarial Network for
Generating Informative and Diversified Text | cs.CL | Existing text generation methods tend to produce repeated and "boring"
expressions. To tackle this problem, we propose a new text generation model,
called Diversity-Promoting Generative Adversarial Network (DP-GAN). The
proposed model assigns low reward for repeated text and high reward for "novel"
text, encouraging th... | computer science |
17,703 | Diverse Beam Search for Increased Novelty in Abstractive Summarization | cs.CL | Text summarization condenses a text to a shorter version while retaining the
important informations. Abstractive summarization is a recent development that
generates new phrases, rather than simply copying or rephrasing sentences
within the original text. Recently neural sequence-to-sequence models have
achieved good r... | computer science |
17,704 | Question-Answer Selection in User to User Marketplace Conversations | cs.CL | Sellers in user to user marketplaces can be inundated with questions from
potential buyers. Answers are often already available in the product
description. We collected a dataset of around 590K such questions and answers
from conversations in an online marketplace. We propose a question answering
system that selects a ... | computer science |
17,705 | Byte-Level Recursive Convolutional Auto-Encoder for Text | cs.CL | This article proposes to auto-encode text at byte-level using convolutional
networks with a recursive architecture. The motivation is to explore whether it
is possible to have scalable and homogeneous text generation at byte-level in a
non-sequential fashion through the simple task of auto-encoding. We show that
non-se... | computer science |
17,706 | Investigations on Knowledge Base Embedding for Relation Prediction and
Extraction | cs.CL | We report an evaluation of the effectiveness of the existing knowledge base
embedding models for relation prediction and for relation extraction on a wide
range of benchmarks. We also describe a new benchmark, which is much larger and
complex than previous ones, which we introduce to help validate the
effectiveness of ... | computer science |
17,707 | Non-Projective Dependency Parsing via Latent Heads Representation (LHR) | cs.CL | In this paper, we introduce a novel approach based on a bidirectional
recurrent autoencoder to perform globally optimized non-projective dependency
parsing via semi-supervised learning. The syntactic analysis is completed at
the end of the neural process that generates a Latent Heads Representation
(LHR), without any a... | computer science |
17,708 | An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using
MIMIC-III Clinical Notes | cs.CL | Code assignment is important on many levels in the modern hospital, from
ensuring accurate billing process to creating a valid record of patient care
history. However, the coding process is tedious, subjective, and requires
medical coders with extensive training. The objective of this study is to
evaluate the performan... | computer science |
17,709 | Unsupervised word sense disambiguation in dynamic semantic spaces | cs.CL | In this paper, we are mainly concerned with the ability to quickly and
automatically distinguish word senses in dynamic semantic spaces in which new
terms and new senses appear frequently. Such spaces are built '"on the fly"
from constantly evolving data sets such as Wikipedia, repositories of patent
grants and applica... | computer science |
17,710 | Enhance word representation for out-of-vocabulary on Ubuntu dialogue
corpus | cs.CL | Ubuntu dialogue corpus is the largest public available dialogue corpus to
make it feasible to build end-to-end deep neural network models directly from
the conversation data. One challenge of Ubuntu dialogue corpus is the large
number of out-of-vocabulary words. In this paper we proposed a method which
combines the gen... | computer science |
17,711 | Biomedical term normalization of EHRs with UMLS | cs.CL | This paper presents a novel prototype for biomedical term normalization of
electronic health record excerpts with the Unified Medical Language System
(UMLS) Metathesaurus. Despite being multilingual and cross-lingual by design,
we first focus on processing clinical text in Spanish because there is no
existing tool for ... | computer science |
17,712 | DisMo: A Morphosyntactic, Disfluency and Multi-Word Unit Annotator. An
Evaluation on a Corpus of French Spontaneous and Read Speech | cs.CL | We present DisMo, a multi-level annotator for spoken language corpora that
integrates part-of-speech tagging with basic disfluency detection and
annotation, and multi-word unit recognition. DisMo is a hybrid system that uses
a combination of lexical resources, rules, and statistical models based on
Conditional Random F... | computer science |
17,713 | Zero-Resource Neural Machine Translation with Multi-Agent Communication
Game | cs.CL | While end-to-end neural machine translation (NMT) has achieved notable
success in the past years in translating a handful of resource-rich language
pairs, it still suffers from the data scarcity problem for low-resource
language pairs and domains. To tackle this problem, we propose an interactive
multimodal framework f... | computer science |
17,714 | Augmenting Librispeech with French Translations: A Multimodal Corpus for
Direct Speech Translation Evaluation | cs.CL | Recent works in spoken language translation (SLT) have attempted to build
end-to-end speech-to-text translation without using source language
transcription during learning or decoding. However, while large quantities of
parallel texts (such as Europarl, OpenSubtitles) are available for training
machine translation syst... | computer science |
17,715 | Natural Language Inference over Interaction Space: ICLR 2018
Reproducibility Report | cs.CL | We have tried to reproduce the results of the paper "Natural Language
Inference over Interaction Space" submitted to ICLR 2018 conference as part of
the ICLR 2018 Reproducibility Challenge. Initially, we were not aware that the
code was available, so we started to implement the network from scratch. We
have evaluated o... | computer science |
17,716 | Recurrent Neural Network-Based Semantic Variational Autoencoder for
Sequence-to-Sequence Learning | cs.CL | Sequence-to-sequence (Seq2seq) models have played an import role in the
recent success of various natural language processing methods, such as machine
translation, text summarization, and speech recognition. However, current
Seq2seq models have trouble preserving global latent information from a long
sequence of words.... | computer science |
17,717 | Online Learning for Effort Reduction in Interactive Neural Machine
Translation | cs.CL | Neural machine translation systems require large amounts of training data and
resources. Even with this, the quality of the translations may be insufficient
for some users or domains. In such cases, the output of the system must be
revised by a human agent. This can be done in a post-editing stage or following
an inter... | computer science |
17,718 | TextZoo, a New Benchmark for Reconsidering Text Classification | cs.CL | Text representation is a fundamental concern in Natural Language Processing,
especially in text classification. Recently, many neural network approaches
with delicate representation model (e.g. FASTTEXT, CNN, RNN and many hybrid
models with attention mechanisms) claimed that they achieved state-of-art in
specific text ... | computer science |
17,719 | Syntax and Semantics of Italian Poetry in the First Half of the 20th
Century | cs.CL | In this paper we study, analyse and comment rhetorical figures present in
some of most interesting poetry of the first half of the twentieth century.
These figures are at first traced back to some famous poet of the past and then
compared to classical Latin prose. Linguistic theory is then called in to show
how they ca... | computer science |
17,720 | Understanding Recurrent Neural State Using Memory Signatures | cs.CL | We demonstrate a network visualization technique to analyze the recurrent
state inside the LSTMs/GRUs used commonly in language and acoustic models.
Interpreting intermediate state and network activations inside end-to-end
models remains an open challenge. Our method allows users to understand exactly
how much and what... | computer science |
17,721 | Automatic Generation of Language-Independent Features for Cross-Lingual
Classification | cs.CL | Many applications require categorization of text documents using predefined
categories. The main approach to performing text categorization is learning
from labeled examples. For many tasks, it may be difficult to find examples in
one language but easy in others. The problem of learning from examples in one
or more lan... | computer science |
17,722 | Making "fetch" happen: The influence of social and linguistic context on
nonstandard word growth and decline | cs.CL | In an online community, new words come and go: today's "haha" may be replaced
by tomorrow's "lol." Changes in online writing are usually studied as a social
process, with innovations diffusing through a network of individuals in a
speech community. But unlike other types of innovation, language change is
shaped and con... | computer science |
17,723 | End-to-End Automatic Speech Translation of Audiobooks | cs.CL | We investigate end-to-end speech-to-text translation on a corpus of
audiobooks specifically augmented for this task. Previous works investigated
the extreme case where source language transcription is not available during
learning nor decoding, but we also study a midway case where source language
transcription is avai... | computer science |
17,724 | A Unified Implicit Dialog Framework for Conversational Search | cs.CL | We propose a unified Implicit Dialog framework for goal-oriented, information
seeking tasks of Conversational Search applications. It aims to enable dialog
interactions with domain data without replying on explicitly encoded the rules
but utilizing the underlying data representation to build the components
required for... | computer science |
17,725 | Sentence Boundary Detection for French with Subword-Level Information
Vectors and Convolutional Neural Networks | cs.CL | In this work we tackle the problem of sentence boundary detection applied to
French as a binary classification task ("sentence boundary" or "not sentence
boundary"). We combine convolutional neural networks with subword-level
information vectors, which are word embedding representations learned from
Wikipedia that take... | computer science |
17,726 | Network Features Based Co-hyponymy Detection | cs.CL | Distinguishing lexical relations has been a long term pursuit in natural
language processing (NLP) domain. Recently, in order to detect lexical
relations like hypernymy, meronymy, co-hyponymy etc., distributional semantic
models are being used extensively in some form or the other. Even though a lot
of efforts have bee... | computer science |
17,727 | Examining the Tip of the Iceberg: A Data Set for Idiom Translation | cs.CL | Neural Machine Translation (NMT) has been widely used in recent years with
significant improvements for many language pairs. Although state-of-the-art NMT
systems are generating progressively better translations, idiom translation
remains one of the open challenges in this field. Idioms, a category of
multiword express... | computer science |
17,728 | A Short Survey on Sense-Annotated Corpora for Diverse Languages and
Resources | cs.CL | With the advancement of research in word sense disambiguation and deep
learning, large sense-annotated datasets are increasingly important for
training supervised systems. However, gathering high-quality sense-annotated
data for as many instances as possible is an arduous task. This has led to the
proliferation of auto... | computer science |
17,729 | Distributional Term Set Expansion | cs.CL | This paper is a short empirical study of the performance of centrality and
classification based iterative term set expansion methods for distributional
semantic models. Iterative term set expansion is an interactive process using
distributional semantics models where a user labels terms as belonging to some
sought afte... | computer science |
17,730 | Linguistic unit discovery from multi-modal inputs in unwritten
languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop | cs.CL | We summarize the accomplishments of a multi-disciplinary workshop exploring
the computational and scientific issues surrounding the discovery of linguistic
units (subwords and words) in a language without orthography. We study the
replacement of orthographic transcriptions by images and/or translated text in
a well-res... | computer science |
17,731 | Classifying movie genres by analyzing text reviews | cs.CL | This paper proposes a method for classifying movie genres by only looking at
text reviews. The data used are from Large Movie Review Dataset v1.0 and IMDb.
This paper compared a K-nearest neighbors (KNN) model and a multilayer
perceptron (MLP) that uses tf-idf as input features. The paper also discusses
different evalu... | computer science |
17,732 | Deep contextualized word representations | cs.CL | We introduce a new type of deep contextualized word representation that
models both (1) complex characteristics of word use (e.g., syntax and
semantics), and (2) how these uses vary across linguistic contexts (i.e., to
model polysemy). Our word vectors are learned functions of the internal states
of a deep bidirectiona... | computer science |
17,733 | Universal Neural Machine Translation for Extremely Low Resource
Languages | cs.CL | In this paper, we propose a new universal machine translation approach
focusing on languages with a limited amount of parallel data. Our proposed
approach utilizes a transfer-learning approach to share lexical and sentences
level representations across multiple source languages into one target
language. The lexical par... | computer science |
17,734 | Improving Retrieval Modeling Using Cross Convolution Networks And Multi
Frequency Word Embedding | cs.CL | To build a satisfying chatbot that has the ability of managing a
goal-oriented multi-turn dialogue, accurate modeling of human conversation is
crucial. In this paper we concentrate on the task of response selection for
multi-turn human-computer conversation with a given context. Previous
approaches show weakness in cap... | computer science |
17,735 | Open Information Extraction on Scientific Text: An Evaluation | cs.CL | Open Information Extraction (OIE) is the task of the unsupervised creation of
structured information from text. OIE is often used as a starting point for a
number of downstream tasks including knowledge base construction, relation
extraction, and question answering. While OIE methods are targeted at being
domain indepe... | computer science |
17,736 | DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language
Inference | cs.CL | We present a novel deep learning architecture to address the natural language
inference (NLI) task. Existing approaches mostly rely on simple reading
mechanisms for independent encoding of the premise and hypothesis. Instead, we
propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to
efficiently mod... | computer science |
17,737 | Tools and resources for Romanian text-to-speech and speech-to-text
applications | cs.CL | In this paper we introduce a set of resources and tools aimed at providing
support for natural language processing, text-to-speech synthesis and speech
recognition for Romanian. While the tools are general purpose and can be used
for any language (we successfully trained our system for more than 50 languages
and partic... | computer science |
17,738 | Calculating the similarity between words and sentences using a lexical
database and corpus statistics | cs.CL | Calculating the semantic similarity between sentences is a long dealt problem
in the area of natural language processing. The semantic analysis field has a
crucial role to play in the research related to the text analytics. The
semantic similarity differs as the domain of operation differs. In this paper,
we present a ... | computer science |
17,739 | Event Nugget Detection with Forward-Backward Recurrent Neural Networks | cs.CL | Traditional event detection methods heavily rely on manually engineered rich
features. Recent deep learning approaches alleviate this problem by automatic
feature engineering. But such efforts, like tradition methods, have so far only
focused on single-token event mentions, whereas in practice events can also be
a phra... | computer science |
17,740 | JU_KS@SAIL_CodeMixed-2017: Sentiment Analysis for Indian Code Mixed
Social Media Texts | cs.CL | This paper reports about our work in the NLP Tool Contest @ICON-2017, shared
task on Sentiment Analysis for Indian Languages (SAIL) (code mixed). To
implement our system, we have used a machine learning algo-rithm called
Multinomial Na\"ive Bayes trained using n-gram and SentiWordnet features. We
have also used a small... | computer science |
17,741 | Cross-topic Argument Mining from Heterogeneous Sources Using
Attention-based Neural Networks | cs.CL | Argument mining is a core technology for automating argument search in large
document collections. Despite its usefulness for this task, most current
approaches to argument mining are designed for use only with specific text
types and fall short when applied to heterogeneous texts. In this paper, we
propose a new sente... | computer science |
17,742 | Learning beyond datasets: Knowledge Graph Augmented Neural Networks for
Natural language Processing | cs.CL | Machine Learning has been the quintessential solution for many AI problems,
but learning is still heavily dependent on the specific training data. Some
learning models can be incorporated with a prior knowledge in the Bayesian set
up, but these learning models do not have the ability to access any organised
world knowl... | computer science |
17,743 | Instance-based Inductive Deep Transfer Learning by Cross-Dataset
Querying with Locality Sensitive Hashing | cs.CL | Supervised learning models are typically trained on a single dataset and the
performance of these models rely heavily on the size of the dataset, i.e.,
amount of data available with the ground truth. Learning algorithms try to
generalize solely based on the data that is presented with during the training.
In this work,... | computer science |
17,744 | Fluency Over Adequacy: A Pilot Study in Measuring User Trust in
Imperfect MT | cs.CL | Although measuring intrinsic quality has been a key factor in the advancement
of Machine Translation (MT), successfully deploying MT requires considering not
just intrinsic quality but also the user experience, including aspects such as
trust. This work introduces a method of studying how users modulate their trust
in ... | computer science |
17,745 | Bayesian Models for Unit Discovery on a Very Low Resource Language | cs.CL | Developing speech technologies for low-resource languages has become a very
active research field over the last decade. Among others, Bayesian models have
shown some promising results on artificial examples but still lack of in situ
experiments. Our work applies state-of-the-art Bayesian models to unsupervised
Acoustic... | computer science |
17,746 | Can Network Embedding of Distributional Thesaurus be Combined with Word
Vectors for Better Representation? | cs.CL | Distributed representations of words learned from text have proved to be
successful in various natural language processing tasks in recent times. While
some methods represent words as vectors computed from text using predictive
model (Word2vec) or dense count based model (GloVe), others attempt to
represent these in a ... | computer science |
17,747 | Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in
Web Argumentation | cs.CL | Arguing without committing a fallacy is one of the main requirements of an
ideal debate. But even when debating rules are strictly enforced and fallacious
arguments punished, arguers often lapse into attacking the opponent by an ad
hominem argument. As existing research lacks solid empirical investigation of
the typolo... | computer science |
17,748 | Tied Multitask Learning for Neural Speech Translation | cs.CL | We explore multitask models for neural translation of speech, augmenting them
in order to reflect two intuitive notions. First, we introduce a model where
the second task decoder receives information from the decoder of the first
task, since higher-level intermediate representations should provide useful
information. S... | computer science |
17,749 | Zero-Shot Question Generation from Knowledge Graphs for Unseen
Predicates and Entity Types | cs.CL | We present a neural model for question generation from knowledge base triples
in a "Zero-Shot" setup, that is generating questions for triples containing
predicates, subject types or object types that were not seen at training time.
Our model leverages triples occurrences in the natural language corpus in an
encoder-de... | computer science |
17,750 | TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection | cs.CL | Detecting novelty of an entire document is an Artificial Intelligence (AI)
frontier problem that has widespread NLP applications, such as extractive
document summarization, tracking development of news events, predicting impact
of scholarly articles, etc. Important though the problem is, we are unaware of
any benchmark... | computer science |
17,751 | Attentive Tensor Product Learning for Language Generation and Grammar
Parsing | cs.CL | This paper proposes a new architecture - Attentive Tensor Product Learning
(ATPL) - to represent grammatical structures in deep learning models. ATPL is a
new architecture to bridge this gap by exploiting Tensor Product
Representations (TPR), a structured neural-symbolic model developed in
cognitive science, aiming to ... | computer science |
17,752 | Combining Textual Content and Structure to Improve Dialog Similarity | cs.CL | Chatbots, taking advantage of the success of the messaging apps and recent
advances in Artificial Intelligence, have become very popular, from helping
business to improve customer services to chatting to users for the sake of
conversation and engagement (celebrity or personal bots). However, developing
and improving a ... | computer science |
17,753 | CytonMT: an Efficient Neural Machine Translation Open-source Toolkit
Implemented in C++ | cs.CL | This paper presented an open-source neural machine translation toolkit named
CytonMT\footnote{https://github.com/arthurxlw/cytonMt}. The toolkit was built
from scratch using C++ and Nvidia's GPU-accelerated libraries. The toolkit
featured training efficiency, code simplicity and translation quality.
Benchmarks showed t... | computer science |
17,754 | Implicit Argument Prediction with Event Knowledge | cs.CL | Implicit arguments are not syntactically connected to their predicates, and
are therefore hard to extract. Previous work has used models with large numbers
of features, evaluated on very small datasets. We propose to train models for
implicit argument prediction on a simple cloze task, for which data can be
generated a... | computer science |
17,755 | CoVeR: Learning Covariate-Specific Vector Representations with Tensor
Decompositions | cs.CL | Word embedding is a useful approach to capture co-occurrence structures in a
large corpus of text. In addition to the text data itself, we often have
additional covariates associated with individual documents in the corpus---e.g.
the demographic of the author, time and venue of publication, etc.---and we
would like the... | computer science |
17,756 | MPST: A Corpus of Movie Plot Synopses with Tags | cs.CL | Social tagging of movies reveals a wide range of heterogeneous information
about movies, like the genre, plot structure, soundtracks, metadata, visual and
emotional experiences. Such information can be valuable in building automatic
systems to create tags for movies. Automatic tagging systems can help
recommendation en... | computer science |
17,757 | Multimodal Named Entity Recognition for Short Social Media Posts | cs.CL | We introduce a new task called Multimodal Named Entity Recognition (MNER) for
noisy user-generated data such as tweets or Snapchat captions, which comprise
short text with accompanying images. These social media posts often come in
inconsistent or incomplete syntax and lexical notations with very limited
surrounding te... | computer science |
17,758 | LIDIOMS: A Multilingual Linked Idioms Data Set | cs.CL | In this paper, we describe the LIDIOMS data set, a multilingual RDF
representation of idioms currently containing five languages: English, German,
Italian, Portuguese, and Russian. The data set is intended to support natural
language processing applications by providing links between idioms across
languages. The underl... | computer science |
17,759 | RDF2PT: Generating Brazilian Portuguese Texts from RDF Data | cs.CL | The generation of natural language from Resource Description Framework (RDF)
data has recently gained significant attention due to the continuous growth of
Linked Data. A number of these approaches generate natural language in
languages other than English, however, no work has been proposed to generate
Brazilian Portug... | computer science |
17,760 | Deep Multimodal Learning for Emotion Recognition in Spoken Language | cs.CL | In this paper, we present a novel deep multimodal framework to predict human
emotions based on sentence-level spoken language. Our architecture has two
distinctive characteristics. First, it extracts the high-level features from
both text and audio via a hybrid deep multimodal structure, which considers the
spatial inf... | computer science |
17,761 | EmotionLines: An Emotion Corpus of Multi-Party Conversations | cs.CL | Feeling emotion is a critical characteristic to distinguish people from
machines. Among all the multi-modal resources for emotion detection, textual
datasets are those containing the least additional information in addition to
semantics, and hence are adopted widely for testing the developed systems.
However, most of t... | computer science |
17,762 | Towards end-to-end spoken language understanding | cs.CL | Spoken language understanding system is traditionally designed as a pipeline
of a number of components. First, the audio signal is processed by an automatic
speech recognizer for transcription or n-best hypotheses. With the recognition
results, a natural language understanding system classifies the text to
structured d... | computer science |
17,763 | Interpretable Charge Predictions for Criminal Cases: Learning to
Generate Court Views from Fact Descriptions | cs.CL | In this paper, we propose to study the problem of COURT VIEW GENeration from
the fact description in a criminal case. The task aims to improve the
interpretability of charge prediction systems and help automatic legal document
generation. We formulate this task as a text-to-text natural language
generation (NLG) proble... | computer science |
17,764 | Evaluating Scoped Meaning Representations | cs.CL | Semantic parsing offers many opportunities to improve natural language
understanding. We present a semantically annotated parallel corpus for English,
German, Italian, and Dutch where sentences are aligned with scoped meaning
representations in order to capture the semantics of negation, modals,
quantification, and pre... | computer science |
17,765 | Ranking Sentences for Extractive Summarization with Reinforcement
Learning | cs.CL | Single document summarization is the task of producing a shorter version of a
document while preserving its principal information content. In this paper we
conceptualize extractive summarization as a sentence ranking task and propose a
novel training algorithm which globally optimizes the ROUGE evaluation metric
throug... | computer science |
17,766 | The JHU Speech LOREHLT 2017 System: Cross-Language Transfer for
Situation-Frame Detection | cs.CL | We describe the system our team used during NIST's LoReHLT (Low Resource
Human Language Technologies) 2017 Evaluations, which evaluated document topic
classification. We present a language agnostic approach combining universal
acoustic modeling, evaluation-language-to-English machine translation (MT) and
an English-lan... | computer science |
17,767 | OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for
Relation Classification in Scientific Papers using Piecewise Convolutional
Neural Networks | cs.CL | We describe our system for SemEval-2018 Shared Task on Semantic Relation
Extraction and Classification in Scientific Papers where we focus on the
Classification task. Our simple piecewise convolution neural encoder performs
decently in an end to end manner. A simple inter-task data augmentation
signifi- cantly boosts t... | computer science |
17,768 | Incorporating Discriminator in Sentence Generation: a Gibbs Sampling
Method | cs.CL | Generating plausible and fluent sentence with desired properties has long
been a challenge. Most of the recent works use recurrent neural networks (RNNs)
and their variants to predict following words given previous sequence and
target label. In this paper, we propose a novel framework to generate
constrained sentences ... | computer science |
17,769 | Revisiting the poverty of the stimulus: hierarchical generalization
without a hierarchical bias in recurrent neural networks | cs.CL | Syntactic rules in human language usually refer to the hierarchical structure
of sentences. However, the input during language acquisition can often be
explained equally well with rules based on linear order. The fact that children
consistently ignore these linear explanations to instead settle on hierarchical
explanat... | computer science |
17,770 | Did You Really Just Have a Heart Attack? Towards Robust Detection of
Personal Health Mentions in Social Media | cs.CL | Millions of users share their experiences on social media sites, such as
Twitter, which in turn generate valuable data for public health monitoring,
digital epidemiology, and other analyses of population health at global scale.
The first, critical, task for these applications is classifying whether a
personal health ev... | computer science |
17,771 | Language Distribution Prediction based on Batch Markov Monte Carlo
Simulation with Migration | cs.CL | Language spreading is a complex mechanism that involves issues like culture,
economics, migration, population etc. In this paper, we propose a set of
methods to model the dynamics of the spreading system. To model the randomness
of language spreading, we propose the Batch Markov Monte Carlo Simulation with
Migration(BM... | computer science |
17,772 | Deep Feed-forward Sequential Memory Networks for Speech Synthesis | cs.CL | The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among the
best parametric Text-to-Speech (TTS) systems in terms of the naturalness of
generated speech, especially the naturalness in prosody. However, the model
complexity and inference cost of BLSTM prevents its usage in many runtime
applications. Me... | computer science |
17,773 | EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and
XGboost Regressors for Emotion Analysis of Tweets | cs.CL | This paper describes our system that has been used in Task1 Affect in Tweets.
We combine two different approaches. The first one called N-Stream ConvNets,
which is a deep learning approach where the second one is XGboost regresseor
based on a set of embedding and lexicons based features. Our system was
evaluated on the... | computer science |
17,774 | Gender Aware Spoken Language Translation Applied to English-Arabic | cs.CL | Spoken Language Translation (SLT) is becoming more widely used and becoming a
communication tool that helps in crossing language barriers. One of the
challenges of SLT is the translation from a language without gender agreement
to a language with gender agreement such as English to Arabic. In this paper,
we introduce a... | computer science |
17,775 | From Phonology to Syntax: Unsupervised Linguistic Typology at Different
Levels with Language Embeddings | cs.CL | A core part of linguistic typology is the classification of languages
according to linguistic properties, such as those detailed in the World Atlas
of Language Structure (WALS). Doing this manually is prohibitively
time-consuming, which is in part evidenced by the fact that only 100 out of
over 7,000 languages spoken i... | computer science |
17,776 | Publishing a Quality Context-aware Annotated Corpus and Lexicon for
Harassment Research | cs.CL | Having a quality annotated corpus is essential especially for applied
research. Despite the recent focus of Web science community on researching
about cyberbullying, the community dose not still have standard benchmarks. In
this paper, we publish first, a quality annotated corpus and second, an
offensive words lexicon ... | computer science |
17,777 | Live Blog Corpus for Summarization | cs.CL | Live blogs are an increasingly popular news format to cover breaking news and
live events in online journalism. Online news websites around the world are
using this medium to give their readers a minute by minute update on an event.
Good summaries enhance the value of the live blogs for a reader but are often
not avail... | computer science |
17,778 | Convolutional Neural Networks for Toxic Comment Classification | cs.CL | Flood of information is produced in a daily basis through the global Internet
usage arising from the on-line interactive communications among users. While
this situation contributes significantly to the quality of human life,
unfortunately it involves enormous dangers, since on-line texts with high
toxicity can cause p... | computer science |
17,779 | Classifying Idiomatic and Literal Expressions Using Topic Models and
Intensity of Emotions | cs.CL | We describe an algorithm for automatic classification of idiomatic and
literal expressions. Our starting point is that words in a given text segment,
such as a paragraph, that are highranking representatives of a common topic of
discussion are less likely to be a part of an idiomatic expression. Our
additional hypothes... | computer science |
17,780 | A Hybrid Word-Character Model for Abstractive Summarization | cs.CL | Abstractive summarization is the popular research topic nowadays. Due to the
difference in language property, Chinese summarization also gains lots of
attention. Most of studies use character-based representation instead of
word-based to keep out the error introduced by word segmentation and OOV
problem. However, we be... | computer science |
17,781 | Extractive Text Summarization using Neural Networks | cs.CL | Text Summarization has been an extensively studied problem. Traditional
approaches to text summarization rely heavily on feature engineering. In
contrast to this, we propose a fully data-driven approach using feedforward
neural networks for single document summarization. We train and evaluate the
model on standard DUC ... | computer science |
17,782 | Collective Entity Disambiguation with Structured Gradient Tree Boosting | cs.CL | We present a gradient-tree-boosting-based structured learning model for
jointly disambiguating named entities in a document. Gradient tree boosting is
a widely used machine learning algorithm that underlies many top-performing
natural language processing systems. Surprisingly, most works limit the use of
gradient tree ... | computer science |
17,783 | Medical Exam Question Answering with Large-scale Reading Comprehension | cs.CL | Reading and understanding text is one important component in computer aided
diagnosis in clinical medicine, also being a major research problem in the
field of NLP. In this work, we introduce a question-answering task called MedQA
to study answering questions in clinical medicine using knowledge in a
large-scale docume... | computer science |
17,784 | Simultaneously Self-Attending to All Mentions for Full-Abstract
Biological Relation Extraction | cs.CL | Most work in relation extraction forms a prediction by looking at a short
span of text within a single sentence containing a single entity pair mention.
This approach often does not consider interactions across mentions, requires
redundant computation for each mention pair, and ignores relationships
expressed across se... | computer science |
17,785 | Analyzing Uncertainty in Neural Machine Translation | cs.CL | Machine translation is a popular test bed for research in neural
sequence-to-sequence models but despite much recent research, there is still a
lack of understanding of these models. Practitioners report performance
degradation with large beams, the under-estimation of rare words and a lack of
diversity in the final tr... | computer science |
17,786 | Improving Sentiment Analysis in Arabic Using Word Representation | cs.CL | The complexities of Arabic language in morphology, orthography and dialects
makes sentiment analysis for Arabic more challenging. Also, text feature
extraction from short messages like tweets, in order to gauge the sentiment,
makes this task even more difficult. In recent years, deep neural networks were
often employed... | computer science |
17,787 | Matching Natural Language Sentences with Hierarchical Sentence
Factorization | cs.CL | Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning sche... | computer science |
17,788 | XNMT: The eXtensible Neural Machine Translation Toolkit | cs.CL | This paper describes XNMT, the eXtensible Neural Machine Translation toolkit.
XNMT distin- guishes itself from other open-source NMT toolkits by its focus on
modular code design, with the purpose of enabling fast iteration in research
and replicable, reliable results. In this paper we describe the design of XNMT
and it... | computer science |
17,789 | Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational
Knowledge for Commonsense Machine Comprehension | cs.CL | This paper describes our system for SemEval-2018 Task 11: Machine
Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks
(TriAN) to model interactions between the passage, question and answers. To
incorporate commonsense knowledge, we augment input with relation embedding
from the graph of gener... | computer science |
17,790 | Joint Training for Neural Machine Translation Models with Monolingual
Data | cs.CL | Monolingual data have been demonstrated to be helpful in improving
translation quality of both statistical machine translation (SMT) systems and
neural machine translation (NMT) systems, especially in resource-poor or domain
adaptation tasks where parallel data are not rich enough. In this paper, we
propose a novel app... | computer science |
17,791 | Cross-lingual and Multilingual Speech Emotion Recognition on English and
French | cs.CL | Research on multilingual speech emotion recognition faces the problem that
most available speech corpora differ from each other in important ways, such as
annotation methods or interaction scenarios. These inconsistencies complicate
building a multilingual system. We present results for cross-lingual and
multilingual e... | computer science |
17,792 | A Factoid Question Answering System for Vietnamese | cs.CL | In this paper, we describe the development of an end-to-end factoid question
answering system for the Vietnamese language. This system combines both
statistical models and ontology-based methods in a chain of processing modules
to provide high-quality mappings from natural language text to entities. We
present the chal... | computer science |
17,793 | Representing Verbs as Argument Concepts | cs.CL | Verbs play an important role in the understanding of natural language text.
This paper studies the problem of abstracting the subject and object arguments
of a verb into a set of noun concepts, known as the "argument concepts". This
set of concepts, whose size is parameterized, represents the fine-grained
semantics of ... | computer science |
17,794 | Lexico-acoustic Neural-based Models for Dialog Act Classification | cs.CL | Recent works have proposed neural models for dialog act classification in
spoken dialogs. However, they have not explored the role and the usefulness of
acoustic information. We propose a neural model that processes both lexical and
acoustic features for classification. Our results on two benchmark datasets
reveal that... | computer science |
17,795 | DEMorphy, German Language Morphological Analyzer | cs.CL | DEMorphy is a morphological analyzer for German. It is built onto large,
compactified lexicons from German Morphological Dictionary. A guesser based on
German declension suffixed is also provided. For German, we provided a
state-of-art morphological analyzer. DEMorphy is implemented in Python with
ease of usability and... | computer science |
17,796 | Hybrid Model For Word Prediction Using Naive Bayes and Latent
Information | cs.CL | Historically, the Natural Language Processing area has been given too much
attention by many researchers. One of the main motivation beyond this interest
is related to the word prediction problem, which states that given a set words
in a sentence, one can recommend the next word. In literature, this problem is
solved b... | computer science |
17,797 | On Modular Training of Neural Acoustics-to-Word Model for LVCSR | cs.CL | End-to-end (E2E) automatic speech recognition (ASR) systems directly map
acoustics to words using a unified model. Previous works mostly focus on E2E
training a single model which integrates acoustic and language model into a
whole. Although E2E training benefits from sequence modeling and simplified
decoding pipelines... | computer science |
17,798 | Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse
Relation Classification | cs.CL | Identifying implicit discourse relations between text spans is a challenging
task because it requires understanding the meaning of the text. To tackle this
task, recent studies have tried several deep learning methods but few of them
exploited the syntactic information. In this work, we explore the idea of
incorporatin... | computer science |
17,799 | Understanding and Improving Multi-Sense Word Embeddings via Extended
Robust Principal Component Analysis | cs.CL | Unsupervised learned representations of polysemous words generate a large of
pseudo multi senses since unsupervised methods are overly sensitive to
contextual variations. In this paper, we address the pseudo multi-sense
detection for word embeddings by dimensionality reduction of sense pairs. We
propose a novel princip... | computer science |
17,800 | CAESAR: Context Awareness Enabled Summary-Attentive Reader | cs.CL | Comprehending meaning from natural language is a primary objective of Natural
Language Processing (NLP), and text comprehension is the cornerstone for
achieving this objective upon which all other problems like chat bots, language
translation and others can be achieved. We report a Summary-Attentive Reader we
designed ... | computer science |
17,801 | Concatenated $p$-mean Word Embeddings as Universal Cross-Lingual
Sentence Representations | cs.CL | Average word embeddings are a common baseline for more sophisticated sentence
embedding techniques. An important advantage of average word embeddings is
their computational and conceptual simplicity. However, they typically fall
short of the performances of more complex models such as InferSent. Here, we
generalize the... | computer science |
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