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17,202 | Natural Language Inference over Interaction Space | cs.CL | Natural Language Inference (NLI) task requires an agent to determine the
logical relationship between a natural language premise and a natural language
hypothesis. We introduce Interactive Inference Network (IIN), a novel class of
neural network architectures that is able to achieve high-level understanding
of the sent... | computer science |
17,203 | Linguistic Features of Genre and Method Variation in Translation: A
Computational Perspective | cs.CL | In this paper we describe the use of text classification methods to
investigate genre and method variation in an English - German translation
corpus. For this purpose we use linguistically motivated features representing
texts using a combination of part-of-speech tags arranged in bigrams, trigrams,
and 4-grams. The cl... | computer science |
17,204 | A Review of Evaluation Techniques for Social Dialogue Systems | cs.CL | In contrast with goal-oriented dialogue, social dialogue has no clear measure
of task success. Consequently, evaluation of these systems is notoriously hard.
In this paper, we review current evaluation methods, focusing on automatic
metrics. We conclude that turn-based metrics often ignore the context and do
not accoun... | computer science |
17,205 | Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis | cs.CL | This paper fills a gap in aspect-based sentiment analysis and aims to present
a new method for preparing and analysing texts concerning opinion and
generating user-friendly descriptive reports in natural language. We present a
comprehensive set of techniques derived from Rhetorical Structure Theory and
sentiment analys... | computer science |
17,206 | Towards an Arabic-English Machine-Translation Based on Semantic Web | cs.CL | Communication tools make the world like a small village and as a consequence
people can contact with others who are from different societies or who speak
different languages. This communication cannot happen effectively without
Machine Translation because they can be found anytime and everywhere. There are
a number of ... | computer science |
17,207 | Machine-Translation History and Evolution: Survey for Arabic-English
Translations | cs.CL | As a result of the rapid changes in information and communication technology
(ICT), the world has become a small village where people from all over the
world connect with each other in dialogue and communication via the Internet.
Also, communications have become a daily routine activity due to the new
globalization whe... | computer science |
17,208 | Synapse at CAp 2017 NER challenge: Fasttext CRF | cs.CL | We present our system for the CAp 2017 NER challenge which is about named
entity recognition on French tweets. Our system leverages unsupervised learning
on a larger dataset of French tweets to learn features feeding a CRF model. It
was ranked first without using any gazetteer or structured external data, with
an F-mea... | computer science |
17,209 | Self-Attentive Residual Decoder for Neural Machine Translation | cs.CL | Neural sequence-to-sequence networks with attention have achieved remarkable
performance for machine translation. One of the reasons for their effectiveness
is their ability to capture relevant source-side contextual information at each
time-step prediction through an attention mechanism. However, the target-side
conte... | computer science |
17,210 | Cross-Platform Emoji Interpretation: Analysis, a Solution, and
Applications | cs.CL | Most social media platforms are largely based on text, and users often write
posts to describe where they are, what they are seeing, and how they are
feeling. Because written text lacks the emotional cues of spoken and
face-to-face dialogue, ambiguities are common in written language. This problem
is exacerbated in the... | computer science |
17,211 | A Deep Generative Framework for Paraphrase Generation | cs.CL | Paraphrase generation is an important problem in NLP, especially in question
answering, information retrieval, information extraction, conversation systems,
to name a few. In this paper, we address the problem of generating paraphrases
automatically. Our proposed method is based on a combination of deep generative
mode... | computer science |
17,212 | Unsupervised Aspect Term Extraction with B-LSTM & CRF using
Automatically Labelled Datasets | cs.CL | Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and
is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA)
contest. The small amount of available datasets for supervised ATE and the
costly human annotation for aspect term labelling give rise to the need for
unsupervised ATE.... | computer science |
17,213 | Transcribing Against Time | cs.CL | We investigate the problem of manually correcting errors from an automatic
speech transcript in a cost-sensitive fashion. This is done by specifying a
fixed time budget, and then automatically choosing location and size of
segments for correction such that the number of corrected errors is maximized.
The core component... | computer science |
17,214 | And That's A Fact: Distinguishing Factual and Emotional Argumentation in
Online Dialogue | cs.CL | We investigate the characteristics of factual and emotional argumentation
styles observed in online debates. Using an annotated set of "factual" and
"feeling" debate forum posts, we extract patterns that are highly correlated
with factual and emotional arguments, and then apply a bootstrapping
methodology to find new p... | computer science |
17,215 | Are you serious?: Rhetorical Questions and Sarcasm in Social Media
Dialog | cs.CL | Effective models of social dialog must understand a broad range of rhetorical
and figurative devices. Rhetorical questions (RQs) are a type of figurative
language whose aim is to achieve a pragmatic goal, such as structuring an
argument, being persuasive, emphasizing a point, or being ironic. While there
are computatio... | computer science |
17,216 | Harvesting Creative Templates for Generating Stylistically Varied
Restaurant Reviews | cs.CL | Many of the creative and figurative elements that make language exciting are
lost in translation in current natural language generation engines. In this
paper, we explore a method to harvest templates from positive and negative
reviews in the restaurant domain, with the goal of vastly expanding the types
of stylistic v... | computer science |
17,217 | Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue | cs.CL | The use of irony and sarcasm in social media allows us to study them at scale
for the first time. However, their diversity has made it difficult to construct
a high-quality corpus of sarcasm in dialogue. Here, we describe the process of
creating a large- scale, highly-diverse corpus of online debate forums
dialogue, an... | computer science |
17,218 | Combining Search with Structured Data to Create a More Engaging User
Experience in Open Domain Dialogue | cs.CL | The greatest challenges in building sophisticated open-domain conversational
agents arise directly from the potential for ongoing mixed-initiative
multi-turn dialogues, which do not follow a particular plan or pursue a
particular fixed information need. In order to make coherent conversational
contributions in this con... | computer science |
17,219 | "How May I Help You?": Modeling Twitter Customer Service Conversations
Using Fine-Grained Dialogue Acts | cs.CL | Given the increasing popularity of customer service dialogue on Twitter,
analysis of conversation data is essential to understand trends in customer and
agent behavior for the purpose of automating customer service interactions. In
this work, we develop a novel taxonomy of fine-grained "dialogue acts"
frequently observ... | computer science |
17,220 | Order-Preserving Abstractive Summarization for Spoken Content Based on
Connectionist Temporal Classification | cs.CL | Connectionist temporal classification (CTC) is a powerful approach for
sequence-to-sequence learning, and has been popularly used in speech
recognition. The central ideas of CTC include adding a label "blank" during
training. With this mechanism, CTC eliminates the need of segment alignment,
and hence has been applied ... | computer science |
17,221 | Role of Morphology Injection in Statistical Machine Translation | cs.CL | Phrase-based Statistical models are more commonly used as they perform
optimally in terms of both, translation quality and complexity of the system.
Hindi and in general all Indian languages are morphologically richer than
English. Hence, even though Phrase-based systems perform very well for the less
divergent languag... | computer science |
17,222 | AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech
Recognition Baseline | cs.CL | An open-source Mandarin speech corpus called AISHELL-1 is released. It is by
far the largest corpus which is suitable for conducting the speech recognition
research and building speech recognition systems for Mandarin. The recording
procedure, including audio capturing devices and environments are presented in
details.... | computer science |
17,223 | Data Innovation for International Development: An overview of natural
language processing for qualitative data analysis | cs.CL | Availability, collection and access to quantitative data, as well as its
limitations, often make qualitative data the resource upon which development
programs heavily rely. Both traditional interview data and social media
analysis can provide rich contextual information and are essential for
research, appraisal, monito... | computer science |
17,224 | Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering | cs.CL | In this paper, we focus on the problem of answer triggering ad-dressed by
Yang et al. (2015), which is a critical component for a real-world question
answering system. We employ a hierarchical gated recurrent neural tensor
(HGRNT) model to capture both the context information and the deep
in-teractions between the cand... | computer science |
17,225 | Unwritten Languages Demand Attention Too! Word Discovery with
Encoder-Decoder Models | cs.CL | Word discovery is the task of extracting words from unsegmented text. In this
paper we examine to what extent neural networks can be applied to this task in
a realistic unwritten language scenario, where only small corpora and limited
annotations are available. We investigate two scenarios: one with no
supervision and ... | computer science |
17,226 | Toward a full-scale neural machine translation in production: the
Booking.com use case | cs.CL | While some remarkable progress has been made in neural machine translation
(NMT) research, there have not been many reports on its development and
evaluation in practice. This paper tries to fill this gap by presenting some of
our findings from building an in-house travel domain NMT system in a large
scale E-commerce s... | computer science |
17,227 | Limitations of Cross-Lingual Learning from Image Search | cs.CL | Cross-lingual representation learning is an important step in making NLP
scale to all the world's languages. Recent work on bilingual lexicon induction
suggests that it is possible to learn cross-lingual representations of words
based on similarities between images associated with these words. However, that
work focuse... | computer science |
17,228 | Sequence to Sequence Learning for Event Prediction | cs.CL | This paper presents an approach to the task of predicting an event
description from a preceding sentence in a text. Our approach explores
sequence-to-sequence learning using a bidirectional multi-layer recurrent
neural network. Our approach substantially outperforms previous work in terms
of the BLEU score on two datas... | computer science |
17,229 | Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural
Dialog Models | cs.CL | In this paper, we present a deep reinforcement learning (RL) framework for
iterative dialog policy optimization in end-to-end task-oriented dialog
systems. Popular approaches in learning dialog policy with RL include letting a
dialog agent to learn against a user simulator. Building a reliable user
simulator, however, ... | computer science |
17,230 | Paraphrasing verbal metonymy through computational methods | cs.CL | Verbal metonymy has received relatively scarce attention in the field of
computational linguistics despite the fact that a model to accurately
paraphrase metonymy has applications both in academia and the technology
sector. The method described in this paper makes use of data from the British
National Corpus in order t... | computer science |
17,231 | Dynamic Oracle for Neural Machine Translation in Decoding Phase | cs.CL | The past several years have witnessed the rapid progress of end-to-end Neural
Machine Translation (NMT). However, there exists discrepancy between training
and inference in NMT when decoding, which may lead to serious problems since
the model might be in a part of the state space it has never seen during
training. To a... | computer science |
17,232 | A Fast and Accurate Vietnamese Word Segmenter | cs.CL | We propose a novel approach to Vietnamese word segmentation. Our approach is
based on the Single Classification Ripple Down Rules methodology (Compton and
Jansen, 1990), where rules are stored in an exception structure and new rules
are only added to correct segmentation errors given by existing rules.
Experimental res... | computer science |
17,233 | Aspect-Based Relational Sentiment Analysis Using a Stacked Neural
Network Architecture | cs.CL | Sentiment analysis can be regarded as a relation extraction problem in which
the sentiment of some opinion holder towards a certain aspect of a product,
theme or event needs to be extracted. We present a novel neural architecture
for sentiment analysis as a relation extraction problem that addresses this
problem by div... | computer science |
17,234 | Aspect-Based Sentiment Analysis Using a Two-Step Neural Network
Architecture | cs.CL | The World Wide Web holds a wealth of information in the form of unstructured
texts such as customer reviews for products, events and more. By extracting and
analyzing the expressed opinions in customer reviews in a fine-grained way,
valuable opportunities and insights for customers and businesses can be gained.
We prop... | computer science |
17,235 | Improving Opinion-Target Extraction with Character-Level Word Embeddings | cs.CL | Fine-grained sentiment analysis is receiving increasing attention in recent
years. Extracting opinion target expressions (OTE) in reviews is often an
important step in fine-grained, aspect-based sentiment analysis. Retrieving
this information from user-generated text, however, can be difficult. Customer
reviews, for in... | computer science |
17,236 | Language Modeling with Highway LSTM | cs.CL | Language models (LMs) based on Long Short Term Memory (LSTM) have shown good
gains in many automatic speech recognition tasks. In this paper, we extend an
LSTM by adding highway networks inside an LSTM and use the resulting Highway
LSTM (HW-LSTM) model for language modeling. The added highway networks increase
the dept... | computer science |
17,237 | A Recorded Debating Dataset | cs.CL | This paper describes an audio and textual dataset of debating speeches, a
first-of-a-kind resource for the growing research field of computational
argumentation and debating technologies. We detail the process of speech
recording by professional debaters, the transcription of the speeches with an
Automatic Speech Recog... | computer science |
17,238 | De-identification of medical records using conditional random fields and
long short-term memory networks | cs.CL | The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing
focuses on the de-identification of psychiatric evaluation records. This paper
describes two participating systems of our team, based on conditional random
fields (CRFs) and long short-term memory networks (LSTMs). A pre-processing
module was in... | computer science |
17,239 | On the Use of Machine Translation-Based Approaches for Vietnamese
Diacritic Restoration | cs.CL | This paper presents an empirical study of two machine translation-based
approaches for Vietnamese diacritic restoration problem, including phrase-based
and neural-based machine translation models. This is the first work that
applies neural-based machine translation method to this problem and gives a
thorough comparison... | computer science |
17,240 | Speech Recognition Challenge in the Wild: Arabic MGB-3 | cs.CL | This paper describes the Arabic MGB-3 Challenge - Arabic Speech Recognition
in the Wild. Unlike last year's Arabic MGB-2 Challenge, for which the
recognition task was based on more than 1,200 hours broadcast TV news
recordings from Aljazeera Arabic TV programs, MGB-3 emphasises dialectal Arabic
using a multi-genre coll... | computer science |
17,241 | Retrofitting Concept Vector Representations of Medical Concepts to
Improve Estimates of Semantic Similarity and Relatedness | cs.CL | Estimation of semantic similarity and relatedness between biomedical concepts
has utility for many informatics applications. Automated methods fall into two
categories: methods based on distributional statistics drawn from text corpora,
and methods using the structure of existing knowledge resources. Methods in the
for... | computer science |
17,242 | Inducing Distant Supervision in Suggestion Mining through Part-of-Speech
Embeddings | cs.CL | Mining suggestion expressing sentences from a given text is a less
investigated sentence classification task, and therefore lacks hand labeled
benchmark datasets. In this work, we propose and evaluate two approaches for
distant supervision in suggestion mining. The distant supervision is obtained
through a large silver... | computer science |
17,243 | Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts | cs.CL | Word embedding is a Natural Language Processing (NLP) technique that
automatically maps words from a vocabulary to vectors of real numbers in an
embedding space. It has been widely used in recent years to boost the
performance of a vari-ety of NLP tasks such as Named Entity Recognition,
Syntac-tic Parsing and Sentiment... | computer science |
17,244 | WERd: Using Social Text Spelling Variants for Evaluating Dialectal
Speech Recognition | cs.CL | We study the problem of evaluating automatic speech recognition (ASR) systems
that target dialectal speech input. A major challenge in this case is that the
orthography of dialects is typically not standardized. From an ASR evaluation
perspective, this means that there is no clear gold standard for the expected
output,... | computer science |
17,245 | Improving Language Modelling with Noise-contrastive estimation | cs.CL | Neural language models do not scale well when the vocabulary is large.
Noise-contrastive estimation (NCE) is a sampling-based method that allows for
fast learning with large vocabularies. Although NCE has shown promising
performance in neural machine translation, it was considered to be an
unsuccessful approach for lan... | computer science |
17,246 | Sentence Correction Based on Large-scale Language Modelling | cs.CL | With the further development of informatization, more and more data is stored
in the form of text. There are some loss of text during their generation and
transmission. The paper aims to establish a language model based on the
large-scale corpus to complete the restoration of missing text. In this paper,
we introduce a... | computer science |
17,247 | Neural Machine Translation | cs.CL | Draft of textbook chapter on neural machine translation. a comprehensive
treatment of the topic, ranging from introduction to neural networks,
computation graphs, description of the currently dominant attentional
sequence-to-sequence model, recent refinements, alternative architectures and
challenges. Written as chapte... | computer science |
17,248 | Challenging Neural Dialogue Models with Natural Data: Memory Networks
Fail on Incremental Phenomena | cs.CL | Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis;
and it contains many sorts of disfluency such as mid-utterance/sentence
hesitations, interruptions, and self-corrections. But training data for machine
learning approaches to dialogue processing is often either cleaned-up or wholly
synthetic ... | computer science |
17,249 | Bootstrapping incremental dialogue systems from minimal data: the
generalisation power of dialogue grammars | cs.CL | We investigate an end-to-end method for automatically inducing task-based
dialogue systems from small amounts of unannotated dialogue data. It combines
an incremental semantic grammar - Dynamic Syntax and Type Theory with Records
(DS-TTR) - with Reinforcement Learning (RL), where language generation and
dialogue manage... | computer science |
17,250 | Mitigating the Impact of Speech Recognition Errors on Chatbot using
Sequence-to-Sequence Model | cs.CL | We apply sequence-to-sequence model to mitigate the impact of speech
recognition errors on open domain end-to-end dialog generation. We cast the
task as a domain adaptation problem where ASR transcriptions and original text
are in two different domains. In this paper, our proposed model includes two
individual encoders... | computer science |
17,251 | Long Short-Term Memory for Japanese Word Segmentation | cs.CL | This study presents a Long Short-Term Memory (LSTM) neural network approach
to Japanese word segmentation (JWS). Previous studies on Chinese word
segmentation (CWS) succeeded in using recurrent neural networks such as LSTM
and gated recurrent units (GRU). However, in contrast to Chinese, Japanese
includes several chara... | computer science |
17,252 | Language Independent Acquisition of Abbreviations | cs.CL | This paper addresses automatic extraction of abbreviations (encompassing
acronyms and initialisms) and corresponding long-form expansions from plain
unstructured text. We create and are going to release a multilingual resource
for abbreviations and their corresponding expansions, built automatically by
exploiting Wikip... | computer science |
17,253 | Identifying Phrasemes via Interlingual Association Measures -- A
Data-driven Approach on Dependency-parsed and Word-aligned Parallel Corpora | cs.CL | This is a preprint of the article "Identifying Phrasemes via Interlingual
Association Measures" that was presented in February 2016 at the LeKo (Lexical
combinations and typified speech in a multilingual context) conference in
Innsbruck. | computer science |
17,254 | Dataset for the First Evaluation on Chinese Machine Reading
Comprehension | cs.CL | Machine Reading Comprehension (MRC) has become enormously popular recently
and has attracted a lot of attention. However, existing reading comprehension
datasets are mostly in English. To add diversity in reading comprehension
datasets, in this paper we propose a new Chinese reading comprehension dataset
for accelerati... | computer science |
17,255 | Using objective words in the reviews to improve the colloquial arabic
sentiment analysis | cs.CL | One of the main difficulties in sentiment analysis of the Arabic language is
the presence of the colloquialism. In this paper, we examine the effect of
using objective words in conjunction with sentimental words on sentiment
classification for the colloquial Arabic reviews, specifically Jordanian
colloquial reviews. Th... | computer science |
17,256 | Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews | cs.CL | Many people use Yelp to find a good restaurant. Nonetheless, with only an
overall rating for each restaurant, Yelp offers not enough information for
independently judging its various aspects such as environment, service or
flavor. In this paper, we introduced a machine learning based method to
characterize such aspects... | computer science |
17,257 | DOC: Deep Open Classification of Text Documents | cs.CL | Traditional supervised learning makes the closed-world assumption that the
classes appeared in the test data must have appeared in training. This also
applies to text learning or text classification. As learning is used
increasingly in dynamic open environments where some new/test documents may not
belong to any of the... | computer science |
17,258 | Improving a Multi-Source Neural Machine Translation Model with Corpus
Extension for Low-Resource Languages | cs.CL | In machine translation, we often try to collect resources to improve
performance. However, most of the language pairs, such as Korean-Arabic and
Korean-Vietnamese, do not have enough resources to train machine translation
systems. In this paper, we propose the use of synthetic methods for extending a
low-resource corpu... | computer science |
17,259 | Input-to-Output Gate to Improve RNN Language Models | cs.CL | This paper proposes a reinforcing method that refines the output layers of
existing Recurrent Neural Network (RNN) language models. We refer to our
proposed method as Input-to-Output Gate (IOG). IOG has an extremely simple
structure, and thus, can be easily combined with any RNN language models. Our
experiments on the ... | computer science |
17,260 | Dataset Construction via Attention for Aspect Term Extraction with
Distant Supervision | cs.CL | Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or
text spans, with the end goal of performing aspect-based sentiment analysis.
The small amount of available datasets for supervised ATE and the fact that
they cover only a few domains raise the need for exploiting other data sources
in new and... | computer science |
17,261 | Predicting Disease-Gene Associations using Cross-Document Graph-based
Features | cs.CL | In the context of personalized medicine, text mining methods pose an
interesting option for identifying disease-gene associations, as they can be
used to generate novel links between diseases and genes which may complement
knowledge from structured databases. The most straightforward approach to
extract such links from... | computer science |
17,262 | Learning to Explain Non-Standard English Words and Phrases | cs.CL | We describe a data-driven approach for automatically explaining new,
non-standard English expressions in a given sentence, building on a large
dataset that includes 15 years of crowdsourced examples from
UrbanDictionary.com. Unlike prior studies that focus on matching keywords from
a slang dictionary, we investigate th... | computer science |
17,263 | Learning Distributions of Meant Color | cs.CL | When a speaker says the name of a color, the color they picture is not
necessarily the same as the listener imagines. Color is a grounded semantic
task, but that grounding is not a single value as the most recent works on
color-generation do. Rather proper understanding of color language requires the
capacity to map a ... | computer science |
17,264 | A Bimodal Network Approach to Model Topic Dynamics | cs.CL | This paper presents an intertemporal bimodal network to analyze the evolution
of the semantic content of a scientific field within the framework of topic
modeling, namely using the Latent Dirichlet Allocation (LDA). The main
contribution is the conceptualization of the topic dynamics and its
formalization and codificat... | computer science |
17,265 | Prosodic Features from Large Corpora of Child-Directed Speech as
Predictors of the Age of Acquisition of Words | cs.CL | The impressive ability of children to acquire language is a widely studied
phenomenon, and the factors influencing the pace and patterns of word learning
remains a subject of active research. Although many models predicting the age
of acquisition of words have been proposed, little emphasis has been directed
to the raw... | computer science |
17,266 | Replicability Analysis for Natural Language Processing: Testing
Significance with Multiple Datasets | cs.CL | With the ever-growing amounts of textual data from a large variety of
languages, domains, and genres, it has become standard to evaluate NLP
algorithms on multiple datasets in order to ensure consistent performance
across heterogeneous setups. However, such multiple comparisons pose
significant challenges to traditiona... | computer science |
17,267 | An attentive neural architecture for joint segmentation and parsing and
its application to real estate ads | cs.CL | In processing human produced text using natural language processing (NLP)
techniques, two fundamental subtasks that arise are (i) segmentation of the
plain text into meaningful subunits (e.g., entities), and (ii) dependency
parsing, to establish relations between subunits. In this paper, we develop a
relatively simple ... | computer science |
17,268 | Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named
Entity Recognition | cs.CL | Named Entity Recognition (NER) is one of the most common tasks of the natural
language processing. The purpose of NER is to find and classify tokens in text
documents into predefined categories called tags, such as person names,
quantity expressions, percentage expressions, names of locations,
organizations, as well as... | computer science |
17,269 | A Deep Neural Network Approach To Parallel Sentence Extraction | cs.CL | Parallel sentence extraction is a task addressing the data sparsity problem
found in multilingual natural language processing applications. We propose an
end-to-end deep neural network approach to detect translational equivalence
between sentences in two different languages. In contrast to previous
approaches, which ty... | computer science |
17,270 | Sentiment Classification with Word Attention based on Weakly Supervised
Learning with a Convolutional Neural Network | cs.CL | In order to maximize the applicability of sentiment analysis results, it is
necessary to not only classify the overall sentiment (positive/negative) of a
given document but also to identify the main words that contribute to the
classification. However, most datasets for sentiment analysis only have the
sentiment label ... | computer science |
17,271 | Graph Convolutional Networks for Named Entity Recognition | cs.CL | In this paper we investigate the role of the dependency tree in a named
entity recognizer upon using a set of GCN. We perform a comparison among
different NER architectures and show that the grammar of a sentence positively
influences the results. Experiments on the ontonotes dataset demonstrate
consistent performance ... | computer science |
17,272 | A Web of Hate: Tackling Hateful Speech in Online Social Spaces | cs.CL | Online social platforms are beset with hateful speech - content that
expresses hatred for a person or group of people. Such content can frighten,
intimidate, or silence platform users, and some of it can inspire other users
to commit violence. Despite widespread recognition of the problems posed by
such content, reliab... | computer science |
17,273 | Jointly Trained Sequential Labeling and Classification by Sparse
Attention Neural Networks | cs.CL | Sentence-level classification and sequential labeling are two fundamental
tasks in language understanding. While these two tasks are usually modeled
separately, in reality, they are often correlated, for example in intent
classification and slot filling, or in topic classification and named-entity
recognition. In order... | computer science |
17,274 | The First Evaluation of Chinese Human-Computer Dialogue Technology | cs.CL | In this paper, we introduce the first evaluation of Chinese human-computer
dialogue technology. We detail the evaluation scheme, tasks, metrics and how to
collect and annotate the data for training, developing and test. The evaluation
includes two tasks, namely user intent classification and online testing of
task-orie... | computer science |
17,275 | Towards Universal Semantic Tagging | cs.CL | The paper proposes the task of universal semantic tagging---tagging word
tokens with language-neutral, semantically informative tags. We argue that the
task, with its independent nature, contributes to better semantic analysis for
wide-coverage multilingual text. We present the initial version of the semantic
tagset an... | computer science |
17,276 | Symbol, Conversational, and Societal Grounding with a Toy Robot | cs.CL | 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. | computer science |
17,277 | Speaker Role Contextual Modeling for Language Understanding and Dialogue
Policy Learning | cs.CL | 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 ro... | computer science |
17,278 | Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken
Language Understanding | cs.CL | 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... | computer science |
17,279 | Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction | cs.CL | 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-vecto... | computer science |
17,280 | What Words Do We Use to Lie?: Word Choice in Deceptive Messages | cs.CL | 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 als... | computer science |
17,281 | Fully Automated Fact Checking Using External Sources | cs.CL | 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 p... | computer science |
17,282 | Robust Tuning Datasets for Statistical Machine Translation | cs.CL | 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 param... | computer science |
17,283 | Visual Reasoning with Natural Language | cs.CL | 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 increa... | computer science |
17,284 | HUMOR: A Crowd-Annotated Spanish Corpus for Humor Analysis | cs.CL | Computational Humor, as the name implies, studies humor from a computational
perspective, and it fosters several tasks, such as humor recognition, humor
generation and humor scoring. The area has been little explored, making it
attractive to tackle by novel Natural Language Processing and Machine Learning
techniques. H... | computer science |
17,285 | Attentive Convolution | cs.CL | In NLP, convolution neural networks (CNNs) have benefited less than recurrent
neural networks (RNNs) from attention mechanisms. We hypothesize that this is
because 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 integr... | computer science |
17,286 | Building Chatbots from Forum Data: Model Selection Using Question
Answering Metrics | cs.CL | 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... | computer science |
17,287 | Compiling and Processing Historical and Contemporary Portuguese Corpora | cs.CL | 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 earl... | computer science |
17,288 | Distributional Inclusion Vector Embedding for Unsupervised Hypernymy
Detection | cs.CL | 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 hyp... | computer science |
17,289 | Minimal Dependency Translation: a Framework for Computer-Assisted
Translation for Under-Resourced Languages | cs.CL | 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 implementati... | computer science |
17,290 | Identifying Nominals with No Head Match Co-references Using Deep
Learning | cs.CL | 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... | computer science |
17,291 | Event Identification as a Decision Process with Non-linear
Representation of Text | cs.CL | 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 l... | computer science |
17,292 | Annotation and Detection of Emotion in Text-based Dialogue Systems with
CNN | cs.CL | 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 an... | computer science |
17,293 | Is Structure Necessary for Modeling Argument Expectations in
Distributional Semantics? | cs.CL | 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 repres... | computer science |
17,294 | MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language
Processing | cs.CL | 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 ... | computer science |
17,295 | Towards an Inferential Lexicon of Event Selecting Predicates for French | cs.CL | 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-em... | computer science |
17,296 | Improving Lexical Choice in Neural Machine Translation | cs.CL | 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... | computer science |
17,297 | Transferring Semantic Roles Using Translation and Syntactic Information | cs.CL | 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 ... | computer science |
17,298 | Cross-Language Question Re-Ranking | cs.CL | 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 pa... | computer science |
17,299 | Semantic Sentiment Analysis of Twitter Data | cs.CL | 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... | computer science |
17,300 | Discourse Structure in Machine Translation Evaluation | cs.CL | 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 sim... | computer science |
17,301 | Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl | cs.CL | 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... | computer science |
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