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16,502 | Nematus: a Toolkit for Neural Machine Translation | cs.CL | We present Nematus, a toolkit for Neural Machine Translation. The toolkit
prioritizes high translation accuracy, usability, and extensibility. Nematus
has been used to build top-performing submissions to shared translation tasks
at WMT and IWSLT, and has been used to train systems for production
environments. | computer science |
16,503 | DRAGNN: A Transition-based Framework for Dynamically Connected Neural
Networks | cs.CL | In this work, we present a compact, modular framework for constructing novel
recurrent neural architectures. Our basic module is a new generic unit, the
Transition Based Recurrent Unit (TBRU). In addition to hidden layer
activations, TBRUs have discrete state dynamics that allow network connections
to be built dynamica... | computer science |
16,504 | Geometrical morphology | cs.CL | We explore inflectional morphology as an example of the relationship of the
discrete and the continuous in linguistics. The grammar requests a form of a
lexeme by specifying a set of feature values, which corresponds to a corner M
of a hypercube in feature value space. The morphology responds to that request
by providi... | computer science |
16,505 | Exploring Question Understanding and Adaptation in Neural-Network-Based
Question Answering | cs.CL | The last several years have seen intensive interest in exploring
neural-network-based models for machine comprehension (MC) and question
answering (QA). In this paper, we approach the problems by closely modelling
questions in a neural network framework. We first introduce syntactic
information to help encode questions... | computer science |
16,506 | Joint Learning of Correlated Sequence Labelling Tasks Using
Bidirectional Recurrent Neural Networks | cs.CL | The stream of words produced by Automatic Speech Recognition (ASR) systems is
typically devoid of punctuations and formatting. Most natural language
processing applications expect segmented and well-formatted texts as input,
which is not available in ASR output. This paper proposes a novel technique of
jointly modeling... | computer science |
16,507 | Extending Automatic Discourse Segmentation for Texts in Spanish to
Catalan | cs.CL | At present, automatic discourse analysis is a relevant research topic in the
field of NLP. However, discourse is one of the phenomena most difficult to
process. Although discourse parsers have been already developed for several
languages, this tool does not exist for Catalan. In order to implement this
kind of parser, ... | computer science |
16,508 | Sparse Named Entity Classification using Factorization Machines | cs.CL | Named entity classification is the task of classifying text-based elements
into various categories, including places, names, dates, times, and monetary
values. A bottleneck in named entity classification, however, is the data
problem of sparseness, because new named entities continually emerge, making it
rather difficu... | computer science |
16,509 | Improving Neural Machine Translation with Conditional Sequence
Generative Adversarial Nets | cs.CL | This paper proposes an approach for applying GANs to NMT. We build a
conditional sequence generative adversarial net which comprises of two
adversarial sub models, a generator and a discriminator. The generator aims to
generate sentences which are hard to be discriminated from human-translated
sentences ( i.e., the gol... | computer science |
16,510 | SyntaxNet Models for the CoNLL 2017 Shared Task | cs.CL | We describe a baseline dependency parsing system for the CoNLL2017 Shared
Task. This system, which we call "ParseySaurus," uses the DRAGNN framework
[Kong et al, 2017] to combine transition-based recurrent parsing and tagging
with character-based word representations. On the v1.3 Universal Dependencies
Treebanks, the n... | computer science |
16,511 | Is this word borrowed? An automatic approach to quantify the likeliness
of borrowing in social media | cs.CL | Code-mixing or code-switching are the effortless phenomena of natural
switching between two or more languages in a single conversation. Use of a
foreign word in a language; however, does not necessarily mean that the speaker
is code-switching because often languages borrow lexical items from other
languages. If a word ... | computer science |
16,512 | End-to-end optimization of goal-driven and visually grounded dialogue
systems | cs.CL | End-to-end design of dialogue systems has recently become a popular research
topic thanks to powerful tools such as encoder-decoder architectures for
sequence-to-sequence learning. Yet, most current approaches cast human-machine
dialogue management as a supervised learning problem, aiming at predicting the
next utteran... | computer science |
16,513 | Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity
Model | cs.CL | This paper describes a neural-network model which performed competitively
(top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS)
task. Our system employs an attention-based recurrent neural network model that
optimizes the sentence similarity. In this paper, we describe our participation
in the mul... | computer science |
16,514 | Construction of a Japanese Word Similarity Dataset | cs.CL | An evaluation of distributed word representation is generally conducted using
a word similarity task and/or a word analogy task. There are many datasets
readily available for these tasks in English. However, evaluating distributed
representation in languages that do not have such resources (e.g., Japanese) is
difficult... | computer science |
16,515 | Métodos de Otimização Combinatória Aplicados ao Problema de
Compressão MultiFrases | cs.CL | The Internet has led to a dramatic increase in the amount of available
information. In this context, reading and understanding this flow of
information have become costly tasks. In the last years, to assist people to
understand textual data, various Natural Language Processing (NLP) applications
based on Combinatorial ... | computer science |
16,516 | Native Language Identification using Stacked Generalization | cs.CL | Ensemble methods using multiple classifiers have proven to be the most
successful approach for the task of Native Language Identification (NLI),
achieving the current state of the art. However, a systematic examination of
ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble
architectures such... | computer science |
16,517 | Deep LSTM for Large Vocabulary Continuous Speech Recognition | cs.CL | Recurrent neural networks (RNNs), especially long short-term memory (LSTM)
RNNs, are effective network for sequential task like speech recognition. Deeper
LSTM models perform well on large vocabulary continuous speech recognition,
because of their impressive learning ability. However, it is more difficult to
train a de... | computer science |
16,518 | The NLTK FrameNet API: Designing for Discoverability with a Rich
Linguistic Resource | cs.CL | A new Python API, integrated within the NLTK suite, offers access to the
FrameNet 1.7 lexical database. The lexicon (structured in terms of frames) as
well as annotated sentences can be processed programatically, or browsed with
human-readable displays via the interactive Python prompt. | computer science |
16,519 | Topic Identification for Speech without ASR | cs.CL | Modern topic identification (topic ID) systems for speech use automatic
speech recognition (ASR) to produce speech transcripts, and perform supervised
classification on such ASR outputs. However, under resource-limited conditions,
the manually transcribed speech required to develop standard ASR systems can be
severely ... | computer science |
16,520 | Hierarchical RNN with Static Sentence-Level Attention for Text-Based
Speaker Change Detection | cs.CL | Traditional speaker change detection in dialogues is typically based on audio
input. In some scenarios, however, researchers can only obtain text, and do not
have access to raw audio signals. Moreover, with the increasing need of deep
semantic processing, text-based dialogue understanding is attracting more
attention i... | computer science |
16,521 | Sequential Recurrent Neural Networks for Language Modeling | cs.CL | Feedforward Neural Network (FNN)-based language models estimate the
probability of the next word based on the history of the last N words, whereas
Recurrent Neural Networks (RNN) perform the same task based only on the last
word and some context information that cycles in the network. This paper
presents a novel approa... | computer science |
16,522 | Multimodal Compact Bilinear Pooling for Multimodal Neural Machine
Translation | cs.CL | In state-of-the-art Neural Machine Translation, an attention mechanism is
used during decoding to enhance the translation. At every step, the decoder
uses this mechanism to focus on different parts of the source sentence to
gather the most useful information before outputting its target word. Recently,
the effectivenes... | computer science |
16,523 | Rapid-Rate: A Framework for Semi-supervised Real-time Sentiment Trend
Detection in Unstructured Big Data | cs.CL | Commercial establishments like restaurants, service centres and retailers
have several sources of customer feedback about products and services, most of
which need not be as structured as rated reviews provided by services like
Yelp, or Amazon, in terms of sentiment conveyed. For instance, Amazon provides
a fine-graine... | computer science |
16,524 | TokTrack: A Complete Token Provenance and Change Tracking Dataset for
the English Wikipedia | cs.CL | We present a dataset that contains every instance of all tokens (~ words)
ever written in undeleted, non-redirect English Wikipedia articles until
October 2016, in total 13,545,349,787 instances. Each token is annotated with
(i) the article revision it was originally created in, and (ii) lists with all
the revisions in... | computer science |
16,525 | Crowdsourcing Universal Part-Of-Speech Tags for Code-Switching | cs.CL | Code-switching is the phenomenon by which bilingual speakers switch between
multiple languages during communication. The importance of developing language
technologies for codeswitching data is immense, given the large populations
that routinely code-switch. High-quality linguistic annotations are extremely
valuable fo... | computer science |
16,526 | Simplifying the Bible and Wikipedia Using Statistical Machine
Translation | cs.CL | I started this work with the hope of generating a text synthesizer (like a
musical synthesizer) that can imitate certain linguistic styles. Most of the
report focuses on text simplification using statistical machine translation
(SMT) techniques. I applied MOSES to a parallel corpus of the Bible (King James
Version and ... | computer science |
16,527 | Morphological Analysis for the Maltese Language: The Challenges of a
Hybrid System | cs.CL | Maltese is a morphologically rich language with a hybrid morphological system
which features both concatenative and non-concatenative processes. This paper
analyses the impact of this hybridity on the performance of machine learning
techniques for morphological labelling and clustering. In particular, we
analyse a data... | computer science |
16,528 | LEPOR: An Augmented Machine Translation Evaluation Metric | cs.CL | Machine translation (MT) was developed as one of the hottest research topics
in the natural language processing (NLP) literature. One important issue in MT
is that how to evaluate the MT system reasonably and tell us whether the
translation system makes an improvement or not. The traditional manual judgment
methods are... | computer science |
16,529 | Learning Simpler Language Models with the Differential State Framework | cs.CL | Learning useful information across long time lags is a critical and difficult
problem for temporal neural models in tasks such as language modeling. Existing
architectures that address the issue are often complex and costly to train. The
Differential State Framework (DSF) is a simple and high-performing design that
uni... | computer science |
16,530 | Question Answering from Unstructured Text by Retrieval and Comprehension | cs.CL | Open domain Question Answering (QA) systems must interact with external
knowledge sources, such as web pages, to find relevant information. Information
sources like Wikipedia, however, are not well structured and difficult to
utilize in comparison with Knowledge Bases (KBs). In this work we present a
two-step approach ... | computer science |
16,531 | A Sentence Simplification System for Improving Relation Extraction | cs.CL | In this demo paper, we present a text simplification approach that is
directed at improving the performance of state-of-the-art Open Relation
Extraction (RE) systems. As syntactically complex sentences often pose a
challenge for current Open RE approaches, we have developed a simplification
framework that performs a pr... | computer science |
16,532 | Learning Similarity Functions for Pronunciation Variations | cs.CL | A significant source of errors in Automatic Speech Recognition (ASR) systems
is due to pronunciation variations which occur in spontaneous and
conversational speech. Usually ASR systems use a finite lexicon that provides
one or more pronunciations for each word. In this paper, we focus on learning a
similarity function... | computer science |
16,533 | Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and
Distributed Word Representations | cs.CL | In this paper, we propose an extension to graph-based sentiment lexicon
induction methods by incorporating distributed and semantic word
representations in building the similarity graph to expand a three-dimensional
sentiment lexicon. We also implemented and evaluated the label propagation
using four different word rep... | computer science |
16,534 | Hierarchical Classification for Spoken Arabic Dialect Identification
using Prosody: Case of Algerian Dialects | cs.CL | In daily communications, Arabs use local dialects which are hard to identify
automatically using conventional classification methods. The dialect
identification challenging task becomes more complicated when dealing with an
under-resourced dialects belonging to a same county/region. In this paper, we
start by analyzing... | computer science |
16,535 | Automatic Argumentative-Zoning Using Word2vec | cs.CL | In comparison with document summarization on the articles from social media
and newswire, argumentative zoning (AZ) is an important task in scientific
paper analysis. Traditional methodology to carry on this task relies on feature
engineering from different levels. In this paper, three models of generating
sentence vec... | computer science |
16,536 | Colors in Context: A Pragmatic Neural Model for Grounded Language
Understanding | cs.CL | We present a model of pragmatic referring expression interpretation in a
grounded communication task (identifying colors from descriptions) that draws
upon predictions from two recurrent neural network classifiers, a speaker and a
listener, unified by a recursive pragmatic reasoning framework. Experiments
show that thi... | computer science |
16,537 | BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character
Dataset | cs.CL | Bangla handwriting recognition is becoming a very important issue nowadays.
It is potentially a very important task specially for Bangla speaking
population of Bangladesh and West Bengal. By keeping that in our mind we are
introducing a comprehensive Bangla handwritten character dataset named
BanglaLekha-Isolated. This... | computer science |
16,538 | N-gram Language Modeling using Recurrent Neural Network Estimation | cs.CL | We investigate the effective memory depth of RNN models by using them for
$n$-gram language model (LM) smoothing.
Experiments on a small corpus (UPenn Treebank, one million words of training
data and 10k vocabulary) have found the LSTM cell with dropout to be the best
model for encoding the $n$-gram state when compar... | computer science |
16,539 | Joining Hands: Exploiting Monolingual Treebanks for Parsing of
Code-mixing Data | cs.CL | In this paper, we propose efficient and less resource-intensive strategies
for parsing of code-mixed data. These strategies are not constrained by
in-domain annotations, rather they leverage pre-existing monolingual annotated
resources for training. We show that these methods can produce significantly
better results as... | computer science |
16,540 | Reading Wikipedia to Answer Open-Domain Questions | cs.CL | This paper proposes to tackle open- domain question answering using Wikipedia
as the unique knowledge source: the answer to any factoid question is a text
span in a Wikipedia article. This task of machine reading at scale combines the
challenges of document retrieval (finding the relevant articles) with that of
machine... | computer science |
16,541 | One-Shot Neural Cross-Lingual Transfer for Paradigm Completion | cs.CL | We present a novel cross-lingual transfer method for paradigm completion, the
task of mapping a lemma to its inflected forms, using a neural encoder-decoder
model, the state of the art for the monolingual task. We use labeled data from
a high-resource language to increase performance on a low-resource language. In
expe... | computer science |
16,542 | Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems | cs.CL | This paper presents the Frames dataset (Frames is available at
http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues
with an average of 15 turns per dialogue. We developed this dataset to study
the role of memory in goal-oriented dialogue systems. Based on Frames, we
introduce a task called frame ... | computer science |
16,543 | Sentiment Analysis of Citations Using Word2vec | cs.CL | Citation sentiment analysis is an important task in scientific paper
analysis. Existing machine learning techniques for citation sentiment analysis
are focusing on labor-intensive feature engineering, which requires large
annotated corpus. As an automatic feature extraction tool, word2vec has been
successfully applied ... | computer science |
16,544 | Towards Building Large Scale Multimodal Domain-Aware Conversation
Systems | cs.CL | While multimodal conversation agents are gaining importance in several
domains such as retail, travel etc., deep learning research in this area has
been limited primarily due to the lack of availability of large-scale, open
chatlogs. To overcome this bottleneck, in this paper we introduce the task of
multimodal, domain... | computer science |
16,545 | Building a Neural Machine Translation System Using Only Synthetic
Parallel Data | cs.CL | Recent works have shown that synthetic parallel data automatically generated
by translation models can be effective for various neural machine translation
(NMT) issues. In this study, we build NMT systems using only synthetic parallel
data. As an efficient alternative to real parallel data, we also present a new
type o... | computer science |
16,546 | Word-Alignment-Based Segment-Level Machine Translation Evaluation using
Word Embeddings | cs.CL | One of the most important problems in machine translation (MT) evaluation is
to evaluate the similarity between translation hypotheses with different
surface forms from the reference, especially at the segment level. We propose
to use word embeddings to perform word alignment for segment-level MT
evaluation. We perform... | computer science |
16,547 | Syntax Aware LSTM Model for Chinese Semantic Role Labeling | cs.CL | As for semantic role labeling (SRL) task, when it comes to utilizing parsing
information, both traditional methods and recent recurrent neural network (RNN)
based methods use the feature engineering way. In this paper, we propose Syntax
Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies
according ... | computer science |
16,548 | Combining Lexical and Syntactic Features for Detecting Content-dense
Texts in News | cs.CL | Content-dense news report important factual information about an event in
direct, succinct manner. Information seeking applications such as information
extraction, question answering and summarization normally assume all text they
deal with is content-dense. Here we empirically test this assumption on news
articles fro... | computer science |
16,549 | A Transition-Based Directed Acyclic Graph Parser for UCCA | cs.CL | We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structur... | computer science |
16,550 | Neural Lattice-to-Sequence Models for Uncertain Inputs | cs.CL | The input to a neural sequence-to-sequence model is often determined by an
up-stream system, e.g. a word segmenter, part of speech tagger, or speech
recognizer. These up-stream models are potentially error-prone. Representing
inputs through word lattices allows making this uncertainty explicit by
capturing alternative ... | computer science |
16,551 | Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency
and Compositionality for Increased Model Capacity and Performance With No
Computational Overhead | cs.CL | Increasing the capacity of recurrent neural networks (RNN) usually involves
augmenting the size of the hidden layer, resulting in a significant increase of
computational cost. An alternative is the recurrent neural tensor network
(RNTN), which increases capacity by employing distinct hidden layer weights for
each vocab... | computer science |
16,552 | Voice Conversion from Unaligned Corpora using Variational Autoencoding
Wasserstein Generative Adversarial Networks | cs.CL | Building a voice conversion (VC) system from non-parallel speech corpora is
challenging but highly valuable in real application scenarios. In most
situations, the source and the target speakers do not repeat the same texts or
they may even speak different languages. In this case, one possible, although
indirect, soluti... | computer science |
16,553 | Interpretation of Semantic Tweet Representations | cs.CL | Research in analysis of microblogging platforms is experiencing a renewed
surge with a large number of works applying representation learning models for
applications like sentiment analysis, semantic textual similarity computation,
hashtag prediction, etc. Although the performance of the representation
learning models ... | computer science |
16,554 | Japanese Sentiment Classification using a Tree-Structured Long
Short-Term Memory with Attention | cs.CL | Previous approaches to training syntax-based sentiment classification models
required phrase-level annotated corpora, which are not readily available in
many languages other than English. Thus, we propose the use of tree-structured
Long Short-Term Memory with an attention mechanism that pays attention to each
subtree o... | computer science |
16,555 | Character-based Joint Segmentation and POS Tagging for Chinese using
Bidirectional RNN-CRF | cs.CL | We present a character-based model for joint segmentation and POS tagging for
Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is
adapted and applied with novel vector representations of Chinese characters
that capture rich contextual information and lower-than-character level
features. The ... | computer science |
16,556 | CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection
Methods for Semantic Textual Similarity | cs.CL | We present our submitted systems for Semantic Textual Similarity (STS) Track
4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must
estimate their semantic similarity by a score between 0 and 5. In our
submission, we use syntax-based, dictionary-based, context-based, and MT-based
methods. We als... | computer science |
16,557 | Linear Ensembles of Word Embedding Models | cs.CL | This paper explores linear methods for combining several word embedding
models into an ensemble. We construct the combined models using an iterative
method based on either ordinary least squares regression or the solution to the
orthogonal Procrustes problem.
We evaluate the proposed approaches on Estonian---a morpho... | computer science |
16,558 | Automatic Measurement of Pre-aspiration | cs.CL | Pre-aspiration is defined as the period of glottal friction occurring in
sequences of vocalic/consonantal sonorants and phonetically voiceless
obstruents. We propose two machine learning methods for automatic measurement
of pre-aspiration duration: a feedforward neural network, which works at the
frame level; and a str... | computer science |
16,559 | MRA - Proof of Concept of a Multilingual Report Annotator Web
Application | cs.CL | MRA (Multilingual Report Annotator) is a web application that translates
Radiology text and annotates it with RadLex terms. Its goal is to explore the
solution of translating non-English Radiology reports as a way to solve the
problem of most of the Text Mining tools being developed for English. In this
brief paper we ... | computer science |
16,560 | Neural Question Generation from Text: A Preliminary Study | cs.CL | Automatic question generation aims to generate questions from a text passage
where the generated questions can be answered by certain sub-spans of the given
passage. Traditional methods mainly use rigid heuristic rules to transform a
sentence into related questions. In this work, we propose to apply the neural
encoder-... | computer science |
16,561 | The Interplay of Semantics and Morphology in Word Embeddings | cs.CL | We explore the ability of word embeddings to capture both semantic and
morphological similarity, as affected by the different types of linguistic
properties (surface form, lemma, morphological tag) used to compose the
representation of each word. We train several models, where each uses a
different subset of these prop... | computer science |
16,562 | Conversation Modeling on Reddit using a Graph-Structured LSTM | cs.CL | This paper presents a novel approach for modeling threaded discussions on
social media using a graph-structured bidirectional LSTM which represents both
hierarchical and temporal conversation structure. In experiments with a task of
predicting popularity of comments in Reddit discussions, the proposed model
outperforms... | computer science |
16,563 | Adposition and Case Supersenses v2: Guidelines for English | cs.CL | This document describes in detail an inventory of 50 semantic labels designed
to characterize the use of adpositions and case markers at a somewhat coarse
level of granularity. Version 2 is a revision of the supersense inventory
proposed for English by Schneider et al. (2015, 2016) and documented in
PrepWiki (hencefort... | computer science |
16,564 | The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural
Semantic Parsing | cs.CL | We evaluate a semantic parser based on a character-based sequence-to-sequence
model in the context of the SemEval-2017 shared task on semantic parsing for
AMRs. With data augmentation, super characters, and POS-tagging we gain major
improvements in performance compared to a baseline character-level model.
Although we i... | computer science |
16,565 | EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for
kEyphrase ClassificaTION | cs.CL | This paper describes our approach to the SemEval 2017 Task 10: "Extracting
Keyphrases and Relations from Scientific Publications", specifically to Subtask
(B): "Classification of identified keyphrases". We explored three different
deep learning approaches: a character-level convolutional neural network (CNN),
a stacked... | computer science |
16,566 | Comparison of Global Algorithms in Word Sense Disambiguation | cs.CL | This article compares four probabilistic algorithms (global algorithms) for
Word Sense Disambiguation (WSD) in terms of the number of scorer calls (local
algo- rithm) and the F1 score as determined by a gold-standard scorer. Two
algorithms come from the state of the art, a Simulated Annealing Algorithm
(SAA) and a Gene... | computer science |
16,567 | Fostering User Engagement: Rhetorical Devices for Applause Generation
Learnt from TED Talks | cs.CL | One problem that every presenter faces when delivering a public discourse is
how to hold the listeners' attentions or to keep them involved. Therefore, many
studies in conversation analysis work on this issue and suggest qualitatively
con-structions that can effectively lead to audience's applause. To investigate
these... | computer science |
16,568 | A Trolling Hierarchy in Social Media and A Conditional Random Field For
Trolling Detection | cs.CL | An-ever increasing number of social media websites, electronic newspapers and
Internet forums allow visitors to leave comments for others to read and
interact. This exchange is not free from participants with malicious
intentions, which do not contribute with the written conversation. Among
different communities users ... | computer science |
16,569 | On the Linearity of Semantic Change: Investigating Meaning Variation via
Dynamic Graph Models | cs.CL | We consider two graph models of semantic change. The first is a time-series
model that relates embedding vectors from one time period to embedding vectors
of previous time periods. In the second, we construct one graph for each word:
nodes in this graph correspond to time points and edge weights to the
similarity of th... | computer science |
16,570 | Prosody: The Rhythms and Melodies of Speech | cs.CL | The present contribution is a tutorial on selected aspects of prosody, the
rhythms and melodies of speech, based on a course of the same name at the
Summer School on Contemporary Phonetics and Phonology at Tongji University,
Shanghai, China in July 2016. The tutorial is not intended as an introduction
to experimental m... | computer science |
16,571 | Improving Implicit Semantic Role Labeling by Predicting Semantic Frame
Arguments | cs.CL | Implicit semantic role labeling (iSRL) is the task of predicting the semantic
roles of a predicate that do not appear as explicit arguments, but rather
regard common sense knowledge or are mentioned earlier in the discourse. We
introduce an approach to iSRL based on a predictive recurrent neural semantic
frame model (P... | computer science |
16,572 | Entity Linking for Queries by Searching Wikipedia Sentences | cs.CL | We present a simple yet effective approach for linking entities in queries.
The key idea is to search sentences similar to a query from Wikipedia articles
and directly use the human-annotated entities in the similar sentences as
candidate entities for the query. Then, we employ a rich set of features, such
as link-prob... | computer science |
16,573 | Character-Word LSTM Language Models | cs.CL | We present a Character-Word Long Short-Term Memory Language Model which both
reduces the perplexity with respect to a baseline word-level language model and
reduces the number of parameters of the model. Character information can reveal
structural (dis)similarities between words and can even be used when a word is
out-... | computer science |
16,574 | Automatic Classification of the Complexity of Nonfiction Texts in
Portuguese for Early School Years | cs.CL | Recent research shows that most Brazilian students have serious problems
regarding their reading skills. The full development of this skill is key for
the academic and professional future of every citizen. Tools for classifying
the complexity of reading materials for children aim to improve the quality of
the model of ... | computer science |
16,575 | Automatic semantic role labeling on non-revised syntactic trees of
journalistic texts | cs.CL | Semantic Role Labeling (SRL) is a Natural Language Processing task that
enables the detection of events described in sentences and the participants of
these events. For Brazilian Portuguese (BP), there are two studies recently
concluded that perform SRL in journalistic texts. [1] obtained F1-measure
scores of 79.6, usi... | computer science |
16,576 | Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation | cs.CL | For extended periods of time, sequence generation models rely on beam search
algorithm to generate output sequence. However, the correctness of beam search
degrades when the a model is over-confident about a suboptimal prediction. In
this paper, we propose to perform minimum Bayes-risk (MBR) decoding for some
extra ste... | computer science |
16,577 | Automatic Keyword Extraction for Text Summarization: A Survey | cs.CL | In recent times, data is growing rapidly in every domain such as news, social
media, banking, education, etc. Due to the excessiveness of data, there is a
need of automatic summarizer which will be capable to summarize the data
especially textual data in original document without losing any critical
purposes. Text summ... | computer science |
16,578 | Unfolding and Shrinking Neural Machine Translation Ensembles | cs.CL | Ensembling is a well-known technique in neural machine translation (NMT) to
improve system performance. Instead of a single neural net, multiple neural
nets with the same topology are trained separately, and the decoder generates
predictions by averaging over the individual models. Ensembling often improves
the quality... | computer science |
16,579 | What do Neural Machine Translation Models Learn about Morphology? | cs.CL | Neural machine translation (MT) models obtain state-of-the-art performance
while maintaining a simple, end-to-end architecture. However, little is known
about what these models learn about source and target languages during the
training process. In this work, we analyze the representations learned by
neural MT models a... | computer science |
16,580 | ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with
Multilingual Relational Knowledge | cs.CL | This paper describes Luminoso's participation in SemEval 2017 Task 2,
"Multilingual and Cross-lingual Semantic Word Similarity", with a system based
on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses
on general knowledge that relates the meanings of words and phrases. Our
submission to SemE... | computer science |
16,581 | Trainable Referring Expression Generation using Overspecification
Preferences | cs.CL | Referring expression generation (REG) models that use speaker-dependent
information require a considerable amount of training data produced by every
individual speaker, or may otherwise perform poorly. In this work we present a
simple REG experiment that allows the use of larger training data sets by
grouping speakers ... | computer science |
16,582 | Incremental Skip-gram Model with Negative Sampling | cs.CL | This paper explores an incremental training strategy for the skip-gram model
with negative sampling (SGNS) from both empirical and theoretical perspectives.
Existing methods of neural word embeddings, including SGNS, are multi-pass
algorithms and thus cannot perform incremental model update. To address this
problem, we... | computer science |
16,583 | Mobile Keyboard Input Decoding with Finite-State Transducers | cs.CL | We propose a finite-state transducer (FST) representation for the models used
to decode keyboard inputs on mobile devices. Drawing from learnings from the
field of speech recognition, we describe a decoding framework that can satisfy
the strict memory and latency constraints of keyboard input. We extend this
framework ... | computer science |
16,584 | A Neural Model for User Geolocation and Lexical Dialectology | cs.CL | We propose a simple yet effective text- based user geolocation model based on
a neural network with one hidden layer, which achieves state of the art
performance over three Twitter benchmark geolocation datasets, in addition to
producing word and phrase embeddings in the hidden layer that we show to be
useful for detec... | computer science |
16,585 | Cross-lingual and cross-domain discourse segmentation of entire
documents | cs.CL | Discourse segmentation is a crucial step in building end-to-end discourse
parsers. However, discourse segmenters only exist for a few languages and
domains. Typically they only detect intra-sentential segment boundaries,
assuming gold standard sentence and token segmentation, and relying on
high-quality syntactic parse... | computer science |
16,586 | Learning Joint Multilingual Sentence Representations with Neural Machine
Translation | cs.CL | In this paper, we use the framework of neural machine translation to learn
joint sentence representations across six very different languages. Our aim is
that a representation which is independent of the language, is likely to
capture the underlying semantics. We define a new cross-lingual similarity
measure, compare u... | computer science |
16,587 | Room for improvement in automatic image description: an error analysis | cs.CL | In recent years we have seen rapid and significant progress in automatic
image description but what are the open problems in this area? Most work has
been evaluated using text-based similarity metrics, which only indicate that
there have been improvements, without explaining what has improved. In this
paper, we present... | computer science |
16,588 | Identity and Granularity of Events in Text | cs.CL | In this paper we describe a method to detect event descrip- tions in
different news articles and to model the semantics of events and their
components using RDF representations. We compare these descriptions to solve a
cross-document event coreference task. Our com- ponent approach to event
semantics defines identity a... | computer science |
16,589 | An entity-driven recursive neural network model for chinese discourse
coherence modeling | cs.CL | Chinese discourse coherence modeling remains a challenge taskin Natural
Language Processing field.Existing approaches mostlyfocus on the need for
feature engineering, whichadoptthe sophisticated features to capture the logic
or syntactic or semantic relationships acrosssentences within a text.In this
paper, we present ... | computer science |
16,590 | Exploiting Cross-Sentence Context for Neural Machine Translation | cs.CL | In translation, considering the document as a whole can help to resolve
ambiguities and inconsistencies. In this paper, we propose a cross-sentence
context-aware approach and investigate the influence of historical contextual
information on the performance of neural machine translation (NMT). First, this
history is sum... | computer science |
16,591 | Get To The Point: Summarization with Pointer-Generator Networks | cs.CL | Neural sequence-to-sequence models have provided a viable new approach for
abstractive text summarization (meaning they are not restricted to simply
selecting and rearranging passages from the original text). However, these
models have two shortcomings: they are liable to reproduce factual details
inaccurately, and the... | computer science |
16,592 | How Robust Are Character-Based Word Embeddings in Tagging and MT Against
Wrod Scramlbing or Randdm Nouse? | cs.CL | This paper investigates the robustness of NLP against perturbed word forms.
While neural approaches can achieve (almost) human-like accuracy for certain
tasks and conditions, they often are sensitive to small changes in the input
such as non-canonical input (e.g., typos). Yet both stability and robustness
are desired p... | computer science |
16,593 | Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of
Concept Maps | cs.CL | Concept maps can be used to concisely represent important information and
bring structure into large document collections. Therefore, we study a variant
of multi-document summarization that produces summaries in the form of concept
maps. However, suitable evaluation datasets for this task are currently
missing. To clos... | computer science |
16,594 | Cardinal Virtues: Extracting Relation Cardinalities from Text | cs.CL | Information extraction (IE) from text has largely focused on relations
between individual entities, such as who has won which award. However, some
facts are never fully mentioned, and no IE method has perfect recall. Thus, it
is beneficial to also tap contents about the cardinalities of these relations,
for example, ho... | computer science |
16,595 | Neural Machine Translation Model with a Large Vocabulary Selected by
Branching Entropy | cs.CL | Neural machine translation (NMT), a new approach to machine translation, has
achieved promising results comparable to those of traditional approaches such
as statistical machine translation (SMT). Despite its recent success, NMT
cannot handle a larger vocabulary because the training complexity and decoding
complexity p... | computer science |
16,596 | Translation of Patent Sentences with a Large Vocabulary of Technical
Terms Using Neural Machine Translation | cs.CL | Neural machine translation (NMT), a new approach to machine translation, has
achieved promising results comparable to those of traditional approaches such
as statistical machine translation (SMT). Despite its recent success, NMT
cannot handle a larger vocabulary because training complexity and decoding
complexity propo... | computer science |
16,597 | Neural Extractive Summarization with Side Information | cs.CL | Most extractive summarization methods focus on the main body of the document
from which sentences need to be extracted. However, the gist of the document
may lie in side information, such as the title and image captions which are
often available for newswire articles. We propose to explore side information
in the conte... | computer science |
16,598 | Cross-lingual Abstract Meaning Representation Parsing | cs.CL | Abstract Meaning Representation (AMR) annotation efforts have mostly focused
on English. In order to train parsers on other languages, we propose a method
based on annotation projection, which involves exploiting annotations in a
source language and a parallel corpus of the source language and a target
language. Using ... | computer science |
16,599 | Distributional Modeling on a Diet: One-shot Word Learning from Text Only | cs.CL | We test whether distributional models can do one-shot learning of
definitional properties from text only. Using Bayesian models, we find that
first learning overarching structure in the known data, regularities in textual
contexts and in properties, helps one-shot learning, and that individual
context items can be high... | computer science |
16,600 | Neural Paraphrase Identification of Questions with Noisy Pretraining | cs.CL | We present a solution to the problem of paraphrase identification of
questions. We focus on a recent dataset of question pairs annotated with binary
paraphrase labels and show that a variant of the decomposable attention model
(Parikh et al., 2016) results in accurate performance on this task, while being
far simpler t... | computer science |
16,601 | MUSE: Modularizing Unsupervised Sense Embeddings | cs.CL | This paper proposes to address the word sense ambiguity issue in an
unsupervised manner, where word sense representations are learned along a word
sense selection mechanism given contexts. Prior work about learning multi-sense
embeddings suffered from either ambiguity of different-level embeddings or
inefficient sense ... | computer science |
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