Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
16,102 | Incorporating Relation Paths in Neural Relation Extraction | cs.CL | Distantly supervised relation extraction has been widely used to find novel
relational facts from plain text. To predict the relation between a pair of two
target entities, existing methods solely rely on those direct sentences
containing both entities. In fact, there are also many sentences containing
only one of the ... | computer science |
16,103 | Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser | cs.CL | We introduce two first-order graph-based dependency parsers achieving a new
state of the art. The first is a consensus parser built from an ensemble of
independently trained greedy LSTM transition-based parsers with different
random initializations. We cast this approach as minimum Bayes risk decoding
(under the Hammin... | computer science |
16,104 | A Character-level Convolutional Neural Network for Distinguishing
Similar Languages and Dialects | cs.CL | Discriminating between closely-related language varieties is considered a
challenging and important task. This paper describes our submission to the DSL
2016 shared-task, which included two sub-tasks: one on discriminating similar
languages and one on identifying Arabic dialects. We developed a
character-level neural n... | computer science |
16,105 | An Investigation of Recurrent Neural Architectures for Drug Name
Recognition | cs.CL | Drug name recognition (DNR) is an essential step in the Pharmacovigilance
(PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical
texts and classify them into predefined categories. State-of-the-art DNR
approaches heavily rely on hand crafted features and domain specific resources
which are diffic... | computer science |
16,106 | The distribution of information content in English sentences | cs.CL | Sentence is a basic linguistic unit, however, little is known about how
information content is distributed across different positions of a sentence.
Based on authentic language data of English, the present study calculated the
entropy and other entropy-related statistics for different sentence positions.
The statistics... | computer science |
16,107 | Large-Scale Machine Translation between Arabic and Hebrew: Available
Corpora and Initial Results | cs.CL | Machine translation between Arabic and Hebrew has so far been limited by a
lack of parallel corpora, despite the political and cultural importance of this
language pair. Previous work relied on manually-crafted grammars or pivoting
via English, both of which are unsatisfactory for building a scalable and
accurate MT sy... | computer science |
16,108 | Lattice-Based Recurrent Neural Network Encoders for Neural Machine
Translation | cs.CL | Neural machine translation (NMT) heavily relies on word-level modelling to
learn semantic representations of input sentences. However, for languages
without natural word delimiters (e.g., Chinese) where input sentences have to
be tokenized first, conventional NMT is confronted with two issues: 1) it is
difficult to fin... | computer science |
16,109 | A Factorized Model for Transitive Verbs in Compositional Distributional
Semantics | cs.CL | We present a factorized compositional distributional semantics model for the
representation of transitive verb constructions. Our model first produces
(subject, verb) and (verb, object) vector representations based on the
similarity of the nouns in the construction to each of the nouns in the
vocabulary and the tendenc... | computer science |
16,110 | S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet
Entity Linking | cs.CL | Non-linear models recently receive a lot of attention as people are starting
to discover the power of statistical and embedding features. However,
tree-based models are seldom studied in the context of structured learning
despite their recent success on various classification and ranking tasks. In
this paper, we propos... | computer science |
16,111 | Toward Socially-Infused Information Extraction: Embedding Authors,
Mentions, and Entities | cs.CL | Entity linking is the task of identifying mentions of entities in text, and
linking them to entries in a knowledge base. This task is especially difficult
in microblogs, as there is little additional text to provide disambiguating
context; rather, authors rely on an implicit common ground of shared knowledge
with their... | computer science |
16,112 | Creating Causal Embeddings for Question Answering with Minimal
Supervision | cs.CL | A common model for question answering (QA) is that a good answer is one that
is closely related to the question, where relatedness is often determined using
general-purpose lexical models such as word embeddings. We argue that a better
approach is to look for answers that are related to the question in a relevant
way, ... | computer science |
16,113 | An Unsupervised Probability Model for Speech-to-Translation Alignment of
Low-Resource Languages | cs.CL | For many low-resource languages, spoken language resources are more likely to
be annotated with translations than with transcriptions. Translated speech data
is potentially valuable for documenting endangered languages or for training
speech translation systems. A first step towards making use of such data would
be to ... | computer science |
16,114 | Aligning Coordinated Text Streams through Burst Information Network
Construction and Decipherment | cs.CL | Aligning coordinated text streams from multiple sources and multiple
languages has opened many new research venues on cross-lingual knowledge
discovery. In this paper we aim to advance state-of-the-art by: (1). extending
coarse-grained topic-level knowledge mining to fine-grained information units
such as entities and ... | computer science |
16,115 | The Effects of Data Size and Frequency Range on Distributional Semantic
Models | cs.CL | This paper investigates the effects of data size and frequency range on
distributional semantic models. We compare the performance of a number of
representative models for several test settings over data of varying sizes, and
over test items of various frequency. Our results show that neural
network-based models underp... | computer science |
16,116 | emoji2vec: Learning Emoji Representations from their Description | cs.CL | Many current natural language processing applications for social media rely
on representation learning and utilize pre-trained word embeddings. There
currently exist several publicly-available, pre-trained sets of word
embeddings, but they contain few or no emoji representations even as emoji
usage in social media has ... | computer science |
16,117 | OC16-CE80: A Chinese-English Mixlingual Database and A Speech
Recognition Baseline | cs.CL | We present the OC16-CE80 Chinese-English mixlingual speech database which was
released as a main resource for training, development and test for the
Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on
O-COCOSDA 2016. This database consists of 80 hours of speech signals recorded
from more than 1,400... | computer science |
16,118 | Deep Reinforcement Learning for Mention-Ranking Coreference Models | cs.CL | Coreference resolution systems are typically trained with heuristic loss
functions that require careful tuning. In this paper we instead apply
reinforcement learning to directly optimize a neural mention-ranking model for
coreference evaluation metrics. We experiment with two approaches: the
REINFORCE policy gradient a... | computer science |
16,119 | Character Sequence Models for ColorfulWords | cs.CL | We present a neural network architecture to predict a point in color space
from the sequence of characters in the color's name. Using large scale
color--name pairs obtained from an online color design forum, we evaluate our
model on a "color Turing test" and find that, given a name, the colors
predicted by our model ar... | computer science |
16,120 | Effective Combination of Language and Vision Through Model Composition
and the R-CCA Method | cs.CL | We address the problem of integrating textual and visual information in
vector space models for word meaning representation. We first present the
Residual CCA (R-CCA) method, that complements the standard CCA method by
representing, for each modality, the difference between the original signal and
the signal projected ... | computer science |
16,121 | Equation Parsing: Mapping Sentences to Grounded Equations | cs.CL | Identifying mathematical relations expressed in text is essential to
understanding a broad range of natural language text from election reports, to
financial news, to sport commentaries to mathematical word problems. This paper
focuses on identifying and understanding mathematical relations described
within a single se... | computer science |
16,122 | Byte-based Language Identification with Deep Convolutional Networks | cs.CL | We report on our system for the shared task on discriminating between similar
languages (DSL 2016). The system uses only byte representations in a deep
residual network (ResNet). The system, named ResIdent, is trained only on the
data released with the task (closed training). We obtain 84.88% accuracy on
subtask A, 68.... | computer science |
16,123 | Psychologically Motivated Text Mining | cs.CL | Natural language processing techniques are increasingly applied to identify
social trends and predict behavior based on large text collections. Existing
methods typically rely on surface lexical and syntactic information. Yet,
research in psychology shows that patterns of human conceptualisation, such as
metaphorical f... | computer science |
16,124 | Empirical Evaluation of RNN Architectures on Sentence Classification
Task | cs.CL | Recurrent Neural Networks have achieved state-of-the-art results for many
problems in NLP and two most popular RNN architectures are Tail Model and
Pooling Model. In this paper, a hybrid architecture is proposed and we present
the first empirical study using LSTMs to compare performance of the three RNN
structures on s... | computer science |
16,125 | Learning Sentence Representation with Guidance of Human Attention | cs.CL | Recently, much progress has been made in learning general-purpose sentence
representations that can be used across domains. However, most of the existing
models typically treat each word in a sentence equally. In contrast, extensive
studies have proven that human read sentences efficiently by making a sequence
of fixat... | computer science |
16,126 | Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent
Neural Networks | cs.CL | This work focuses on the rapid development of linguistic annotation tools for
resource-poor languages. We experiment several cross-lingual annotation
projection methods using Recurrent Neural Networks (RNN) models. The
distinctive feature of our approach is that our multilingual word
representation requires only a para... | computer science |
16,127 | Controlling Output Length in Neural Encoder-Decoders | cs.CL | Neural encoder-decoder models have shown great success in many sequence
generation tasks. However, previous work has not investigated situations in
which we would like to control the length of encoder-decoder outputs. This
capability is crucial for applications such as text summarization, in which we
have to generate c... | computer science |
16,128 | Referential Uncertainty and Word Learning in High-dimensional,
Continuous Meaning Spaces | cs.CL | This paper discusses lexicon word learning in high-dimensional meaning spaces
from the viewpoint of referential uncertainty. We investigate various
state-of-the-art Machine Learning algorithms and discuss the impact of scaling,
representation and meaning space structure. We demonstrate that current Machine
Learning tec... | computer science |
16,129 | Modeling Language Change in Historical Corpora: The Case of Portuguese | cs.CL | This paper presents a number of experiments to model changes in a historical
Portuguese corpus composed of literary texts for the purpose of temporal text
classification. Algorithms were trained to classify texts with respect to their
publication date taking into account lexical variation represented as word
n-grams, a... | computer science |
16,130 | Discriminating Similar Languages: Evaluations and Explorations | cs.CL | We present an analysis of the performance of machine learning classifiers on
discriminating between similar languages and language varieties. We carried out
a number of experiments using the results of the two editions of the
Discriminating between Similar Languages (DSL) shared task. We investigate the
progress made b... | computer science |
16,131 | Vocabulary Selection Strategies for Neural Machine Translation | cs.CL | Classical translation models constrain the space of possible outputs by
selecting a subset of translation rules based on the input sentence. Recent
work on improving the efficiency of neural translation models adopted a similar
strategy by restricting the output vocabulary to a subset of likely candidates
given the sou... | computer science |
16,132 | Sentence Segmentation in Narrative Transcripts from Neuropsychological
Tests using Recurrent Convolutional Neural Networks | cs.CL | Automated discourse analysis tools based on Natural Language Processing (NLP)
aiming at the diagnosis of language-impairing dementias generally extract
several textual metrics of narrative transcripts. However, the absence of
sentence boundary segmentation in the transcripts prevents the direct
application of NLP metho... | computer science |
16,133 | Very Deep Convolutional Neural Networks for Robust Speech Recognition | cs.CL | This paper describes the extension and optimization of our previous work on
very deep convolutional neural networks (CNNs) for effective recognition of
noisy speech in the Aurora 4 task. The appropriate number of convolutional
layers, the sizes of the filters, pooling operations and input feature maps are
all modified:... | computer science |
16,134 | Syntactic Structures and Code Parameters | cs.CL | We assign binary and ternary error-correcting codes to the data of syntactic
structures of world languages and we study the distribution of code points in
the space of code parameters. We show that, while most codes populate the lower
region approximating a superposition of Thomae functions, there is a
substantial pres... | computer science |
16,135 | Nonsymbolic Text Representation | cs.CL | We introduce the first generic text representation model that is completely
nonsymbolic, i.e., it does not require the availability of a segmentation or
tokenization method that attempts to identify words or other symbolic units in
text. This applies to training the parameters of the model on a training corpus
as well ... | computer science |
16,136 | Multimodal Semantic Simulations of Linguistically Underspecified Motion
Events | cs.CL | In this paper, we describe a system for generating three-dimensional visual
simulations of natural language motion expressions. We use a rich formal model
of events and their participants to generate simulations that satisfy the
minimal constraints entailed by the associated utterance, relying on semantic
knowledge of ... | computer science |
16,137 | Orthographic Syllable as basic unit for SMT between Related Languages | cs.CL | We explore the use of the orthographic syllable, a variable-length
consonant-vowel sequence, as a basic unit of translation between related
languages which use abugida or alphabetic scripts. We show that orthographic
syllable level translation significantly outperforms models trained over other
basic units (word, morph... | computer science |
16,138 | Grounding the Lexical Sets of Causative-Inchoative Verbs with Word
Embedding | cs.CL | Lexical sets contain the words filling the argument positions of a verb in
one of its senses. They can be grounded empirically through their automatic
extraction from corpora. The purpose of this paper is demonstrating that their
vector representation based on word embedding provides insights onto many
linguistic pheno... | computer science |
16,139 | Chinese Event Extraction Using DeepNeural Network with Word Embedding | cs.CL | A lot of prior work on event extraction has exploited a variety of features
to represent events. Such methods have several drawbacks: 1) the features are
often specific for a particular domain and do not generalize well; 2) the
features are derived from various linguistic analyses and are error-prone; and
3) some featu... | computer science |
16,140 | A Computational Approach to Automatic Prediction of Drunk Texting | cs.CL | Alcohol abuse may lead to unsociable behavior such as crime, drunk driving,
or privacy leaks. We introduce automatic drunk-texting prediction as the task
of identifying whether a text was written when under the influence of alcohol.
We experiment with tweets labeled using hashtags as distant supervision. Our
classifier... | computer science |
16,141 | Are Word Embedding-based Features Useful for Sarcasm Detection? | cs.CL | This paper makes a simple increment to state-of-the-art in sarcasm detection
research. Existing approaches are unable to capture subtle forms of context
incongruity which lies at the heart of sarcasm. We explore if prior work can be
enhanced using semantic similarity/discordance between word embeddings. We
augment word... | computer science |
16,142 | Is Neural Machine Translation Ready for Deployment? A Case Study on 30
Translation Directions | cs.CL | In this paper we provide the largest published comparison of translation
quality for phrase-based SMT and neural machine translation across 30
translation directions. For ten directions we also include hierarchical
phrase-based MT. Experiments are performed for the recently published United
Nations Parallel Corpus v1.0... | computer science |
16,143 | Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or
Lexical Resources? | cs.CL | This paper presents an approach combining lexico-semantic resources and
distributed representations of words applied to the evaluation in machine
translation (MT). This study is made through the enrichment of a well-known MT
evaluation metric: METEOR. This metric enables an approximate match (synonymy
or morphological ... | computer science |
16,144 | A tentative model for dimensionless phoneme distance from binary
distinctive features | cs.CL | This work proposes a tentative model for the calculation of dimensionless
distances between phonemes; sounds are described with binary distinctive
features and distances show linear consistency in terms of such features. The
model can be used as a scoring function for local and global pairwise alignment
of phoneme sequ... | computer science |
16,145 | VoxML: A Visualization Modeling Language | cs.CL | We present the specification for a modeling language, VoxML, which encodes
semantic knowledge of real-world objects represented as three-dimensional
models, and of events and attributes related to and enacted over these objects.
VoxML is intended to overcome the limitations of existing 3D visual markup
languages by all... | computer science |
16,146 | Neural Structural Correspondence Learning for Domain Adaptation | cs.CL | Domain adaptation, adapting models from domains rich in labeled training data
to domains poor in such data, is a fundamental NLP challenge. We introduce a
neural network model that marries together ideas from two prominent strands of
research on domain adaptation through representation learning: structural
corresponden... | computer science |
16,147 | Generating Simulations of Motion Events from Verbal Descriptions | cs.CL | In this paper, we describe a computational model for motion events in natural
language that maps from linguistic expressions, through a dynamic event
interpretation, into three-dimensional temporal simulations in a model.
Starting with the model from (Pustejovsky and Moszkowicz, 2011), we analyze
motion events using te... | computer science |
16,148 | Neural-based Noise Filtering from Word Embeddings | cs.CL | Word embeddings have been demonstrated to benefit NLP tasks impressively.
Yet, there is room for improvement in the vector representations, because
current word embeddings typically contain unnecessary information, i.e., noise.
We propose two novel models to improve word embeddings by unsupervised
learning, in order to... | computer science |
16,149 | Toward Automatic Understanding of the Function of Affective Language in
Support Groups | cs.CL | Understanding expressions of emotions in support forums has considerable
value and NLP methods are key to automating this. Many approaches
understandably use subjective categories which are more fine-grained than a
straightforward polarity-based spectrum. However, the definition of such
categories is non-trivial and, i... | computer science |
16,150 | Scalable Machine Translation in Memory Constrained Environments | cs.CL | Machine translation is the discipline concerned with developing automated
tools for translating from one human language to another. Statistical machine
translation (SMT) is the dominant paradigm in this field. In SMT, translations
are generated by means of statistical models whose parameters are learned from
bilingual ... | computer science |
16,151 | There's No Comparison: Reference-less Evaluation Metrics in Grammatical
Error Correction | cs.CL | Current methods for automatically evaluating grammatical error correction
(GEC) systems rely on gold-standard references. However, these methods suffer
from penalizing grammatical edits that are correct but not in the gold
standard. We show that reference-less grammaticality metrics correlate very
strongly with human j... | computer science |
16,152 | Challenges of Computational Processing of Code-Switching | cs.CL | This paper addresses challenges of Natural Language Processing (NLP) on
non-canonical multilingual data in which two or more languages are mixed. It
refers to code-switching which has become more popular in our daily life and
therefore obtains an increasing amount of attention from the research
community. We report our... | computer science |
16,153 | A Semantic Analyzer for the Comprehension of the Spontaneous Arabic
Speech | cs.CL | This work is part of a large research project entitled "Or\'eodule" aimed at
developing tools for automatic speech recognition, translation, and synthesis
for Arabic language. Our attention has mainly been focused on an attempt to
improve the probabilistic model on which our semantic decoder is based. To
achieve this g... | computer science |
16,154 | Computational linking theory | cs.CL | A linking theory explains how verbs' semantic arguments are mapped to their
syntactic arguments---the inverse of the Semantic Role Labeling task from the
shallow semantic parsing literature. In this paper, we develop the
Computational Linking Theory framework as a method for implementing and testing
linking theories pr... | computer science |
16,155 | Enabling Medical Translation for Low-Resource Languages | cs.CL | We present research towards bridging the language gap between migrant workers
in Qatar and medical staff. In particular, we present the first steps towards
the development of a real-world Hindi-English machine translation system for
doctor-patient communication. As this is a low-resource language pair,
especially for s... | computer science |
16,156 | A Dynamic Window Neural Network for CCG Supertagging | cs.CL | Combinatory Category Grammar (CCG) supertagging is a task to assign lexical
categories to each word in a sentence. Almost all previous methods use fixed
context window sizes as input features. However, it is obvious that different
tags usually rely on different context window sizes. These motivate us to build
a superta... | computer science |
16,157 | Modelling Sentence Pairs with Tree-structured Attentive Encoder | cs.CL | We describe an attentive encoder that combines tree-structured recursive
neural networks and sequential recurrent neural networks for modelling sentence
pairs. Since existing attentive models exert attention on the sequential
structure, we propose a way to incorporate attention into the tree topology.
Specially, given ... | computer science |
16,158 | Very Deep Convolutional Networks for End-to-End Speech Recognition | cs.CL | Sequence-to-sequence models have shown success in end-to-end speech
recognition. However these models have only used shallow acoustic encoder
networks. In our work, we successively train very deep convolutional networks
to add more expressive power and better generalization for end-to-end ASR
models. We apply network-i... | computer science |
16,159 | Neural Paraphrase Generation with Stacked Residual LSTM Networks | cs.CL | In this paper, we propose a novel neural approach for paraphrase generation.
Conventional para- phrase generation methods either leverage hand-written rules
and thesauri-based alignments, or use statistical machine learning principles.
To the best of our knowledge, this work is the first to explore deep learning
models... | computer science |
16,160 | Leveraging Recurrent Neural Networks for Multimodal Recognition of
Social Norm Violation in Dialog | cs.CL | Social norms are shared rules that govern and facilitate social interaction.
Violating such social norms via teasing and insults may serve to upend power
imbalances or, on the contrary reinforce solidarity and rapport in
conversation, rapport which is highly situated and context-dependent. In this
work, we investigate ... | computer science |
16,161 | An Empirical Exploration of Skip Connections for Sequential Tagging | cs.CL | In this paper, we empirically explore the effects of various kinds of skip
connections in stacked bidirectional LSTMs for sequential tagging. We
investigate three kinds of skip connections connecting to LSTM cells: (a) skip
connections to the gates, (b) skip connections to the internal states and (c)
skip connections t... | computer science |
16,162 | Toward a new instances of NELL | cs.CL | We are developing the method to start new instances of NELL in various
languages and develop then NELL multilingualism. We base our method on our
experience on NELL Portuguese and NELL French. This reports explain our method
and develops some research perspectives. | computer science |
16,163 | GMM-Free Flat Start Sequence-Discriminative DNN Training | cs.CL | Recently, attempts have been made to remove Gaussian mixture models (GMM)
from the training process of deep neural network-based hidden Markov models
(HMM/DNN). For the GMM-free training of a HMM/DNN hybrid we have to solve two
problems, namely the initial alignment of the frame-level state labels and the
creation of c... | computer science |
16,164 | Keystroke dynamics as signal for shallow syntactic parsing | cs.CL | Keystroke dynamics have been extensively used in psycholinguistic and writing
research to gain insights into cognitive processing. But do keystroke logs
contain actual signal that can be used to learn better natural language
processing models?
We postulate that keystroke dynamics contain information about syntactic
s... | computer science |
16,165 | Survey on the Use of Typological Information in Natural Language
Processing | cs.CL | In recent years linguistic typology, which classifies the world's languages
according to their functional and structural properties, has been widely used
to support multilingual NLP. While the growing importance of typological
information in supporting multilingual tasks has been recognised, no systematic
survey of exi... | computer science |
16,166 | A Paradigm for Situated and Goal-Driven Language Learning | cs.CL | A distinguishing property of human intelligence is the ability to flexibly
use language in order to communicate complex ideas with other humans in a
variety of contexts. Research in natural language dialogue should focus on
designing communicative agents which can integrate themselves into these
contexts and productive... | computer science |
16,167 | Language Models with Pre-Trained (GloVe) Word Embeddings | cs.CL | In this work we implement a training of a Language Model (LM), using
Recurrent Neural Network (RNN) and GloVe word embeddings, introduced by
Pennigton et al. in [1]. The implementation is following the general idea of
training RNNs for LM tasks presented in [2], but is rather using Gated
Recurrent Unit (GRU) [3] for a ... | computer science |
16,168 | SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban
Neighbourhoods | cs.CL | In this paper, we introduce the task of targeted aspect-based sentiment
analysis. The goal is to extract fine-grained information with respect to
entities mentioned in user comments. This work extends both aspect-based
sentiment analysis that assumes a single entity per document and targeted
sentiment analysis that ass... | computer science |
16,169 | Question Generation from a Knowledge Base with Web Exploration | cs.CL | Question generation from a knowledge base (KB) is the task of generating
questions related to the domain of the input KB. We propose a system for
generating fluent and natural questions from a KB, which significantly reduces
the human effort by leveraging massive web resources. In more detail, a seed
question set is fi... | computer science |
16,170 | A Neural Network for Coordination Boundary Prediction | cs.CL | We propose a neural-network based model for coordination boundary prediction.
The network is designed to incorporate two signals: the similarity between
conjuncts and the observation that replacing the whole coordination phrase with
a conjunct tends to produce a coherent sentences. The modeling makes use of
several LST... | computer science |
16,171 | Fast, Scalable Phrase-Based SMT Decoding | cs.CL | The utilization of statistical machine translation (SMT) has grown enormously
over the last decade, many using open-source software developed by the NLP
community. As commercial use has increased, there is need for software that is
optimized for commercial requirements, in particular, fast phrase-based
decoding and mor... | computer science |
16,172 | Translation Quality Estimation using Recurrent Neural Network | cs.CL | This paper describes our submission to the shared task on word/phrase level
Quality Estimation (QE) in the First Conference on Statistical Machine
Translation (WMT16). The objective of the shared task was to predict if the
given word/phrase is a correct/incorrect (OK/BAD) translation in the given
sentence. In this pape... | computer science |
16,173 | Interactive Attention for Neural Machine Translation | cs.CL | Conventional attention-based Neural Machine Translation (NMT) conducts
dynamic alignment in generating the target sentence. By repeatedly reading the
representation of source sentence, which keeps fixed after generated by the
encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced
state-of-the-art... | computer science |
16,174 | Neural Machine Translation Advised by Statistical Machine Translation | cs.CL | Neural Machine Translation (NMT) is a new approach to machine translation
that has made great progress in recent years. However, recent studies show that
NMT generally produces fluent but inadequate translations (Tu et al. 2016b; Tu
et al. 2016a; He et al. 2016; Tu et al. 2017). This is in contrast to
conventional Stat... | computer science |
16,175 | Pre-Translation for Neural Machine Translation | cs.CL | Recently, the development of neural machine translation (NMT) has
significantly improved the translation quality of automatic machine
translation. While most sentences are more accurate and fluent than
translations by statistical machine translation (SMT)-based systems, in some
cases, the NMT system produces translatio... | computer science |
16,176 | Achieving Human Parity in Conversational Speech Recognition | cs.CL | Conversational speech recognition has served as a flagship speech recognition
task since the release of the Switchboard corpus in the 1990s. In this paper,
we measure the human error rate on the widely used NIST 2000 test set, and find
that our latest automated system has reached human parity. The error rate of
profess... | computer science |
16,177 | End-to-end attention-based distant speech recognition with Highway LSTM | cs.CL | End-to-end attention-based models have been shown to be competitive
alternatives to conventional DNN-HMM models in the Speech Recognition Systems.
In this paper, we extend existing end-to-end attention-based models that can be
applied for Distant Speech Recognition (DSR) task. Specifically, we propose an
end-to-end att... | computer science |
16,178 | Personalized Machine Translation: Preserving Original Author Traits | cs.CL | The language that we produce reflects our personality, and various personal
and demographic characteristics can be detected in natural language texts. We
focus on one particular personal trait of the author, gender, and study how it
is manifested in original texts and in translations. We show that author's
gender has a... | computer science |
16,179 | Addressing Community Question Answering in English and Arabic | cs.CL | This paper studies the impact of different types of features applied to
learning to re-rank questions in community Question Answering. We tested our
models on two datasets released in SemEval-2016 Task 3 on "Community Question
Answering". Task 3 targeted real-life Web fora both in English and Arabic. Our
models include... | computer science |
16,180 | SYSTRAN's Pure Neural Machine Translation Systems | cs.CL | Since the first online demonstration of Neural Machine Translation (NMT) by
LISA, NMT development has recently moved from laboratory to production systems
as demonstrated by several entities announcing roll-out of NMT engines to
replace their existing technologies. NMT systems have a large number of
training configurat... | computer science |
16,181 | Vietnamese Named Entity Recognition using Token Regular Expressions and
Bidirectional Inference | cs.CL | This paper describes an efficient approach to improve the accuracy of a named
entity recognition system for Vietnamese. The approach combines regular
expressions over tokens and a bidirectional inference method in a sequence
labelling model. The proposed method achieves an overall $F_1$ score of 89.66%
on a test set of... | computer science |
16,182 | Bidirectional LSTM-CRF for Clinical Concept Extraction | cs.CL | Extraction of concepts present in patient clinical records is an essential
step in clinical research. The 2010 i2b2/VA Workshop on Natural Language
Processing Challenges for clinical records presented concept extraction (CE)
task, with aim to identify concepts (such as treatments, tests, problems) and
classify them int... | computer science |
16,183 | Chinese Restaurant Process for cognate clustering: A threshold free
approach | cs.CL | In this paper, we introduce a threshold free approach, motivated from Chinese
Restaurant Process, for the purpose of cognate clustering. We show that our
approach yields similar results to a linguistically motivated cognate
clustering system known as LexStat. Our Chinese Restaurant Process system is
fast and does not r... | computer science |
16,184 | A Theme-Rewriting Approach for Generating Algebra Word Problems | cs.CL | Texts present coherent stories that have a particular theme or overall
setting, for example science fiction or western. In this paper, we present a
text generation method called {\it rewriting} that edits existing
human-authored narratives to change their theme without changing the underlying
story. We apply the approa... | computer science |
16,185 | Cross-Lingual Syntactic Transfer with Limited Resources | cs.CL | We describe a simple but effective method for cross-lingual syntactic
transfer of dependency parsers, in the scenario where a large amount of
translation data is not available. The method makes use of three steps: 1) a
method for deriving cross-lingual word clusters, which can then be used in a
multilingual parser; 2) ... | computer science |
16,186 | Lexicon Integrated CNN Models with Attention for Sentiment Analysis | cs.CL | With the advent of word embeddings, lexicons are no longer fully utilized for
sentiment analysis although they still provide important features in the
traditional setting. This paper introduces a novel approach to sentiment
analysis that integrates lexicon embeddings and an attention mechanism into
Convolutional Neural... | computer science |
16,187 | Authorship Attribution Based on Life-Like Network Automata | cs.CL | The authorship attribution is a problem of considerable practical and
technical interest. Several methods have been designed to infer the authorship
of disputed documents in multiple contexts. While traditional statistical
methods based solely on word counts and related measurements have provided a
simple, yet effectiv... | computer science |
16,188 | Learning variable length units for SMT between related languages via
Byte Pair Encoding | cs.CL | We explore the use of segments learnt using Byte Pair Encoding (referred to
as BPE units) as basic units for statistical machine translation between
related languages and compare it with orthographic syllables, which are
currently the best performing basic units for this translation task. BPE
identifies the most freque... | computer science |
16,189 | Lexicons and Minimum Risk Training for Neural Machine Translation:
NAIST-CMU at WAT2016 | cs.CL | This year, the Nara Institute of Science and Technology (NAIST)/Carnegie
Mellon University (CMU) submission to the Japanese-English translation track of
the 2016 Workshop on Asian Translation was based on attentional neural machine
translation (NMT) models. In addition to the standard NMT model, we make a
number of imp... | computer science |
16,190 | Neural Machine Translation with Characters and Hierarchical Encoding | cs.CL | Most existing Neural Machine Translation models use groups of characters or
whole words as their unit of input and output. We propose a model with a
hierarchical char2word encoder, that takes individual characters both as input
and output. We first argue that this hierarchical representation of the
character encoder re... | computer science |
16,191 | An Approach to Speed-up the Word Sense Disambiguation Procedure through
Sense Filtering | cs.CL | In this paper, we are going to focus on speed up of the Word Sense
Disambiguation procedure by filtering the relevant senses of an ambiguous word
through Part-of-Speech Tagging. First, this proposed approach performs the
Part-of-Speech Tagging operation before the disambiguation procedure using
Bigram approximation. As... | computer science |
16,192 | Iterative Refinement for Machine Translation | cs.CL | Existing machine translation decoding algorithms generate translations in a
strictly monotonic fashion and never revisit previous decisions. As a result,
earlier mistakes cannot be corrected at a later stage. In this paper, we
present a translation scheme that starts from an initial guess and then makes
iterative impro... | computer science |
16,193 | Automatic Identification of Sarcasm Target: An Introductory Approach | cs.CL | Past work in computational sarcasm deals primarily with sarcasm detection. In
this paper, we introduce a novel, related problem: sarcasm target
identification i.e., extracting the target of ridicule in a sarcastic
sentence). We present an introductory approach for sarcasm target
identification. Our approach employs two... | computer science |
16,194 | Two are Better than One: An Ensemble of Retrieval- and Generation-Based
Dialog Systems | cs.CL | Open-domain human-computer conversation has attracted much attention in the
field of NLP. Contrary to rule- or template-based domain-specific dialog
systems, open-domain conversation usually requires data-driven approaches,
which can be roughly divided into two categories: retrieval-based and
generation-based systems. ... | computer science |
16,195 | Bridging Neural Machine Translation and Bilingual Dictionaries | cs.CL | Neural Machine Translation (NMT) has become the new state-of-the-art in
several language pairs. However, it remains a challenging problem how to
integrate NMT with a bilingual dictionary which mainly contains words rarely or
never seen in the bilingual training data. In this paper, we propose two
methods to bridge NMT ... | computer science |
16,196 | Statistical Machine Translation for Indian Languages: Mission Hindi | cs.CL | This paper discusses Centre for Development of Advanced Computing Mumbai's
(CDACM) submission to the NLP Tools Contest on Statistical Machine Translation
in Indian Languages (ILSMT) 2014 (collocated with ICON 2014). The objective of
the contest was to explore the effectiveness of Statistical Machine Translation
(SMT) f... | computer science |
16,197 | Reordering rules for English-Hindi SMT | cs.CL | Reordering is a preprocessing stage for Statistical Machine Translation (SMT)
system where the words of the source sentence are reordered as per the syntax
of the target language. We are proposing a rich set of rules for better
reordering. The idea is to facilitate the training process by better alignments
and parallel... | computer science |
16,198 | UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation | cs.CL | This document briefly describes the systems submitted by the Center for
Robust Speech Systems (CRSS) from The University of Texas at Dallas (UTD) to
the 2016 National Institute of Standards and Technology (NIST) Speaker
Recognition Evaluation (SRE). We developed several UBM and DNN i-Vector based
speaker recognition sy... | computer science |
16,199 | EmojiNet: Building a Machine Readable Sense Inventory for Emoji | cs.CL | Emoji are a contemporary and extremely popular way to enhance electronic
communication. Without rigid semantics attached to them, emoji symbols take on
different meanings based on the context of a message. Thus, like the word sense
disambiguation task in natural language processing, machines also need to
disambiguate t... | computer science |
16,200 | Still not there? Comparing Traditional Sequence-to-Sequence Models to
Encoder-Decoder Neural Networks on Monotone String Translation Tasks | cs.CL | We analyze the performance of encoder-decoder neural models and compare them
with well-known established methods. The latter represent different classes of
traditional approaches that are applied to the monotone sequence-to-sequence
tasks OCR post-correction, spelling correction, grapheme-to-phoneme conversion,
and lem... | computer science |
16,201 | How Document Pre-processing affects Keyphrase Extraction Performance | cs.CL | The SemEval-2010 benchmark dataset has brought renewed attention to the task
of automatic keyphrase extraction. This dataset is made up of scientific
articles that were automatically converted from PDF format to plain text and
thus require careful preprocessing so that irrevelant spans of text do not
negatively affect ... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.