Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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16,702 | A Generative Model of a Pronunciation Lexicon for Hindi | cs.CL | Voice browser applications in Text-to- Speech (TTS) and Automatic Speech
Recognition (ASR) systems crucially depend on a pronunciation lexicon. The
present paper describes the model of pronunciation lexicon of Hindi developed
to automatically generate the output forms of Hindi at two levels, the
<phoneme> and the <PS> ... | computer science |
16,703 | Generating Memorable Mnemonic Encodings of Numbers | cs.CL | The major system is a mnemonic system that can be used to memorize sequences
of numbers. In this work, we present a method to automatically generate
sentences that encode a given number. We propose several encoding models and
compare the most promising ones in a password memorability study. The results
of the study sho... | computer science |
16,704 | Density Estimation for Geolocation via Convolutional Mixture Density
Network | cs.CL | Nowadays, geographic information related to Twitter is crucially important
for fine-grained applications. However, the amount of geographic information
avail- able on Twitter is low, which makes the pursuit of many applications
challenging. Under such circumstances, estimating the location of a tweet is an
important go... | computer science |
16,705 | Reinforced Mnemonic Reader for Machine Comprehension | cs.CL | In this paper, we introduce the Reinforced Mnemonic Reader for machine
comprehension (MC) task, which aims to answer a query about a given context
document. We propose several novel mechanisms that address critical problems in
MC that are not adequately solved by previous works, such as enhancing the
capacity of encode... | computer science |
16,706 | Ontology-Aware Token Embeddings for Prepositional Phrase Attachment | cs.CL | Type-level word embeddings use the same set of parameters to represent all
instances of a word regardless of its context, ignoring the inherent lexical
ambiguity in language. Instead, we embed semantic concepts (or synsets) as
defined in WordNet and represent a word token in a particular context by
estimating a distrib... | computer science |
16,707 | Convolutional Sequence to Sequence Learning | cs.CL | The prevalent approach to sequence to sequence learning maps an input
sequence to a variable length output sequence via recurrent neural networks. We
introduce an architecture based entirely on convolutional neural networks.
Compared to recurrent models, computations over all elements can be fully
parallelized during t... | computer science |
16,708 | Does William Shakespeare REALLY Write Hamlet? Knowledge Representation
Learning with Confidence | cs.CL | Knowledge graphs (KGs), which could provide essential relational information
between entities, have been widely utilized in various knowledge-driven
applications. Since the overall human knowledge is innumerable that still grows
explosively and changes frequently, knowledge construction and update
inevitably involve au... | computer science |
16,709 | A Systematic Review of Hindi Prosody | cs.CL | Prosody describes both form and function of a sentence using the
suprasegmental features of speech. Prosody phenomena are explored in the domain
of higher phonological constituents such as word, phonological phrase and
intonational phrase. The study of prosody at the word level is called word
prosody and above word lev... | computer science |
16,710 | Drug-drug Interaction Extraction via Recurrent Neural Network with
Multiple Attention Layers | cs.CL | Drug-drug interaction (DDI) is a vital information when physicians and
pharmacists intend to co-administer two or more drugs. Thus, several DDI
databases are constructed to avoid mistakenly combined use. In recent years,
automatically extracting DDIs from biomedical text has drawn researchers'
attention. However, the e... | computer science |
16,711 | Logical Parsing from Natural Language Based on a Neural Translation
Model | cs.CL | Semantic parsing has emerged as a significant and powerful paradigm for
natural language interface and question answering systems. Traditional methods
of building a semantic parser rely on high-quality lexicons, hand-crafted
grammars and linguistic features which are limited by applied domain or
representation. In this... | computer science |
16,712 | TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for
Reading Comprehension | cs.CL | We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for... | computer science |
16,713 | A Survey of Deep Learning Methods for Relation Extraction | cs.CL | Relation Extraction is an important sub-task of Information Extraction which
has the potential of employing deep learning (DL) models with the creation of
large datasets using distant supervision. In this review, we compare the
contributions and pitfalls of the various DL models that have been used for the
task, to hel... | computer science |
16,714 | Analysing Data-To-Text Generation Benchmarks | cs.CL | Recently, several data-sets associating data to text have been created to
train data-to-text surface realisers. It is unclear however to what extent the
surface realisation task exercised by these data-sets is linguistically
challenging. Do these data-sets provide enough variety to encourage the
development of generic,... | computer science |
16,715 | A Minimal Span-Based Neural Constituency Parser | cs.CL | In this work, we present a minimal neural model for constituency parsing
based on independent scoring of labels and spans. We show that this model is
not only compatible with classical dynamic programming techniques, but also
admits a novel greedy top-down inference algorithm based on recursive
partitioning of the inpu... | computer science |
16,716 | Learning with Noise: Enhance Distantly Supervised Relation Extraction
with Dynamic Transition Matrix | cs.CL | Distant supervision significantly reduces human efforts in building training
data for many classification tasks. While promising, this technique often
introduces noise to the generated training data, which can severely affect the
model performance. In this paper, we take a deep look at the application of
distant superv... | computer science |
16,717 | Content-based Approach for Vietnamese Spam SMS Filtering | cs.CL | Short Message Service (SMS) spam is a serious problem in Vietnam because of
the availability of very cheap pre-paid SMS packages. There are some systems to
detect and filter spam messages for English, most of which use machine learning
techniques to analyze the content of messages and classify them. For
Vietnamese, the... | computer science |
16,718 | Building a Semantic Role Labelling System for Vietnamese | cs.CL | Semantic role labelling (SRL) is a task in natural language processing which
detects and classifies the semantic arguments associated with the predicates of
a sentence. It is an important step towards understanding the meaning of a
natural language. There exists SRL systems for well-studied languages like
English, Chin... | computer science |
16,719 | End-to-end Recurrent Neural Network Models for Vietnamese Named Entity
Recognition: Word-level vs. Character-level | cs.CL | This paper demonstrates end-to-end neural network architectures for
Vietnamese named entity recognition. Our best model is a combination of
bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
input, which achieves an F1... | computer science |
16,720 | Dynamic Compositional Neural Networks over Tree Structure | cs.CL | Tree-structured neural networks have proven to be effective in learning
semantic representations by exploiting syntactic information. In spite of their
success, most existing models suffer from the underfitting problem: they
recursively use the same shared compositional function throughout the whole
compositional proce... | computer science |
16,721 | Sketching Word Vectors Through Hashing | cs.CL | We propose a new fast word embedding technique using hash functions. The
method is a derandomization of a new type of random projections: By
disregarding the classic constraint used in designing random projections (i.e.,
preserving pairwise distances in a particular normed space), our solution
exploits extremely sparse... | computer science |
16,722 | A Deep Reinforced Model for Abstractive Summarization | cs.CL | Attentional, RNN-based encoder-decoder models for abstractive summarization
have achieved good performance on short input and output sequences. For longer
documents and summaries however these models often include repetitive and
incoherent phrases. We introduce a neural network model with a novel
intra-attention that a... | computer science |
16,723 | Reducing Bias in Production Speech Models | cs.CL | Replacing hand-engineered pipelines with end-to-end deep learning systems has
enabled strong results in applications like speech and object recognition.
However, the causality and latency constraints of production systems put
end-to-end speech models back into the underfitting regime and expose biases in
the model that... | computer science |
16,724 | Evaluating vector-space models of analogy | cs.CL | Vector-space representations provide geometric tools for reasoning about the
similarity of a set of objects and their relationships. Recent machine learning
methods for deriving vector-space embeddings of words (e.g., word2vec) have
achieved considerable success in natural language processing. These vector
spaces have ... | computer science |
16,725 | Arc-swift: A Novel Transition System for Dependency Parsing | cs.CL | Transition-based dependency parsers often need sequences of local shift and
reduce operations to produce certain attachments. Correct individual decisions
hence require global information about the sentence context and mistakes cause
error propagation. This paper proposes a novel transition system, arc-swift,
that enab... | computer science |
16,726 | Learning Semantic Correspondences in Technical Documentation | cs.CL | We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representatio... | computer science |
16,727 | Annotating and Modeling Empathy in Spoken Conversations | cs.CL | Empathy, as defined in behavioral sciences, expresses the ability of human
beings to recognize, understand and react to emotions, attitudes and beliefs of
others. The lack of an operational definition of empathy makes it difficult to
measure it. In this paper, we address two related problems in automatic
affective beha... | computer science |
16,728 | Joint Modeling of Content and Discourse Relations in Dialogues | cs.CL | We present a joint modeling approach to identify salient discussion points in
spoken meetings as well as to label the discourse relations between speaker
turns. A variation of our model is also discussed when discourse relations are
treated as latent variables. Experimental results on two popular meeting
corpora show t... | computer science |
16,729 | Winning on the Merits: The Joint Effects of Content and Style on Debate
Outcomes | cs.CL | Debate and deliberation play essential roles in politics and government, but
most models presume that debates are won mainly via superior style or agenda
control. Ideally, however, debates would be won on the merits, as a function of
which side has the stronger arguments. We propose a predictive model of debate
that es... | computer science |
16,730 | Representation learning of drug and disease terms for drug repositioning | cs.CL | Drug repositioning (DR) refers to identification of novel indications for the
approved drugs. The requirement of huge investment of time as well as money and
risk of failure in clinical trials have led to surge in interest in drug
repositioning. DR exploits two major aspects associated with drugs and
diseases: existenc... | computer science |
16,731 | Key-Value Retrieval Networks for Task-Oriented Dialogue | cs.CL | Neural task-oriented dialogue systems often struggle to smoothly interface
with a knowledge base. In this work, we seek to address this problem by
proposing a new neural dialogue agent that is able to effectively sustain
grounded, multi-domain discourse through a novel key-value retrieval mechanism.
The model is end-to... | computer science |
16,732 | A Biomedical Information Extraction Primer for NLP Researchers | cs.CL | Biomedical Information Extraction is an exciting field at the crossroads of
Natural Language Processing, Biology and Medicine. It encompasses a variety of
different tasks that require application of state-of-the-art NLP techniques,
such as NER and Relation Extraction. This paper provides an overview of the
problems in ... | computer science |
16,733 | Subregular Complexity and Deep Learning | cs.CL | This paper argues that the judicial use of formal language theory and
grammatical inference are invaluable tools in understanding how deep neural
networks can and cannot represent and learn long-term dependencies in temporal
sequences. Learning experiments were conducted with two types of Recurrent
Neural Networks (RNN... | computer science |
16,734 | A Novel Neural Network Model for Joint POS Tagging and Graph-based
Dependency Parsing | cs.CL | We present a novel neural network model that learns POS tagging and
graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to
learn feature representations shared for both POS tagging and dependency
parsing tasks, thus handling the feature-engineering problem. Our extensive
experiments, on 19 langua... | computer science |
16,735 | Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model | cs.CL | Frame stacking is broadly applied in end-to-end neural network training like
connectionist temporal classification (CTC), and it leads to more accurate
models and faster decoding. However, it is not well-suited to conventional
neural network based on context-dependent state acoustic model, if the decoder
is unchanged. ... | computer science |
16,736 | Unlabeled Data for Morphological Generation With Character-Based
Sequence-to-Sequence Models | cs.CL | We present a semi-supervised way of training a character-based
encoder-decoder recurrent neural network for morphological reinflection, the
task of generating one inflected word form from another. This is achieved by
using unlabeled tokens or random strings as training data for an autoencoding
task, adapting a network ... | computer science |
16,737 | Political Footprints: Political Discourse Analysis using Pre-Trained
Word Vectors | cs.CL | In this paper, we discuss how machine learning could be used to produce a
systematic and more objective political discourse analysis. Political
footprints are vector space models (VSMs) applied to political discourse. Each
of their vectors represents a word, and is produced by training the English
lexicon on large text... | computer science |
16,738 | Decoding Sentiment from Distributed Representations of Sentences | cs.CL | Distributed representations of sentences have been developed recently to
represent their meaning as real-valued vectors. However, it is not clear how
much information such representations retain about the polarity of sentences.
To study this question, we decode sentiment from unsupervised sentence
representations learn... | computer science |
16,739 | Information Density as a Factor for Variation in the Embedding of
Relative Clauses | cs.CL | In German, relative clauses can be positioned in-situ or extraposed. A
potential factor for the variation might be information density. In this study,
this hypothesis is tested with a corpus of 17th century German funeral sermons.
For each referent in the relative clauses and their matrix clauses, the
attention state w... | computer science |
16,740 | Universal Dependencies Parsing for Colloquial Singaporean English | cs.CL | Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies sc... | computer science |
16,741 | ParlAI: A Dialog Research Software Platform | cs.CL | We introduce ParlAI (pronounced "par-lay"), an open-source software platform
for dialog research implemented in Python, available at http://parl.ai. Its
goal is to provide a unified framework for sharing, training and testing of
dialog models, integration of Amazon Mechanical Turk for data collection, human
evaluation,... | computer science |
16,742 | A Lightweight Regression Method to Infer Psycholinguistic Properties for
Brazilian Portuguese | cs.CL | Psycholinguistic properties of words have been used in various approaches to
Natural Language Processing tasks, such as text simplification and readability
assessment. Most of these properties are subjective, involving costly and
time-consuming surveys to be gathered. Recent approaches use the limited
datasets of psych... | computer science |
16,743 | Spelling Correction as a Foreign Language | cs.CL | In this paper, we reformulated the spell correction problem as a machine
translation task under the encoder-decoder framework. This reformulation
enabled us to use a single model for solving the problem that is traditionally
formulated as learning a language model and an error model. This model employs
multi-layer recu... | computer science |
16,744 | Recurrent Additive Networks | cs.CL | We introduce recurrent additive networks (RANs), a new gated RNN which is
distinguished by the use of purely additive latent state updates. At every time
step, the new state is computed as a gated component-wise sum of the input and
the previous state, without any of the non-linearities commonly used in RNN
transition ... | computer science |
16,745 | W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis | cs.CL | With the increase of online customer opinions in specialised websites and
social networks, the necessity of automatic systems to help to organise and
classify customer reviews by domain-specific aspect/categories and sentiment
polarity is more important than ever. Supervised approaches to Aspect Based
Sentiment Analysi... | computer science |
16,746 | Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech
Recognition | cs.CL | With the evolution of neural network based methods, automatic speech
recognition (ASR) field has been advanced to a level where building an
application with speech interface is a reality. In spite of these advances,
building a real-time speech recogniser faces several problems such as low
recognition accuracy, domain c... | computer science |
16,747 | Latent Human Traits in the Language of Social Media: An Open-Vocabulary
Approach | cs.CL | Over the past century, personality theory and research has successfully
identified core sets of characteristics that consistently describe and explain
fundamental differences in the way people think, feel and behave. Such
characteristics were derived through theory, dictionary analyses, and survey
research using explic... | computer science |
16,748 | Local Monotonic Attention Mechanism for End-to-End Speech and Language
Processing | cs.CL | Recently, encoder-decoder neural networks have shown impressive performance
on many sequence-related tasks. The architecture commonly uses an attentional
mechanism which allows the model to learn alignments between the source and the
target sequence. Most attentional mechanisms used today is based on a global
attention... | computer science |
16,749 | Question-Answering with Grammatically-Interpretable Representations | cs.CL | We introduce an architecture, the Tensor Product Recurrent Network (TPRN). In
our application of TPRN, internal representations learned by end-to-end
optimization in a deep neural network performing a textual question-answering
(QA) task can be interpreted using basic concepts from linguistic theory. No
performance pen... | computer science |
16,750 | Deep Investigation of Cross-Language Plagiarism Detection Methods | cs.CL | This paper is a deep investigation of cross-language plagiarism detection
methods on a new recently introduced open dataset, which contains parallel and
comparable collections of documents with multiple characteristics (different
genres, languages and sizes of texts). We investigate cross-language plagiarism
detection ... | computer science |
16,751 | Parsing with CYK over Distributed Representations: "Classical" Syntactic
Parsing in the Novel Era of Neural Networks | cs.CL | Syntactic parsing is a key task in natural language processing which has been
dominated by symbolic, grammar-based syntactic parsers. Neural networks, with
their distributed representations, are challenging these methods.
In this paper, we want to show that existing parsing algorithms can cross the
border and be defi... | computer science |
16,752 | Joint PoS Tagging and Stemming for Agglutinative Languages | cs.CL | The number of word forms in agglutinative languages is theoretically infinite
and this variety in word forms introduces sparsity in many natural language
processing tasks. Part-of-speech tagging (PoS tagging) is one of these tasks
that often suffers from sparsity. In this paper, we present an unsupervised
Bayesian mode... | computer science |
16,753 | Deep Voice 2: Multi-Speaker Neural Text-to-Speech | cs.CL | We introduce a technique for augmenting neural text-to-speech (TTS) with
lowdimensional trainable speaker embeddings to generate different voices from a
single model. As a starting point, we show improvements over the two
state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and
Tacotron. We introduce ... | computer science |
16,754 | Max-Cosine Matching Based Neural Models for Recognizing Textual
Entailment | cs.CL | Recognizing textual entailment is a fundamental task in a variety of text
mining or natural language processing applications. This paper proposes a
simple neural model for RTE problem. It first matches each word in the
hypothesis with its most-similar word in the premise, producing an augmented
representation of the hy... | computer science |
16,755 | Jointly Learning Sentence Embeddings and Syntax with Unsupervised
Tree-LSTMs | cs.CL | We introduce a neural network that represents sentences by composing their
words according to induced binary parse trees. We use Tree-LSTM as our
composition function, applied along a tree structure found by a fully
differentiable natural language chart parser. Our model simultaneously
optimises both the composition fu... | computer science |
16,756 | Biomedical Event Trigger Identification Using Bidirectional Recurrent
Neural Network Based Models | cs.CL | Biomedical events describe complex interactions between various biomedical
entities. Event trigger is a word or a phrase which typically signifies the
occurrence of an event. Event trigger identification is an important first step
in all event extraction methods. However many of the current approaches either
rely on co... | computer science |
16,757 | Detecting and Explaining Crisis | cs.CL | Individuals on social media may reveal themselves to be in various states of
crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
from social media text automatically and accurately can have profound
consequences. However, detecting a general state of crisis without explaining
why has limited ... | computer science |
16,758 | Semi-Supervised Model Training for Unbounded Conversational Speech
Recognition | cs.CL | For conversational large-vocabulary continuous speech recognition (LVCSR)
tasks, up to about two thousand hours of audio is commonly used to train state
of the art models. Collection of labeled conversational audio however, is
prohibitively expensive, laborious and error-prone. Furthermore, academic
corpora like Fisher... | computer science |
16,759 | On the relation between dependency distance, crossing dependencies, and
parsing. Comment on "Dependency distance: a new perspective on syntactic
patterns in natural languages" by Haitao Liu et al | cs.CL | Liu et al. (2017) provide a comprehensive account of research on dependency
distance in human languages. While the article is a very rich and useful report
on this complex subject, here I will expand on a few specific issues where
research in computational linguistics (specifically natural language
processing) can info... | computer science |
16,760 | Understanding Abuse: A Typology of Abusive Language Detection Subtasks | cs.CL | As the body of research on abusive language detection and analysis grows,
there is a need for critical consideration of the relationships between
different subtasks that have been grouped under this label. Based on work on
hate speech, cyberbullying, and online abuse we propose a typology that
captures central similari... | computer science |
16,761 | Listen, Interact and Talk: Learning to Speak via Interaction | cs.CL | One of the long-term goals of artificial intelligence is to build an agent
that can communicate intelligently with human in natural language. Most
existing work on natural language learning relies heavily on training over a
pre-collected dataset with annotated labels, leading to an agent that
essentially captures the s... | computer science |
16,762 | Neural Semantic Parsing by Character-based Translation: Experiments with
Abstract Meaning Representations | cs.CL | We evaluate the character-level translation method for neural semantic
parsing on a large corpus of sentences annotated with Abstract Meaning
Representations (AMRs). Using a sequence-to-sequence model, and some trivial
preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1
(F-score on AMR-tripl... | computer science |
16,763 | Subject Specific Stream Classification Preprocessing Algorithm for
Twitter Data Stream | cs.CL | Micro-blogging service Twitter is a lucrative source for data mining
applications on global sentiment. But due to the omnifariousness of the
subjects mentioned in each data item; it is inefficient to run a data mining
algorithm on the raw data. This paper discusses an algorithm to accurately
classify the entire stream ... | computer science |
16,764 | Supervised Complementary Entity Recognition with Augmented Key-value
Pairs of Knowledge | cs.CL | Extracting opinion targets is an important task in sentiment analysis on
product reviews and complementary entities (products) are one important type of
opinion targets that may work together with the reviewed product. In this
paper, we address the problem of Complementary Entity Recognition (CER) as a
supervised seque... | computer science |
16,765 | Dynamics of core of language vocabulary | cs.CL | Studies of the overall structure of vocabulary and its dynamics became
possible due to creation of diachronic text corpora, especially Google Books
Ngram. This article discusses the question of core change rate and the degree
to which the core words cover the texts. Different periods of the last three
centuries and six... | computer science |
16,766 | An Automatic Contextual Analysis and Clustering Classifiers Ensemble
approach to Sentiment Analysis | cs.CL | Products reviews are one of the major resources to determine the public
sentiment. The existing literature on reviews sentiment analysis mainly
utilizes supervised paradigm, which needs labeled data to be trained on and
suffers from domain-dependency. This article addresses these issues by
describes a completely automa... | computer science |
16,767 | Who's to say what's funny? A computer using Language Models and Deep
Learning, That's Who! | cs.CL | Humor is a defining characteristic of human beings. Our goal is to develop
methods that automatically detect humorous statements and rank them on a
continuous scale. In this paper we report on results using a Language Model
approach, and outline our plans for using methods from Deep Learning. | computer science |
16,768 | On the "Calligraphy" of Books | cs.CL | Authorship attribution is a natural language processing task that has been
widely studied, often by considering small order statistics. In this paper, we
explore a complex network approach to assign the authorship of texts based on
their mesoscopic representation, in an attempt to capture the flow of the
narrative. Ind... | computer science |
16,769 | The Importance of Automatic Syntactic Features in Vietnamese Named
Entity Recognition | cs.CL | This paper presents a state-of-the-art system for Vietnamese Named Entity
Recognition (NER). By incorporating automatic syntactic features with word
embeddings as input for bidirectional Long Short-Term Memory (Bi-LSTM), our
system, although simpler than some deep learning architectures, achieves a much
better result f... | computer science |
16,770 | A Low Dimensionality Representation for Language Variety Identification | cs.CL | Language variety identification aims at labelling texts in a native language
(e.g. Spanish, Portuguese, English) with its specific variation (e.g.
Argentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work
we propose a low dimensionality representation (LDR) to address this task with
five different ... | computer science |
16,771 | Character Composition Model with Convolutional Neural Networks for
Dependency Parsing on Morphologically Rich Languages | cs.CL | We present a transition-based dependency parser that uses a convolutional
neural network to compose word representations from characters. The character
composition model shows great improvement over the word-lookup model,
especially for parsing agglutinative languages. These improvements are even
better than using pre-... | computer science |
16,772 | Does the Geometry of Word Embeddings Help Document Classification? A
Case Study on Persistent Homology Based Representations | cs.CL | We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space... | computer science |
16,773 | Analysis of the Effect of Dependency Information on Predicate-Argument
Structure Analysis and Zero Anaphora Resolution | cs.CL | This paper investigates and analyzes the effect of dependency information on
predicate-argument structure analysis (PASA) and zero anaphora resolution (ZAR)
for Japanese, and shows that a straightforward approach of PASA and ZAR works
effectively even if dependency information was not available. We constructed an
analy... | computer science |
16,774 | Learning When to Attend for Neural Machine Translation | cs.CL | In the past few years, attention mechanisms have become an indispensable
component of end-to-end neural machine translation models. However, previous
attention models always refer to some source words when predicting a target
word, which contradicts with the fact that some target words have no
corresponding source word... | computer science |
16,775 | Are distributional representations ready for the real world? Evaluating
word vectors for grounded perceptual meaning | cs.CL | Distributional word representation methods exploit word co-occurrences to
build compact vector encodings of words. While these representations enjoy
widespread use in modern natural language processing, it is unclear whether
they accurately encode all necessary facets of conceptual meaning. In this
paper, we evaluate h... | computer science |
16,776 | Semantic Refinement GRU-based Neural Language Generation for Spoken
Dialogue Systems | cs.CL | Natural language generation (NLG) plays a critical role in spoken dialogue
systems. This paper presents a new approach to NLG by using recurrent neural
networks (RNN), in which a gating mechanism is applied before RNN computation.
This allows the proposed model to generate appropriate sentences. The RNN-based
generator... | computer science |
16,777 | Natural Language Generation for Spoken Dialogue System using RNN
Encoder-Decoder Networks | cs.CL | Natural language generation (NLG) is a critical component in a spoken
dialogue system. This paper presents a Recurrent Neural Network based
Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to
select, aggregate semantic elements produced by an attention mechanism over the
input elements, and to... | computer science |
16,778 | Polish Read Speech Corpus for Speech Tools and Services | cs.CL | This paper describes the speech processing activities conducted at the Polish
consortium of the CLARIN project. The purpose of this segment of the project
was to develop specific tools that would allow for automatic and semi-automatic
processing of large quantities of acoustic speech data. The tools include the
followi... | computer science |
16,779 | Using of heterogeneous corpora for training of an ASR system | cs.CL | The paper summarizes the development of the LVCSR system built as a part of
the Pashto speech-translation system at the SCALE (Summer Camp for Applied
Language Exploration) 2015 workshop on "Speech-to-text-translation for
low-resource languages". The Pashto language was chosen as a good "proxy"
low-resource language, e... | computer science |
16,780 | Morph-fitting: Fine-Tuning Word Vector Spaces with Simple
Language-Specific Rules | cs.CL | Morphologically rich languages accentuate two properties of distributional
vector space models: 1) the difficulty of inducing accurate representations for
low-frequency word forms; and 2) insensitivity to distinct lexical relations
that have similar distributional signatures. These effects are detrimental for
language ... | computer science |
16,781 | NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation
Systems | cs.CL | In this paper, we present nmtpy, a flexible Python toolkit based on Theano
for training Neural Machine Translation and other neural sequence-to-sequence
architectures. nmtpy decouples the specification of a network from the training
and inference utilities to simplify the addition of a new architecture and
reduce the a... | computer science |
16,782 | Machine Assisted Analysis of Vowel Length Contrasts in Wolof | cs.CL | Growing digital archives and improving algorithms for automatic analysis of
text and speech create new research opportunities for fundamental research in
phonetics. Such empirical approaches allow statistical evaluation of a much
larger set of hypothesis about phonetic variation and its conditioning factors
(among them... | computer science |
16,783 | Function Assistant: A Tool for NL Querying of APIs | cs.CL | In this paper, we describe Function Assistant, a lightweight Python-based
toolkit for querying and exploring source code repositories using natural
language. The toolkit is designed to help end-users of a target API quickly
find information about functions through high-level natural language queries
and descriptions. F... | computer science |
16,784 | Morphological Embeddings for Named Entity Recognition in Morphologically
Rich Languages | cs.CL | In this work, we present new state-of-the-art results of 93.59,% and 79.59,%
for Turkish and Czech named entity recognition based on the model of (Lample et
al., 2016). We contribute by proposing several schemes for representing the
morphological analysis of a word in the context of named entity recognition. We
show th... | computer science |
16,785 | Attentive Convolutional Neural Network based Speech Emotion Recognition:
A Study on the Impact of Input Features, Signal Length, and Acted Speech | cs.CL | Speech emotion recognition is an important and challenging task in the realm
of human-computer interaction. Prior work proposed a variety of models and
feature sets for training a system. In this work, we conduct extensive
experiments using an attentive convolutional neural network with multi-view
learning objective fu... | computer science |
16,786 | Prosodic Event Recognition using Convolutional Neural Networks with
Context Information | cs.CL | This paper demonstrates the potential of convolutional neural networks (CNN)
for detecting and classifying prosodic events on words, specifically pitch
accents and phrase boundary tones, from frame-based acoustic features. Typical
approaches use not only feature representations of the word in question but
also its surr... | computer science |
16,787 | Concept Transfer Learning for Adaptive Language Understanding | cs.CL | Semantic transfer is an important problem of the language understanding (LU),
which is about how the recognition pattern of a semantic concept benefits other
associated concepts. In this paper, we propose a new semantic representation
based on combinatory concepts. Semantic slot is represented as a composition of
diffe... | computer science |
16,788 | CRNN: A Joint Neural Network for Redundancy Detection | cs.CL | This paper proposes a novel framework for detecting redundancy in supervised
sentence categorisation. Unlike traditional singleton neural network, our model
incorporates character-aware convolutional neural network (Char-CNN) with
character-aware recurrent neural network (Char-RNN) to form a convolutional
recurrent neu... | computer science |
16,789 | One-step and Two-step Classification for Abusive Language Detection on
Twitter | cs.CL | Automatic abusive language detection is a difficult but important task for
online social media. Our research explores a two-step approach of performing
classification on abusive language and then classifying into specific types and
compares it with one-step approach of doing one multi-class classification for
detecting... | computer science |
16,790 | Language Generation with Recurrent Generative Adversarial Networks
without Pre-training | cs.CL | Generative Adversarial Networks (GANs) have shown great promise recently in
image generation. Training GANs for language generation has proven to be more
difficult, because of the non-differentiable nature of generating text with
recurrent neural networks. Consequently, past work has either resorted to
pre-training wit... | computer science |
16,791 | Acquisition of Translation Lexicons for Historically Unwritten Languages
via Bridging Loanwords | cs.CL | With the advent of informal electronic communications such as social media,
colloquial languages that were historically unwritten are being written for the
first time in heavily code-switched environments. We present a method for
inducing portions of translation lexicons through the use of expert knowledge
in these set... | computer science |
16,792 | Text Summarization using Abstract Meaning Representation | cs.CL | With an ever increasing size of text present on the Internet, automatic
summary generation remains an important problem for natural language
understanding. In this work we explore a novel full-fledged pipeline for text
summarization with an intermediate step of Abstract Meaning Representation
(AMR). The pipeline propos... | computer science |
16,793 | A Frame Tracking Model for Memory-Enhanced Dialogue Systems | cs.CL | Recently, resources and tasks were proposed to go beyond state tracking in
dialogue systems. An example is the frame tracking task, which requires
recording multiple frames, one for each user goal set during the dialogue. This
allows a user, for instance, to compare items corresponding to different goals.
This paper pr... | computer science |
16,794 | A General-Purpose Tagger with Convolutional Neural Networks | cs.CL | We present a general-purpose tagger based on convolutional neural networks
(CNN), used for both composing word vectors and encoding context information.
The CNN tagger is robust across different tagging tasks: without task-specific
tuning of hyper-parameters, it achieves state-of-the-art results in
part-of-speech taggi... | computer science |
16,795 | Label-Dependencies Aware Recurrent Neural Networks | cs.CL | In the last few years, Recurrent Neural Networks (RNNs) have proved effective
on several NLP tasks. Despite such great success, their ability to model
\emph{sequence labeling} is still limited. This lead research toward solutions
where RNNs are combined with models which already proved effective in this
domain, such as... | computer science |
16,796 | Assessing the Linguistic Productivity of Unsupervised Deep Neural
Networks | cs.CL | Increasingly, cognitive scientists have demonstrated interest in applying
tools from deep learning. One use for deep learning is in language acquisition
where it is useful to know if a linguistic phenomenon can be learned through
domain-general means. To assess whether unsupervised deep learning is
appropriate, we firs... | computer science |
16,797 | Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext | cs.CL | We consider the problem of learning general-purpose, paraphrastic sentence
embeddings in the setting of Wieting et al. (2016b). We use neural machine
translation to generate sentential paraphrases via back-translation of
bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to
serve as training da... | computer science |
16,798 | Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic
Similarity | cs.CL | Semantic similarity measures are an important part in Natural Language
Processing tasks. However Semantic similarity measures built for general use do
not perform well within specific domains. Therefore in this study we introduce
a domain specific semantic similarity measure that was created by the
synergistic union of... | computer science |
16,799 | Question Answering and Question Generation as Dual Tasks | cs.CL | We study the problem of joint question answering (QA) and question generation
(QG) in this paper.
Our intuition is that QA and QG have intrinsic connections and these two
tasks could improve each other.
On one side, the QA model judges whether the generated question of a QG model
is relevant to the answer.
On the... | computer science |
16,800 | Macquarie University at BioASQ 5b -- Query-based Summarisation
Techniques for Selecting the Ideal Answers | cs.CL | Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B)
focused on the use of query-based extractive summarisation techniques for the
generation of the ideal answers. Four runs were submitted, with approaches
ranging from a trivial system that selected the first $n$ snippets, to the use
of deep le... | computer science |
16,801 | Insights into Analogy Completion from the Biomedical Domain | cs.CL | Analogy completion has been a popular task in recent years for evaluating the
semantic properties of word embeddings, but the standard methodology makes a
number of assumptions about analogies that do not always hold, either in recent
benchmark datasets or when expanding into other domains. Through an analysis of
analo... | computer science |
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