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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16,002 | UsingWord Embeddings for Query Translation for Hindi to English Cross
Language Information Retrieval | cs.CL | Cross-Language Information Retrieval (CLIR) has become an important problem
to solve in the recent years due to the growth of content in multiple languages
in the Web. One of the standard methods is to use query translation from source
to target language. In this paper, we propose an approach based on word
embeddings, ... | computer science |
16,003 | Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine
Translation | cs.CL | Out-of-vocabulary words account for a large proportion of errors in machine
translation systems, especially when the system is used on a different domain
than the one where it was trained. In order to alleviate the problem, we
propose to use a log-bilinear softmax-based model for vocabulary expansion,
such that given a... | computer science |
16,004 | Text authorship identified using the dynamics of word co-occurrence
networks | cs.CL | The identification of authorship in disputed documents still requires human
expertise, which is now unfeasible for many tasks owing to the large volumes of
text and authors in practical applications. In this study, we introduce a
methodology based on the dynamics of word co-occurrence networks representing
written text... | computer science |
16,005 | Boundary-based MWE segmentation with text partitioning | cs.CL | This work presents a fine-grained, text-chunking algorithm designed for the
task of multiword expressions (MWEs) segmentation. As a lexical class, MWEs
include a wide variety of idioms, whose automatic identification are a
necessity for the handling of colloquial language. This algorithm's core
novelty is its use of no... | computer science |
16,006 | Desiderata for Vector-Space Word Representations | cs.CL | A plethora of vector-space representations for words is currently available,
which is growing. These consist of fixed-length vectors containing real values,
which represent a word. The result is a representation upon which the power of
many conventional information processing and data mining techniques can be
brought t... | computer science |
16,007 | Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken
Language Understanding | cs.CL | This paper investigates the framework of encoder-decoder with attention for
sequence labelling based spoken language understanding. We introduce
Bidirectional Long Short Term Memory - Long Short Term Memory networks
(BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep
learning. In the sequence l... | computer science |
16,008 | HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment | cs.CL | We introduce HyperLex - a dataset and evaluation resource that quantifies the
extent of of the semantic category membership, that is, type-of relation also
known as hyponymy-hypernymy or lexical entailment (LE) relation between 2,616
concept pairs. Cognitive psychology research has established that typicality
and categ... | computer science |
16,009 | Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network | cs.CL | Language processing mechanism by humans is generally more robust than
computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the
psycholinguistics literature has demonstrated such a robust word processing
mechanism, where jumbled words (e.g. Cmabrigde / Cambridge) are recognized with
little cost. On the... | computer science |
16,010 | Canonical Correlation Inference for Mapping Abstract Scenes to Text | cs.CL | We describe a technique for structured prediction, based on canonical
correlation analysis. Our learning algorithm finds two projections for the
input and the output spaces that aim at projecting a given input and its
correct output into points close to each other. We demonstrate our technique on
a language-vision prob... | computer science |
16,011 | A pragmatic theory of generic language | cs.CL | Generalizations about categories are central to human understanding, and
generic language (e.g. "Dogs bark.") provides a simple and ubiquitous way to
communicate these generalizations. Yet the meaning of generic language is
philosophically puzzling and has resisted precise formalization. We explore the
idea that the co... | computer science |
16,012 | Temporal Attention Model for Neural Machine Translation | cs.CL | Attention-based Neural Machine Translation (NMT) models suffer from attention
deficiency issues as has been observed in recent research. We propose a novel
mechanism to address some of these limitations and improve the NMT attention.
Specifically, our approach memorizes the alignments temporally (within each
sentence) ... | computer science |
16,013 | Hierarchical Character-Word Models for Language Identification | cs.CL | Social media messages' brevity and unconventional spelling pose a challenge
to language identification. We introduce a hierarchical model that learns
character and contextualized word-level representations for language
identification. Our method performs well against strong base- lines, and can
also reveal code-switchi... | computer science |
16,014 | An assessment of orthographic similarity measures for several African
languages | cs.CL | Natural Language Interfaces and tools such as spellcheckers and Web search in
one's own language are known to be useful in ICT-mediated communication. Most
languages in Southern Africa are under-resourced, however. Therefore, it would
be very useful if both the generic and the few language-specific NLP tools
could be r... | computer science |
16,015 | Sex, drugs, and violence | cs.CL | Automatically detecting inappropriate content can be a difficult NLP task,
requiring understanding context and innuendo, not just identifying specific
keywords. Due to the large quantity of online user-generated content, automatic
detection is becoming increasingly necessary. We take a largely unsupervised
approach usi... | computer science |
16,016 | WikiReading: A Novel Large-scale Language Understanding Task over
Wikipedia | cs.CL | We present WikiReading, a large-scale natural language understanding task and
publicly-available dataset with 18 million instances. The task is to predict
textual values from the structured knowledge base Wikidata by reading the text
of the corresponding Wikipedia articles. The task contains a rich variety of
challengi... | computer science |
16,017 | The statistical trade-off between word order and word structure -
large-scale evidence for the principle of least effort | cs.CL | Languages employ different strategies to transmit structural and grammatical
information. While, for example, grammatical dependency relationships in
sentences are mainly conveyed by the ordering of the words for languages like
Mandarin Chinese, or Vietnamese, the word ordering is much less restricted for
languages suc... | computer science |
16,018 | Redefining part-of-speech classes with distributional semantic models | cs.CL | This paper studies how word embeddings trained on the British National Corpus
interact with part of speech boundaries. Our work targets the Universal PoS tag
set, which is currently actively being used for annotation of a range of
languages. We experiment with training classifiers for predicting PoS tags for
words base... | computer science |
16,019 | Analysis of Morphology in Topic Modeling | cs.CL | Topic models make strong assumptions about their data. In particular,
different words are implicitly assumed to have different meanings: topic models
are often used as human-interpretable dimensionality reductions and a
proliferation of words with identical meanings would undermine the utility of
the top-$m$ word list ... | computer science |
16,020 | Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction
Tasks | cs.CL | There is a lot of research interest in encoding variable length sentences
into fixed length vectors, in a way that preserves the sentence meanings. Two
common methods include representations based on averaging word vectors, and
representations based on the hidden states of recurrent neural networks such as
LSTMs. The s... | computer science |
16,021 | Natural Language Processing using Hadoop and KOSHIK | cs.CL | Natural language processing, as a data analytics related technology, is used
widely in many research areas such as artificial intelligence, human language
processing, and translation. At present, due to explosive growth of data, there
are many challenges for natural language processing. Hadoop is one of the
platforms t... | computer science |
16,022 | Fast, Small and Exact: Infinite-order Language Modelling with Compressed
Suffix Trees | cs.CL | Efficient methods for storing and querying are critical for scaling
high-order n-gram language models to large corpora. We propose a language model
based on compressed suffix trees, a representation that is highly compact and
can be easily held in memory, while supporting queries needed in computing
language model prob... | computer science |
16,023 | Authorship clustering using multi-headed recurrent neural networks | cs.CL | A recurrent neural network that has been trained to separately model the
language of several documents by unknown authors is used to measure similarity
between the documents. It is able to find clues of common authorship even when
the documents are very short and about disparate topics. While it is easy to
make statist... | computer science |
16,024 | Neural versus Phrase-Based Machine Translation Quality: a Case Study | cs.CL | Within the field of Statistical Machine Translation (SMT), the neural
approach (NMT) has recently emerged as the first technology able to challenge
the long-standing dominance of phrase-based approaches (PBMT). In particular,
at the IWSLT 2015 evaluation campaign, NMT outperformed well established
state-of-the-art PBMT... | computer science |
16,025 | Proceedings of the LexSem+Logics Workshop 2016 | cs.CL | Lexical semantics continues to play an important role in driving research
directions in NLP, with the recognition and understanding of context becoming
increasingly important in delivering successful outcomes in NLP tasks. Besides
traditional processing areas such as word sense and named entity
disambiguation, the crea... | computer science |
16,026 | Ensemble of Jointly Trained Deep Neural Network-Based Acoustic Models
for Reverberant Speech Recognition | cs.CL | Distant speech recognition is a challenge, particularly due to the corruption
of speech signals by reverberation caused by large distances between the
speaker and microphone. In order to cope with a wide range of reverberations in
real-world situations, we present novel approaches for acoustic modeling
including an ens... | computer science |
16,027 | Path-based vs. Distributional Information in Recognizing Lexical
Semantic Relations | cs.CL | Recognizing various semantic relations between terms is beneficial for many
NLP tasks. While path-based and distributional information sources are
considered complementary for this task, the superior results the latter showed
recently suggested that the former's contribution might have become obsolete.
We follow the re... | computer science |
16,028 | SlangSD: Building and Using a Sentiment Dictionary of Slang Words for
Short-Text Sentiment Classification | cs.CL | Sentiment in social media is increasingly considered as an important resource
for customer segmentation, market understanding, and tackling other
socio-economic issues. However, sentiment in social media is difficult to
measure since user-generated content is usually short and informal. Although
many traditional sentim... | computer science |
16,029 | Multilingual Modal Sense Classification using a Convolutional Neural
Network | cs.CL | Modal sense classification (MSC) is a special WSD task that depends on the
meaning of the proposition in the modal's scope. We explore a CNN architecture
for classifying modal sense in English and German. We show that CNNs are
superior to manually designed feature-based classifiers and a standard NN
classifier. We anal... | computer science |
16,030 | DNN-based Speech Synthesis for Indian Languages from ASCII text | cs.CL | Text-to-Speech synthesis in Indian languages has a seen lot of progress over
the decade partly due to the annual Blizzard challenges. These systems assume
the text to be written in Devanagari or Dravidian scripts which are nearly
phonemic orthography scripts. However, the most common form of computer
interaction among ... | computer science |
16,031 | A Strong Baseline for Learning Cross-Lingual Word Embeddings from
Sentence Alignments | cs.CL | While cross-lingual word embeddings have been studied extensively in recent
years, the qualitative differences between the different algorithms remain
vague. We observe that whether or not an algorithm uses a particular feature
set (sentence IDs) accounts for a significant performance gap among these
algorithms. This f... | computer science |
16,032 | Who did What: A Large-Scale Person-Centered Cloze Dataset | cs.CL | We have constructed a new "Who-did-What" dataset of over 200,000
fill-in-the-gap (cloze) multiple choice reading comprehension problems
constructed from the LDC English Gigaword newswire corpus. The WDW dataset has
a variety of novel features. First, in contrast with the CNN and Daily Mail
datasets (Hermann et al., 201... | computer science |
16,033 | Automatic Selection of Context Configurations for Improved
Class-Specific Word Representations | cs.CL | This paper is concerned with identifying contexts useful for training word
representation models for different word classes such as adjectives (A), verbs
(V), and nouns (N). We introduce a simple yet effective framework for an
automatic selection of class-specific context configurations. We construct a
context configur... | computer science |
16,034 | Learning to Start for Sequence to Sequence Architecture | cs.CL | The sequence to sequence architecture is widely used in the response
generation and neural machine translation to model the potential relationship
between two sentences. It typically consists of two parts: an encoder that
reads from the source sentence and a decoder that generates the target sentence
word by word accor... | computer science |
16,035 | Modeling Human Reading with Neural Attention | cs.CL | When humans read text, they fixate some words and skip others. However, there
have been few attempts to explain skipping behavior with computational models,
as most existing work has focused on predicting reading times (e.g.,~using
surprisal). In this paper, we propose a novel approach that models both
skipping and rea... | computer science |
16,036 | Using Distributed Representations to Disambiguate Biomedical and
Clinical Concepts | cs.CL | In this paper, we report a knowledge-based method for Word Sense
Disambiguation in the domains of biomedical and clinical text. We combine word
representations created on large corpora with a small number of definitions
from the UMLS to create concept representations, which we then compare to
representations of the con... | computer science |
16,037 | Topic Sensitive Neural Headline Generation | cs.CL | Neural models have recently been used in text summarization including
headline generation. The model can be trained using a set of document-headline
pairs. However, the model does not explicitly consider topical similarities and
differences of documents. We suggest to categorizing documents into various
topics so that ... | computer science |
16,038 | Using the Output Embedding to Improve Language Models | cs.CL | We study the topmost weight matrix of neural network language models. We show
that this matrix constitutes a valid word embedding. When training language
models, we recommend tying the input embedding and this output embedding. We
analyze the resulting update rules and show that the tied embedding evolves in
a more sim... | computer science |
16,039 | Context Gates for Neural Machine Translation | cs.CL | In neural machine translation (NMT), generation of a target word depends on
both source and target contexts. We find that source contexts have a direct
impact on the adequacy of a translation while target contexts affect the
fluency. Intuitively, generation of a content word should rely more on the
source context and g... | computer science |
16,040 | An Incremental Parser for Abstract Meaning Representation | cs.CL | Meaning Representation (AMR) is a semantic representation for natural
language that embeds annotations related to traditional tasks such as named
entity recognition, semantic role labeling, word sense disambiguation and
co-reference resolution. We describe a transition-based parser for AMR that
parses sentences left-to... | computer science |
16,041 | Median-Based Generation of Synthetic Speech Durations using a
Non-Parametric Approach | cs.CL | This paper proposes a new approach to duration modelling for statistical
parametric speech synthesis in which a recurrent statistical model is trained
to output a phone transition probability at each timestep (acoustic frame).
Unlike conventional approaches to duration modelling -- which assume that
duration distributi... | computer science |
16,042 | Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening
Comprehension Test by Machine | cs.CL | Multimedia or spoken content presents more attractive information than plain
text content, but it's more difficult to display on a screen and be selected by
a user. As a result, accessing large collections of the former is much more
difficult and time-consuming than the latter for humans. It's highly attractive
to deve... | computer science |
16,043 | Which techniques does your application use?: An information extraction
framework for scientific articles | cs.CL | Every field of research consists of multiple application areas with various
techniques routinely used to solve problems in these wide range of application
areas. With the exponential growth in research volumes, it has become difficult
to keep track of the ever-growing number of application areas as well as the
correspo... | computer science |
16,044 | Semantic descriptions of 24 evaluational adjectives, for application in
sentiment analysis | cs.CL | We apply the Natural Semantic Metalanguage (NSM) approach (Goddard and
Wierzbicka 2014) to the lexical-semantic analysis of English evaluational
adjectives and compare the results with the picture developed in the Appraisal
Framework (Martin and White 2005). The analysis is corpus-assisted, with
examples mainly drawn f... | computer science |
16,045 | A Large-Scale Multilingual Disambiguation of Glosses | cs.CL | Linking concepts and named entities to knowledge bases has become a crucial
Natural Language Understanding task. In this respect, recent works have shown
the key advantage of exploiting textual definitions in various Natural Language
Processing applications. However, to date there are no reliable large-scale
corpora of... | computer science |
16,046 | Robust Named Entity Recognition in Idiosyncratic Domains | cs.CL | Named entity recognition often fails in idiosyncratic domains. That causes a
problem for depending tasks, such as entity linking and relation extraction. We
propose a generic and robust approach for high-recall named entity recognition.
Our approach is easy to train and offers strong generalization over diverse
domain-... | computer science |
16,047 | Improving Sparse Word Representations with Distributional Inference for
Semantic Composition | cs.CL | Distributional models are derived from co-occurrences in a corpus, where only
a small proportion of all possible plausible co-occurrences will be observed.
This results in a very sparse vector space, requiring a mechanism for inferring
missing knowledge. Most methods face this challenge in ways that render the
resultin... | computer science |
16,048 | A Context-aware Natural Language Generator for Dialogue Systems | cs.CL | We present a novel natural language generation system for spoken dialogue
systems capable of entraining (adapting) to users' way of speaking, providing
contextually appropriate responses. The generator is based on recurrent neural
networks and the sequence-to-sequence approach. It is fully trainable from data
which inc... | computer science |
16,049 | Aligning Packed Dependency Trees: a theory of composition for
distributional semantics | cs.CL | We present a new framework for compositional distributional semantics in
which the distributional contexts of lexemes are expressed in terms of anchored
packed dependency trees. We show that these structures have the potential to
capture the full sentential contexts of a lexeme and provide a uniform basis
for the compo... | computer science |
16,050 | A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence
Features | cs.CL | Relation classification is associated with many potential applications in the
artificial intelligence area. Recent approaches usually leverage neural
networks based on structure features such as syntactic or dependency features
to solve this problem. However, high-cost structure features make such
approaches inconvenie... | computer science |
16,051 | Testing APSyn against Vector Cosine on Similarity Estimation | cs.CL | In Distributional Semantic Models (DSMs), Vector Cosine is widely used to
estimate similarity between word vectors, although this measure was noticed to
suffer from several shortcomings. The recent literature has proposed other
methods which attempt to mitigate such biases. In this paper, we intend to
investigate APSyn... | computer science |
16,052 | Hierarchical Attention Model for Improved Machine Comprehension of
Spoken Content | cs.CL | Multimedia or spoken content presents more attractive information than plain
text content, but the former is more difficult to display on a screen and be
selected by a user. As a result, accessing large collections of the former is
much more difficult and time-consuming than the latter for humans. It's
therefore highly... | computer science |
16,053 | What to do about non-standard (or non-canonical) language in NLP | cs.CL | Real world data differs radically from the benchmark corpora we use in
natural language processing (NLP). As soon as we apply our technologies to the
real world, performance drops. The reason for this problem is obvious: NLP
models are trained on samples from a limited set of canonical varieties that
are considered sta... | computer science |
16,054 | A Dictionary-based Approach to Racism Detection in Dutch Social Media | cs.CL | We present a dictionary-based approach to racism detection in Dutch social
media comments, which were retrieved from two public Belgian social media sites
likely to attract racist reactions. These comments were labeled as racist or
non-racist by multiple annotators. For our approach, three discourse
dictionaries were c... | computer science |
16,055 | Demographic Dialectal Variation in Social Media: A Case Study of
African-American English | cs.CL | Though dialectal language is increasingly abundant on social media, few
resources exist for developing NLP tools to handle such language. We conduct a
case study of dialectal language in online conversational text by investigating
African-American English (AAE) on Twitter. We propose a distantly supervised
model to ide... | computer science |
16,056 | How Much is 131 Million Dollars? Putting Numbers in Perspective with
Compositional Descriptions | cs.CL | How much is 131 million US dollars? To help readers put such numbers in
context, we propose a new task of automatically generating short descriptions
known as perspectives, e.g. "$131 million is about the cost to employ everyone
in Texas over a lunch period". First, we collect a dataset of numeric mentions
in news arti... | computer science |
16,057 | Improving Correlation with Human Judgments by Integrating Semantic
Similarity with Second--Order Vectors | cs.CL | Vector space methods that measure semantic similarity and relatedness often
rely on distributional information such as co--occurrence frequencies or
statistical measures of association to weight the importance of particular
co--occurrences. In this paper, we extend these methods by incorporating a
measure of semantic s... | computer science |
16,058 | Skipping Word: A Character-Sequential Representation based Framework for
Question Answering | cs.CL | Recent works using artificial neural networks based on word distributed
representation greatly boost the performance of various natural language
learning tasks, especially question answering. Though, they also carry along
with some attendant problems, such as corpus selection for embedding learning,
dictionary transfor... | computer science |
16,059 | Bi-Text Alignment of Movie Subtitles for Spoken English-Arabic
Statistical Machine Translation | cs.CL | We describe efforts towards getting better resources for English-Arabic
machine translation of spoken text. In particular, we look at movie subtitles
as a unique, rich resource, as subtitles in one language often get translated
into other languages. Movie subtitles are not new as a resource and have been
explored in pr... | computer science |
16,060 | PMI Matrix Approximations with Applications to Neural Language Modeling | cs.CL | The negative sampling (NEG) objective function, used in word2vec, is a
simplification of the Noise Contrastive Estimation (NCE) method. NEG was found
to be highly effective in learning continuous word representations. However,
unlike NCE, it was considered inapplicable for the purpose of learning the
parameters of a la... | computer science |
16,061 | Attention-Based Recurrent Neural Network Models for Joint Intent
Detection and Slot Filling | cs.CL | Attention-based encoder-decoder neural network models have recently shown
promising results in machine translation and speech recognition. In this work,
we propose an attention-based neural network model for joint intent detection
and slot filling, both of which are critical steps for many speech
understanding and dial... | computer science |
16,062 | Joint Online Spoken Language Understanding and Language Modeling with
Recurrent Neural Networks | cs.CL | Speaker intent detection and semantic slot filling are two critical tasks in
spoken language understanding (SLU) for dialogue systems. In this paper, we
describe a recurrent neural network (RNN) model that jointly performs intent
detection, slot filling, and language modeling. The neural network model keeps
updating th... | computer science |
16,063 | Sentiment Classification of Food Reviews | cs.CL | Sentiment analysis of reviews is a popular task in natural language
processing. In this work, the goal is to predict the score of food reviews on a
scale of 1 to 5 with two recurrent neural networks that are carefully tuned. As
for baseline, we train a simple RNN for classification. Then we extend the
baseline to GRU. ... | computer science |
16,064 | Learning Lexical Entries for Robotic Commands using Crowdsourcing | cs.CL | Robotic commands in natural language usually contain various spatial
descriptions that are semantically similar but syntactically different. Mapping
such syntactic variants into semantic concepts that can be understood by robots
is challenging due to the high flexibility of natural language expressions. To
tackle this ... | computer science |
16,065 | Harassment detection: a benchmark on the #HackHarassment dataset | cs.CL | Online harassment has been a problem to a greater or lesser extent since the
early days of the internet. Previous work has applied anti-spam techniques like
machine-learning based text classification (Reynolds, 2011) to detecting
harassing messages. However, existing public datasets are limited in size, with
labels of ... | computer science |
16,066 | Dialogue manager domain adaptation using Gaussian process reinforcement
learning | cs.CL | Spoken dialogue systems allow humans to interact with machines using natural
speech. As such, they have many benefits. By using speech as the primary
communication medium, a computer interface can facilitate swift, human-like
acquisition of information. In recent years, speech interfaces have become ever
more popular, ... | computer science |
16,067 | A Large Scale Corpus of Gulf Arabic | cs.CL | Most Arabic natural language processing tools and resources are developed to
serve Modern Standard Arabic (MSA), which is the official written language in
the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have
received some attention lately and have a growing collection of resources that
include... | computer science |
16,068 | On the Similarities Between Native, Non-native and Translated Texts | cs.CL | We present a computational analysis of three language varieties: native,
advanced non-native, and translation. Our goal is to investigate the
similarities and differences between non-native language productions and
translations, contrasting both with native language. Using a collection of
computational methods we estab... | computer science |
16,069 | Unsupervised Identification of Translationese | cs.CL | Translated texts are distinctively different from original ones, to the
extent that supervised text classification methods can distinguish between them
with high accuracy. These differences were proven useful for statistical
machine translation. However, it has been suggested that the accuracy of
translation detection ... | computer science |
16,070 | Morphological Constraints for Phrase Pivot Statistical Machine
Translation | cs.CL | The lack of parallel data for many language pairs is an important challenge
to statistical machine translation (SMT). One common solution is to pivot
through a third language for which there exist parallel corpora with the source
and target languages. Although pivoting is a robust technique, it introduces
some low qual... | computer science |
16,071 | Read, Tag, and Parse All at Once, or Fully-neural Dependency Parsing | cs.CL | We present a dependency parser implemented as a single deep neural network
that reads orthographic representations of words and directly generates
dependencies and their labels. Unlike typical approaches to parsing, the model
doesn't require part-of-speech (POS) tagging of the sentences. With proper
regularization and ... | computer science |
16,072 | The Microsoft 2016 Conversational Speech Recognition System | cs.CL | We describe Microsoft's conversational speech recognition system, in which we
combine recent developments in neural-network-based acoustic and language
modeling to advance the state of the art on the Switchboard recognition task.
Inspired by machine learning ensemble techniques, the system uses a range of
convolutional... | computer science |
16,073 | Neural Machine Translation with Supervised Attention | cs.CL | The attention mechanisim is appealing for neural machine translation, since
it is able to dynam- ically encode a source sentence by generating a alignment
between a target word and source words. Unfortunately, it has been proved to be
worse than conventional alignment models in aligment accuracy. In this paper,
we anal... | computer science |
16,074 | Neural Machine Transliteration: Preliminary Results | cs.CL | Machine transliteration is the process of automatically transforming the
script of a word from a source language to a target language, while preserving
pronunciation. Sequence to sequence learning has recently emerged as a new
paradigm in supervised learning. In this paper a character-based
encoder-decoder model has be... | computer science |
16,075 | Transliteration in Any Language with Surrogate Languages | cs.CL | We introduce a method for transliteration generation that can produce
transliterations in every language. Where previous results are only as
multilingual as Wikipedia, we show how to use training data from Wikipedia as
surrogate training for any language. Thus, the problem becomes one of ranking
Wikipedia languages in ... | computer science |
16,076 | Factored Neural Machine Translation | cs.CL | We present a new approach for neural machine translation (NMT) using the
morphological and grammatical decomposition of the words (factors) in the
output side of the neural network. This architecture addresses two main
problems occurring in MT, namely dealing with a large target language
vocabulary and the out of vocab... | computer science |
16,077 | Characterizing the Language of Online Communities and its Relation to
Community Reception | cs.CL | This work investigates style and topic aspects of language in online
communities: looking at both utility as an identifier of the community and
correlation with community reception of content. Style is characterized using a
hybrid word and part-of-speech tag n-gram language model, while topic is
represented using Laten... | computer science |
16,078 | Distant Supervision for Relation Extraction beyond the Sentence Boundary | cs.CL | The growing demand for structured knowledge has led to great interest in
relation extraction, especially in cases with limited supervision. However,
existing distance supervision approaches only extract relations expressed in
single sentences. In general, cross-sentence relation extraction is
under-explored, even in th... | computer science |
16,079 | An Iterative Transfer Learning Based Ensemble Technique for Automatic
Short Answer Grading | cs.CL | Automatic short answer grading (ASAG) techniques are designed to
automatically assess short answers to questions in natural language, having a
length of a few words to a few sentences. Supervised ASAG techniques have been
demonstrated to be effective but suffer from a couple of key practical
limitations. They are great... | computer science |
16,080 | Multilinear Grammar: Ranks and Interpretations | cs.CL | Multilinear Grammar provides a framework for integrating the many different
syntagmatic structures of language into a coherent semiotically based Rank
Interpretation Architecture, with default linear grammars at each rank. The
architecture defines a Sui Generis Condition on ranks, from discourse through
utterance and p... | computer science |
16,081 | The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition | cs.CL | This paper describes the Arabic Multi-Genre Broadcast (MGB-2) Challenge for
SLT-2016. Unlike last year's English MGB Challenge, which focused on
recognition of diverse TV genres, this year, the challenge has an emphasis on
handling the diversity in dialect in Arabic speech. Audio data comes from 19
distinct programmes ... | computer science |
16,082 | Multi-view Dimensionality Reduction for Dialect Identification of Arabic
Broadcast Speech | cs.CL | In this work, we present a new Vector Space Model (VSM) of speech utterances
for the task of spoken dialect identification. Generally, DID systems are built
using two sets of features that are extracted from speech utterances; acoustic
and phonetic. The acoustic and phonetic features are used to form vector
representat... | computer science |
16,083 | Advances in All-Neural Speech Recognition | cs.CL | This paper advances the design of CTC-based all-neural (or end-to-end) speech
recognizers. We propose a novel symbol inventory, and a novel iterated-CTC
method in which a second system is used to transform a noisy initial output
into a cleaner version. We present a number of stabilization and initialization
methods we ... | computer science |
16,084 | Enhanced LSTM for Natural Language Inference | cs.CL | Reasoning and inference are central to human and artificial intelligence.
Modeling inference in human language is very challenging. With the availability
of large annotated data (Bowman et al., 2015), it has recently become feasible
to train neural network based inference models, which have shown to be very
effective. ... | computer science |
16,085 | Automatic Quality Assessment for Speech Translation Using Joint ASR and
MT Features | cs.CL | This paper addresses automatic quality assessment of spoken language
translation (SLT). This relatively new task is defined and formalized as a
sequence labeling problem where each word in the SLT hypothesis is tagged as
good or bad according to a large feature set. We propose several word
confidence estimators (WCE) b... | computer science |
16,086 | Learning Robust Representations of Text | cs.CL | Deep neural networks have achieved remarkable results across many language
processing tasks, however these methods are highly sensitive to noise and
adversarial attacks. We present a regularization based method for limiting
network sensitivity to its inputs, inspired by ideas from computer vision, thus
learning models ... | computer science |
16,087 | Italy goes to Stanford: a collection of CoreNLP modules for Italian | cs.CL | In this we paper present Tint, an easy-to-use set of fast, accurate and
extendable Natural Language Processing modules for Italian. It is based on
Stanford CoreNLP and is freely available as a standalone software or a library
that can be integrated in an existing project. | computer science |
16,088 | Generating Politically-Relevant Event Data | cs.CL | Automatically generated political event data is an important part of the
social science data ecosystem. The approaches for generating this data, though,
have remained largely the same for two decades. During this time, the field of
computational linguistics has progressed tremendously. This paper presents an
overview o... | computer science |
16,089 | One Sentence One Model for Neural Machine Translation | cs.CL | Neural machine translation (NMT) becomes a new state-of-the-art and achieves
promising translation results using a simple encoder-decoder neural network.
This neural network is trained once on the parallel corpus and the fixed
network is used to translate all the test sentences. We argue that the general
fixed network ... | computer science |
16,090 | Weakly supervised spoken term discovery using cross-lingual side
information | cs.CL | Recent work on unsupervised term discovery (UTD) aims to identify and cluster
repeated word-like units from audio alone. These systems are promising for some
very low-resource languages where transcribed audio is unavailable, or where no
written form of the language exists. However, in some cases it may still be
feasib... | computer science |
16,091 | Semi-supervised knowledge extraction for detection of drugs and their
effects | cs.CL | New Psychoactive Substances (NPS) are drugs that lay in a grey area of
legislation, since they are not internationally and officially banned, possibly
leading to their not prosecutable trade. The exacerbation of the phenomenon is
that NPS can be easily sold and bought online. Here, we consider large corpora
of textual ... | computer science |
16,092 | Minimally Supervised Written-to-Spoken Text Normalization | cs.CL | In speech-applications such as text-to-speech (TTS) or automatic speech
recognition (ASR), \emph{text normalization} refers to the task of converting
from a \emph{written} representation into a representation of how the text is
to be \emph{spoken}. In all real-world speech applications, the text
normalization engine is... | computer science |
16,093 | Joint CTC-Attention based End-to-End Speech Recognition using Multi-task
Learning | cs.CL | Recently, there has been an increasing interest in end-to-end speech
recognition that directly transcribes speech to text without any predefined
alignments. One approach is the attention-based encoder-decoder framework that
learns a mapping between variable-length input and output sequences in one step
using a purely d... | computer science |
16,094 | Generating Abstractive Summaries from Meeting Transcripts | cs.CL | Summaries of meetings are very important as they convey the essential content
of discussions in a concise form. Generally, it is time consuming to read and
understand the whole documents. Therefore, summaries play an important role as
the readers are interested in only the important context of discussions. In
this work... | computer science |
16,095 | Multi-document abstractive summarization using ILP based multi-sentence
compression | cs.CL | Abstractive summarization is an ideal form of summarization since it can
synthesize information from multiple documents to create concise informative
summaries. In this work, we aim at developing an abstractive summarizer. First,
our proposed approach identifies the most important document in the
multi-document set. Th... | computer science |
16,096 | Abstractive Meeting Summarization UsingDependency Graph Fusion | cs.CL | Automatic summarization techniques on meeting conversations developed so far
have been primarily extractive, resulting in poor summaries. To improve this,
we propose an approach to generate abstractive summaries by fusing important
content from several utterances. Any meeting is generally comprised of several
discussio... | computer science |
16,097 | Semantic Tagging with Deep Residual Networks | cs.CL | We propose a novel semantic tagging task, sem-tagging, tailored for the
purpose of multilingual semantic parsing, and present the first tagger using
deep residual networks (ResNets). Our tagger uses both word and character
representations and includes a novel residual bypass architecture. We evaluate
the tagset both in... | computer science |
16,098 | Knowledge Representation via Joint Learning of Sequential Text and
Knowledge Graphs | cs.CL | Textual information is considered as significant supplement to knowledge
representation learning (KRL). There are two main challenges for constructing
knowledge representations from plain texts: (1) How to take full advantages of
sequential contexts of entities in plain texts for KRL. (2) How to dynamically
select thos... | computer science |
16,099 | Annotating Derivations: A New Evaluation Strategy and Dataset for
Algebra Word Problems | cs.CL | We propose a new evaluation for automatic solvers for algebra word problems,
which can identify mistakes that existing evaluations overlook. Our proposal is
to evaluate such solvers using derivations, which reflect how an equation
system was constructed from the word problem. To accomplish this, we develop an
algorithm... | computer science |
16,100 | Deep Multi-Task Learning with Shared Memory | cs.CL | Neural network based models have achieved impressive results on various
specific tasks. However, in previous works, most models are learned separately
based on single-task supervised objectives, which often suffer from
insufficient training data. In this paper, we propose two deep architectures
which can be trained joi... | computer science |
16,101 | AMR-to-text generation as a Traveling Salesman Problem | cs.CL | The task of AMR-to-text generation is to generate grammatical text that
sustains the semantic meaning for a given AMR graph. We at- tack the task by
first partitioning the AMR graph into smaller fragments, and then generating
the translation for each fragment, before finally deciding the order by solving
an asymmetric ... | computer science |
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