Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
16,302 | A Simple, Fast Diverse Decoding Algorithm for Neural Generation | cs.CL | In this paper, we propose a simple, fast decoding algorithm that fosters
diversity in neural generation. The algorithm modifies the standard beam search
algorithm by adding an inter-sibling ranking penalty, favoring choosing
hypotheses from diverse parents. We evaluate the proposed model on the tasks of
dialogue respon... | computer science |
16,303 | Attention-based Memory Selection Recurrent Network for Language Modeling | cs.CL | Recurrent neural networks (RNNs) have achieved great success in language
modeling. However, since the RNNs have fixed size of memory, their memory
cannot store all the information about the words it have seen before in the
sentence, and thus the useful long-term information may be ignored when
predicting the next words... | computer science |
16,304 | Knowledge Graph Representation with Jointly Structural and Textual
Encoding | cs.CL | The objective of knowledge graph embedding is to encode both entities and
relations of knowledge graphs into continuous low-dimensional vector spaces.
Previously, most works focused on symbolic representation of knowledge graph
with structure information, which can not handle new entities or entities with
few facts wel... | computer science |
16,305 | Fill it up: Exploiting partial dependency annotations in a minimum
spanning tree parser | cs.CL | Unsupervised models of dependency parsing typically require large amounts of
clean, unlabeled data plus gold-standard part-of-speech tags. Adding indirect
supervision (e.g. language universals and rules) can help, but we show that
obtaining small amounts of direct supervision - here, partial dependency
annotations - pr... | computer science |
16,306 | Semi Supervised Preposition-Sense Disambiguation using Multilingual Data | cs.CL | Prepositions are very common and very ambiguous, and understanding their
sense is critical for understanding the meaning of the sentence. Supervised
corpora for the preposition-sense disambiguation task are small, suggesting a
semi-supervised approach to the task. We show that signals from unannotated
multilingual data... | computer science |
16,307 | Exploiting Unlabeled Data for Neural Grammatical Error Detection | cs.CL | Identifying and correcting grammatical errors in the text written by
non-native writers has received increasing attention in recent years. Although
a number of annotated corpora have been established to facilitate data-driven
grammatical error detection and correction approaches, they are still limited
in terms of quan... | computer science |
16,308 | Developing a cardiovascular disease risk factor annotated corpus of
Chinese electronic medical records | cs.CL | Cardiovascular disease (CVD) has become the leading cause of death in China,
and most of the cases can be prevented by controlling risk factors. The goal of
this study was to build a corpus of CVD risk factor annotations based on
Chinese electronic medical records (CEMRs). This corpus is intended to be used
to develop ... | computer science |
16,309 | Learning to Compose Words into Sentences with Reinforcement Learning | cs.CL | We use reinforcement learning to learn tree-structured neural networks for
computing representations of natural language sentences. In contrast with prior
work on tree-structured models in which the trees are either provided as input
or predicted using supervision from explicit treebank annotations, the tree
structures... | computer science |
16,310 | An End-to-End Architecture for Keyword Spotting and Voice Activity
Detection | cs.CL | We propose a single neural network architecture for two tasks: on-line
keyword spotting and voice activity detection. We develop novel inference
algorithms for an end-to-end Recurrent Neural Network trained with the
Connectionist Temporal Classification loss function which allow our model to
achieve high accuracy on bo... | computer science |
16,311 | Geometry of Compositionality | cs.CL | This paper proposes a simple test for compositionality (i.e., literal usage)
of a word or phrase in a context-specific way. The test is computationally
simple, relying on no external resources and only uses a set of trained word
vectors. Experiments show that the proposed method is competitive with state of
the art and... | computer science |
16,312 | Context-aware Natural Language Generation with Recurrent Neural Networks | cs.CL | This paper studied generating natural languages at particular contexts or
situations. We proposed two novel approaches which encode the contexts into a
continuous semantic representation and then decode the semantic representation
into text sequences with recurrent neural networks. During decoding, the
context informat... | computer science |
16,313 | Towards Accurate Word Segmentation for Chinese Patents | cs.CL | A patent is a property right for an invention granted by the government to
the inventor. An invention is a solution to a specific technological problem.
So patents often have a high concentration of scientific and technical terms
that are rare in everyday language. The Chinese word segmentation model trained
on current... | computer science |
16,314 | Deep encoding of etymological information in TEI | cs.CL | This paper aims to provide a comprehensive modeling and representation of
etymological data in digital dictionaries. The purpose is to integrate in one
coherent framework both digital representations of legacy dictionaries, and
also born-digital lexical databases that are constructed manually or
semi-automatically. We ... | computer science |
16,315 | A POS Tagger for Code Mixed Indian Social Media Text - ICON-2016 NLP
Tools Contest Entry from Surukam | cs.CL | Building Part-of-Speech (POS) taggers for code-mixed Indian languages is a
particularly challenging problem in computational linguistics due to a dearth
of accurately annotated training corpora. ICON, as part of its NLP tools
contest has organized this challenge as a shared task for the second
consecutive year to impro... | computer science |
16,316 | Expanding Subjective Lexicons for Social Media Mining with Embedding
Subspaces | cs.CL | Recent approaches for sentiment lexicon induction have capitalized on
pre-trained word embeddings that capture latent semantic properties. However,
embeddings obtained by optimizing performance of a given task (e.g. predicting
contextual words) are sub-optimal for other applications. In this paper, we
address this prob... | computer science |
16,317 | Social Media Argumentation Mining: The Quest for Deliberateness in
Raucousness | cs.CL | Argumentation mining from social media content has attracted increasing
attention. The task is both challenging and rewarding. The informal nature of
user-generated content makes the task dauntingly difficult. On the other hand,
the insights that could be gained by a large-scale analysis of social media
argumentation m... | computer science |
16,318 | Aspect-augmented Adversarial Networks for Domain Adaptation | cs.CL | We introduce a neural method for transfer learning between two (source and
target) classification tasks or aspects over the same domain. Rather than
training on target labels, we use a few keywords pertaining to source and
target aspects indicating sentence relevance instead of document class labels.
Documents are enco... | computer science |
16,319 | Stance detection in online discussions | cs.CL | This paper describes our system created to detect stance in online
discussions. The goal is to identify whether the author of a comment is in
favor of the given target or against. Our approach is based on a maximum
entropy classifier, which uses surface-level, sentiment and domain-specific
features. The system was orig... | computer science |
16,320 | Shortcut Sequence Tagging | cs.CL | Deep stacked RNNs are usually hard to train. Adding shortcut connections
across different layers is a common way to ease the training of stacked
networks. However, extra shortcuts make the recurrent step more complicated. To
simply the stacked architecture, we propose a framework called shortcut block,
which is a marri... | computer science |
16,321 | On (Commercial) Benefits of Automatic Text Summarization Systems in the
News Domain: A Case of Media Monitoring and Media Response Analysis | cs.CL | In this work, we present the results of a systematic study to investigate the
(commercial) benefits of automatic text summarization systems in a real world
scenario. More specifically, we define a use case in the context of media
monitoring and media response analysis and claim that even using a simple
query-based extr... | computer science |
16,322 | Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences | cs.CL | With the rapid growth of social media on the web, emotional polarity
computation has become a flourishing frontier in the text mining community.
However, it is challenging to understand the latest trends and summarize the
state or general opinions about products due to the big diversity and size of
social media data an... | computer science |
16,323 | Joint Semantic Synthesis and Morphological Analysis of the Derived Word | cs.CL | Much like sentences are composed of words, words themselves are composed of
smaller units. For example, the English word questionably can be analyzed as
question+able+ly. However, this structural decomposition of the word does not
directly give us a semantic representation of the word's meaning. Since
morphology obeys ... | computer science |
16,324 | Textual Entailment with Structured Attentions and Composition | cs.CL | Deep learning techniques are increasingly popular in the textual entailment
task, overcoming the fragility of traditional discrete models with hard
alignments and logics. In particular, the recently proposed attention models
(Rockt\"aschel et al., 2015; Wang and Jiang, 2015) achieves state-of-the-art
accuracy by comput... | computer science |
16,325 | Crime Topic Modeling | cs.CL | The classification of crime into discrete categories entails a massive loss
of information. Crimes emerge out of a complex mix of behaviors and situations,
yet most of these details cannot be captured by singular crime type labels.
This information loss impacts our ability to not only understand the causes of
crime, bu... | computer science |
16,326 | Replication issues in syntax-based aspect extraction for opinion mining | cs.CL | Reproducing experiments is an important instrument to validate previous work
and build upon existing approaches. It has been tackled numerous times in
different areas of science. In this paper, we introduce an empirical
replicability study of three well-known algorithms for syntactic centric
aspect-based opinion mining... | computer science |
16,327 | Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word
Representation | cs.CL | Previous researches have shown that learning multiple representations for
polysemous words can improve the performance of word embeddings on many tasks.
However, this leads to another problem. Several vectors of a word may actually
point to the same meaning, namely pseudo multi-sense. In this paper, we
introduce the co... | computer science |
16,328 | Enumeration of Extractive Oracle Summaries | cs.CL | To analyze the limitations and the future directions of the extractive
summarization paradigm, this paper proposes an Integer Linear Programming (ILP)
formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also
propose an algorithm that enumerates all of the oracle summaries for a set of
reference su... | computer science |
16,329 | Cross-Lingual Dependency Parsing with Late Decoding for Truly
Low-Resource Languages | cs.CL | In cross-lingual dependency annotation projection, information is often lost
during transfer because of early decoding. We present an end-to-end graph-based
neural network dependency parser that can be trained to reproduce matrices of
edge scores, which can be directly projected across word alignments. We show
that our... | computer science |
16,330 | Neural Machine Translation on Scarce-Resource Condition: A case-study on
Persian-English | cs.CL | Neural Machine Translation (NMT) is a new approach for Machine Translation
(MT), and due to its success, it has absorbed the attention of many researchers
in the field. In this paper, we study NMT model on Persian-English language
pairs, to analyze the model and investigate the appropriateness of the model
for scarce-r... | computer science |
16,331 | Sentence-level dialects identification in the greater China region | cs.CL | Identifying the different varieties of the same language is more challenging
than unrelated languages identification. In this paper, we propose an approach
to discriminate language varieties or dialects of Mandarin Chinese for the
Mainland China, Hong Kong, Taiwan, Macao, Malaysia and Singapore, a.k.a., the
Greater Chi... | computer science |
16,332 | Neural Personalized Response Generation as Domain Adaptation | cs.CL | In this paper, we focus on the personalized response generation for
conversational systems. Based on the sequence to sequence learning, especially
the encoder-decoder framework, we propose a two-phase approach, namely
initialization then adaptation, to model the responding style of human and then
generate personalized ... | computer science |
16,333 | Task-Specific Attentive Pooling of Phrase Alignments Contributes to
Sentence Matching | cs.CL | This work studies comparatively two typical sentence matching tasks: textual
entailment (TE) and answer selection (AS), observing that weaker phrase
alignments are more critical in TE, while stronger phrase alignments deserve
more attention in AS. The key to reach this observation lies in phrase
detection, phrase repre... | computer science |
16,334 | Bidirectional American Sign Language to English Translation | cs.CL | We outline a bidirectional translation system that converts sentences from
American Sign Language (ASL) to English, and vice versa. To perform machine
translation between ASL and English, we utilize a generative approach.
Specifically, we employ an adjustment to the IBM word-alignment model 1 (IBM
WAM1), where we defin... | computer science |
16,335 | Generalisation in Named Entity Recognition: A Quantitative Analysis | cs.CL | Named Entity Recognition (NER) is a key NLP task, which is all the more
challenging on Web and user-generated content with their diverse and
continuously changing language. This paper aims to quantify how this diversity
impacts state-of-the-art NER methods, by measuring named entity (NE) and
context variability, featur... | computer science |
16,336 | A Multifaceted Evaluation of Neural versus Phrase-Based Machine
Translation for 9 Language Directions | cs.CL | We aim to shed light on the strengths and weaknesses of the newly introduced
neural machine translation paradigm. To that end, we conduct a multifaceted
evaluation in which we compare outputs produced by state-of-the-art neural
machine translation and phrase-based machine translation systems for 9 language
directions a... | computer science |
16,337 | Question Analysis for Arabic Question Answering Systems | cs.CL | The first step of processing a question in Question Answering(QA) Systems is
to carry out a detailed analysis of the question for the purpose of determining
what it is asking for and how to perfectly approach answering it. Our Question
analysis uses several techniques to analyze any question given in natural
language: ... | computer science |
16,338 | Cross-lingual RST Discourse Parsing | cs.CL | Discourse parsing is an integral part of understanding information flow and
argumentative structure in documents. Most previous research has focused on
inducing and evaluating models from the English RST Discourse Treebank.
However, discourse treebanks for other languages exist, including Spanish,
German, Basque, Dutch... | computer science |
16,339 | Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network | cs.CL | Distinguishing between antonyms and synonyms is a key task to achieve high
performance in NLP systems. While they are notoriously difficult to distinguish
by distributional co-occurrence models, pattern-based methods have proven
effective to differentiate between the relations. In this paper, we present a
novel neural ... | computer science |
16,340 | Job Detection in Twitter | cs.CL | In this report, we propose a new application for twitter data called
\textit{job detection}. We identify people's job category based on their
tweets. As a preliminary work, we limited our task to identify only IT workers
from other job holders. We have used and compared both simple bag of words
model and a document rep... | computer science |
16,341 | De-identification In practice | cs.CL | We report our effort to identify the sensitive information, subset of data
items listed by HIPAA (Health Insurance Portability and Accountability), from
medical text using the recent advances in natural language processing and
machine learning techniques. We represent the words with high dimensional
continuous vectors ... | computer science |
16,342 | Parsing Universal Dependencies without training | cs.CL | We propose UDP, the first training-free parser for Universal Dependencies
(UD). Our algorithm is based on PageRank and a small set of head attachment
rules. It features two-step decoding to guarantee that function words are
attached as leaf nodes. The parser requires no training, and it is competitive
with a delexicali... | computer science |
16,343 | Generating High-Quality and Informative Conversation Responses with
Sequence-to-Sequence Models | cs.CL | Sequence-to-sequence models have been applied to the conversation response
generation problem where the source sequence is the conversation history and
the target sequence is the response. Unlike translation, conversation
responding is inherently creative. The generation of long, informative,
coherent, and diverse resp... | computer science |
16,344 | An Empirical Comparison of Simple Domain Adaptation Methods for Neural
Machine Translation | cs.CL | In this paper, we propose a novel domain adaptation method named "mixed fine
tuning" for neural machine translation (NMT). We combine two existing
approaches namely fine tuning and multi domain NMT. We first train an NMT model
on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus
which is a mi... | computer science |
16,345 | Single-Pass, Adaptive Natural Language Filtering: Measuring Value in
User Generated Comments on Large-Scale, Social Media News Forums | cs.CL | There are large amounts of insight and social discovery potential in mining
crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com,
Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of
participation with its varying quality of commentary, extracting value out of
such data ... | computer science |
16,346 | A Data-Oriented Model of Literary Language | cs.CL | We consider the task of predicting how literary a text is, with a gold
standard from human ratings. Aside from a standard bigram baseline, we apply
rich syntactic tree fragments, mined from the training set, and a series of
hand-picked features. Our model is the first to distinguish degrees of highly
and less literary ... | computer science |
16,347 | LanideNN: Multilingual Language Identification on Character Window | cs.CL | In language identification, a common first step in natural language
processing, we want to automatically determine the language of some input text.
Monolingual language identification assumes that the given document is written
in one language. In multilingual language identification, the document is
usually in two or t... | computer science |
16,348 | SMARTies: Sentiment Models for Arabic Target Entities | cs.CL | We consider entity-level sentiment analysis in Arabic, a morphologically rich
language with increasing resources. We present a system that is applied to
complex posts written in response to Arabic newspaper articles. Our goal is to
identify important entity "targets" within the post along with the polarity
expressed ab... | computer science |
16,349 | Deep Neural Networks for Czech Multi-label Document Classification | cs.CL | This paper is focused on automatic multi-label document classification of
Czech text documents. The current approaches usually use some pre-processing
which can have negative impact (loss of information, additional implementation
work, etc). Therefore, we would like to omit it and use deep neural networks
that learn fr... | computer science |
16,350 | QCRI Machine Translation Systems for IWSLT 16 | cs.CL | This paper describes QCRI's machine translation systems for the IWSLT 2016
evaluation campaign. We participated in the Arabic->English and English->Arabic
tracks. We built both Phrase-based and Neural machine translation models, in an
effort to probe whether the newly emerged NMT framework surpasses the
traditional phr... | computer science |
16,351 | Neural Models for Sequence Chunking | cs.CL | Many natural language understanding (NLU) tasks, such as shallow parsing
(i.e., text chunking) and semantic slot filling, require the assignment of
representative labels to the meaningful chunks in a sentence. Most of the
current deep neural network (DNN) based methods consider these tasks as a
sequence labeling proble... | computer science |
16,352 | Dialog Context Language Modeling with Recurrent Neural Networks | cs.CL | In this work, we propose contextual language models that incorporate dialog
level discourse information into language modeling. Previous works on
contextual language model treat preceding utterances as a sequence of inputs,
without considering dialog interactions. We design recurrent neural network
(RNN) based contextu... | computer science |
16,353 | Deep Memory Networks for Attitude Identification | cs.CL | We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exa... | computer science |
16,354 | Machine Translation Approaches and Survey for Indian Languages | cs.CL | In this study, we present an analysis regarding the performance of the
state-of-art Phrase-based Statistical Machine Translation (SMT) on multiple
Indian languages. We report baseline systems on several language pairs. The
motivation of this study is to promote the development of SMT and linguistic
resources for these ... | computer science |
16,355 | Assessing User Expertise in Spoken Dialog System Interactions | cs.CL | Identifying the level of expertise of its users is important for a system
since it can lead to a better interaction through adaptation techniques.
Furthermore, this information can be used in offline processes of root cause
analysis. However, not much effort has been put into automatically identifying
the level of expe... | computer science |
16,356 | A Joint Framework for Argumentative Text Analysis Incorporating Domain
Knowledge | cs.CL | For argumentation mining, there are several sub-tasks such as argumentation
component type classification, relation classification. Existing research tends
to solve such sub-tasks separately, but ignore the close relation between them.
In this paper, we present a joint framework incorporating logical relation
between s... | computer science |
16,357 | Harnessing Cognitive Features for Sarcasm Detection | cs.CL | In this paper, we propose a novel mechanism for enriching the feature vector,
for the task of sarcasm detection, with cognitive features extracted from
eye-movement patterns of human readers. Sarcasm detection has been a
challenging research problem, and its importance for NLP applications such as
review summarization,... | computer science |
16,358 | Leveraging Cognitive Features for Sentiment Analysis | cs.CL | Sentiments expressed in user-generated short text and sentences are nuanced
by subtleties at lexical, syntactic, semantic and pragmatic levels. To address
this, we propose to augment traditional features used for sentiment analysis
and sarcasm detection, with cognitive features derived from the eye-movement
patterns of... | computer science |
16,359 | CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation | cs.CL | While the general analysis of named entities has received substantial
research attention, the analysis of relations over named entities has not. In
fact, a review of the literature on unstructured as well as structured data
revealed a deficiency in research on the abstract conceptualization required to
organize relatio... | computer science |
16,360 | Incorporating Global Visual Features into Attention-Based Neural Machine
Translation | cs.CL | We introduce multi-modal, attention-based neural machine translation (NMT)
models which incorporate visual features into different parts of both the
encoder and the decoder. We utilise global image features extracted using a
pre-trained convolutional neural network and incorporate them (i) as words in
the source senten... | computer science |
16,361 | Adversarial Learning for Neural Dialogue Generation | cs.CL | In this paper, drawing intuition from the Turing test, we propose using
adversarial training for open-domain dialogue generation: the system is trained
to produce sequences that are indistinguishable from human-generated dialogue
utterances. We cast the task as a reinforcement learning (RL) problem where we
jointly tra... | computer science |
16,362 | Learning to Decode for Future Success | cs.CL | We introduce a simple, general strategy to manipulate the behavior of a
neural decoder that enables it to generate outputs that have specific
properties of interest (e.g., sequences of a pre-specified length). The model
can be thought of as a simple version of the actor-critic model that uses an
interpolation of the ac... | computer science |
16,363 | Hierarchical Recurrent Attention Network for Response Generation | cs.CL | We study multi-turn response generation in chatbots where a response is
generated according to a conversation context. Existing work has modeled the
hierarchy of the context, but does not pay enough attention to the fact that
words and utterances in the context are differentially important. As a result,
they may lose i... | computer science |
16,364 | emLam -- a Hungarian Language Modeling baseline | cs.CL | This paper aims to make up for the lack of documented baselines for Hungarian
language modeling. Various approaches are evaluated on three publicly available
Hungarian corpora. Perplexity values comparable to models of similar-sized
English corpora are reported. A new, freely downloadable Hungar- ian benchmark
corpus i... | computer science |
16,365 | Emotion Recognition From Speech With Recurrent Neural Networks | cs.CL | In this paper the task of emotion recognition from speech is considered.
Proposed approach uses deep recurrent neural network trained on a sequence of
acoustic features calculated over small speech intervals. At the same time
special probabilistic-nature CTC loss function allows to consider long
utterances containing b... | computer science |
16,366 | Measuring the Reliability of Hate Speech Annotations: The Case of the
European Refugee Crisis | cs.CL | Some users of social media are spreading racist, sexist, and otherwise
hateful content. For the purpose of training a hate speech detection system,
the reliability of the annotations is crucial, but there is no universally
agreed-upon definition. We collected potentially hateful messages and asked two
groups of interne... | computer science |
16,367 | Adversarial Evaluation of Dialogue Models | cs.CL | The recent application of RNN encoder-decoder models has resulted in
substantial progress in fully data-driven dialogue systems, but evaluation
remains a challenge. An adversarial loss could be a way to directly evaluate
the extent to which generated dialogue responses sound like they came from a
human. This could redu... | computer science |
16,368 | Drug-Drug Interaction Extraction from Biomedical Text Using Long Short
Term Memory Network | cs.CL | Simultaneous administration of multiple drugs can have synergistic or
antagonistic effects as one drug can affect activities of other drugs.
Synergistic effects lead to improved therapeutic outcomes, whereas,
antagonistic effects can be life-threatening, may lead to increased healthcare
cost, or may even cause death. T... | computer science |
16,369 | Using English as Pivot to Extract Persian-Italian Parallel Sentences
from Non-Parallel Corpora | cs.CL | The effectiveness of a statistical machine translation system (SMT) is very
dependent upon the amount of parallel corpus used in the training phase. For
low-resource language pairs there are not enough parallel corpora to build an
accurate SMT. In this paper, a novel approach is presented to extract bilingual
Persian-I... | computer science |
16,370 | Extracting Bilingual Persian Italian Lexicon from Comparable Corpora
Using Different Types of Seed Dictionaries | cs.CL | Bilingual dictionaries are very important in various fields of natural
language processing. In recent years, research on extracting new bilingual
lexicons from non-parallel (comparable) corpora have been proposed. Almost all
use a small existing dictionary or other resource to make an initial list
called the "seed dict... | computer science |
16,371 | Graph-Based Semi-Supervised Conditional Random Fields For Spoken
Language Understanding Using Unaligned Data | cs.CL | We experiment graph-based Semi-Supervised Learning (SSL) of Conditional
Random Fields (CRF) for the application of Spoken Language Understanding (SLU)
on unaligned data. The aligned labels for examples are obtained using IBM
Model. We adapt a baseline semi-supervised CRF by defining new feature set and
altering the lab... | computer science |
16,372 | Structural Analysis of Hindi Phonetics and A Method for Extraction of
Phonetically Rich Sentences from a Very Large Hindi Text Corpus | cs.CL | Automatic speech recognition (ASR) and Text to speech (TTS) are two prominent
area of research in human computer interaction nowadays. A set of phonetically
rich sentences is in a matter of importance in order to develop these two
interactive modules of HCI. Essentially, the set of phonetically rich sentences
has to co... | computer science |
16,373 | Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language
Model | cs.CL | In this paper, we describe a research method that generates Bangla word
clusters on the basis of relating to meaning in language and contextual
similarity. The importance of word clustering is in parts of speech (POS)
tagging, word sense disambiguation, text classification, recommender system,
spell checker, grammar ch... | computer science |
16,374 | SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text | cs.CL | Use of social media has grown dramatically during the last few years. Users
follow informal languages in communicating through social media. The language
of communication is often mixed in nature, where people transcribe their
regional language with English and this technique is found to be extremely
popular. Natural l... | computer science |
16,375 | AMR-to-text Generation with Synchronous Node Replacement Grammar | cs.CL | This paper addresses the task of AMR-to-text generation by leveraging
synchronous node replacement grammar. During training, graph-to-string rules
are learned using a heuristic extraction algorithm. At test time, a graph
transducer is applied to collapse input AMRs and generate output sentences.
Evaluated on SemEval-20... | computer science |
16,376 | Analysing Temporal Evolution of Interlingual Wikipedia Article Pairs | cs.CL | Wikipedia articles representing an entity or a topic in different language
editions evolve independently within the scope of the language-specific user
communities. This can lead to different points of views reflected in the
articles, as well as complementary and inconsistent information. An analysis of
how the informa... | computer science |
16,377 | Symbolic, Distributed and Distributional Representations for Natural
Language Processing in the Era of Deep Learning: a Survey | cs.CL | Natural language and symbols are intimately correlated. Recent advances in
machine learning (ML) and in natural language processing (NLP) seem to
contradict the above intuition: symbols are fading away, erased by vectors or
tensors called distributed and distributional representations. However, there
is a strict link b... | computer science |
16,378 | Automatic Prediction of Discourse Connectives | cs.CL | Accurate prediction of suitable discourse connectives (however, furthermore,
etc.) is a key component of any system aimed at building coherent and fluent
discourses from shorter sentences and passages. As an example, a dialog system
might assemble a long and informative answer by sampling passages extracted
from differ... | computer science |
16,379 | Multilingual Multi-modal Embeddings for Natural Language Processing | cs.CL | We propose a novel discriminative model that learns embeddings from
multilingual and multi-modal data, meaning that our model can take advantage of
images and descriptions in multiple languages to improve embedding quality. To
that end, we introduce a modification of a pairwise contrastive estimation
optimisation funct... | computer science |
16,380 | Predicting Target Language CCG Supertags Improves Neural Machine
Translation | cs.CL | Neural machine translation (NMT) models are able to partially learn syntactic
information from sequential lexical information. Still, some complex syntactic
phenomena such as prepositional phrase attachment are poorly modeled. This work
aims to answer two questions: 1) Does explicitly modeling target language
syntax he... | computer science |
16,381 | Doubly-Attentive Decoder for Multi-modal Neural Machine Translation | cs.CL | We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and p... | computer science |
16,382 | An Empirical Evaluation of Zero Resource Acoustic Unit Discovery | cs.CL | Acoustic unit discovery (AUD) is a process of automatically identifying a
categorical acoustic unit inventory from speech and producing corresponding
acoustic unit tokenizations. AUD provides an important avenue for unsupervised
acoustic model training in a zero resource setting where expert-provided
linguistic knowled... | computer science |
16,383 | Prepositions in Context | cs.CL | Prepositions are highly polysemous, and their variegated senses encode
significant semantic information. In this paper we match each preposition's
complement and attachment and their interplay crucially to the geometry of the
word vectors to the left and right of the preposition. Extracting such features
from the vast ... | computer science |
16,384 | Opinion Recommendation using Neural Memory Model | cs.CL | We present opinion recommendation, a novel task of jointly predicting a
custom review with a rating score that a certain user would give to a certain
product or service, given existing reviews and rating scores to the product or
service by other users, and the reviews that the user has given to other
products and servi... | computer science |
16,385 | Neural Semantic Parsing over Multiple Knowledge-bases | cs.CL | A fundamental challenge in developing semantic parsers is the paucity of
strong supervision in the form of language utterances annotated with logical
form. In this paper, we propose to exploit structural regularities in language
in different domains, and train semantic parsers over multiple knowledge-bases
(KBs), while... | computer science |
16,386 | A Hybrid Approach For Hindi-English Machine Translation | cs.CL | In this paper, an extended combined approach of phrase based statistical
machine translation (SMT), example based MT (EBMT) and rule based MT (RBMT) is
proposed to develop a novel hybrid data driven MT system capable of
outperforming the baseline SMT, EBMT and RBMT systems from which it is derived.
In short, the propos... | computer science |
16,387 | Q-WordNet PPV: Simple, Robust and (almost) Unsupervised Generation of
Polarity Lexicons for Multiple Languages | cs.CL | This paper presents a simple, robust and (almost) unsupervised
dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector)
to automatically generate polarity lexicons. We show that qwn-ppv outperforms
other automatically generated lexicons for the four extrinsic evaluations
presented here. It also s... | computer science |
16,388 | DNN adaptation by automatic quality estimation of ASR hypotheses | cs.CL | In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are current... | computer science |
16,389 | Multi-task memory networks for category-specific aspect and opinion
terms co-extraction | cs.CL | In aspect-based sentiment analysis, most existing methods either focus on
aspect/opinion terms extraction or aspect terms categorization. However, each
task by itself only provides partial information to end users. To generate more
detailed and structured opinion analysis, we propose a finer-grained problem,
which we c... | computer science |
16,390 | Ensemble Distillation for Neural Machine Translation | cs.CL | Knowledge distillation describes a method for training a student network to
perform better by learning from a stronger teacher network. Translating a
sentence with an Neural Machine Translation (NMT) engine is time expensive and
having a smaller model speeds up this process. We demonstrate how to transfer
the translati... | computer science |
16,391 | Beam Search Strategies for Neural Machine Translation | cs.CL | The basic concept in Neural Machine Translation (NMT) is to train a large
Neural Network that maximizes the translation performance on a given parallel
corpus. NMT is then using a simple left-to-right beam-search decoder to
generate new translations that approximately maximize the trained conditional
probability. The c... | computer science |
16,392 | Living a discrete life in a continuous world: Reference with distributed
representations | cs.CL | Reference is a crucial property of language that allows us to connect
linguistic expressions to the world. Modeling it requires handling both
continuous and discrete aspects of meaning. Data-driven models excel at the
former, but struggle with the latter, and the reverse is true for symbolic
models.
This paper (a) in... | computer science |
16,393 | The Effect of Different Writing Tasks on Linguistic Style: A Case Study
of the ROC Story Cloze Task | cs.CL | A writer's style depends not just on personal traits but also on her intent
and mental state. In this paper, we show how variants of the same writing task
can lead to measurable differences in writing style. We present a case study
based on the story cloze task (Mostafazadeh et al., 2016a), where annotators
were assign... | computer science |
16,394 | Comparative Study of CNN and RNN for Natural Language Processing | cs.CL | Deep neural networks (DNN) have revolutionized the field of natural language
processing (NLP). Convolutional neural network (CNN) and recurrent neural
network (RNN), the two main types of DNN architectures, are widely explored to
handle various NLP tasks. CNN is supposed to be good at extracting
position-invariant feat... | computer science |
16,395 | A Knowledge-Grounded Neural Conversation Model | cs.CL | Neural network models are capable of generating extremely natural sounding
conversational interactions. Nevertheless, these models have yet to demonstrate
that they can incorporate content in the form of factual information or
entity-grounded opinion that would enable them to serve in more task-oriented
conversational ... | computer science |
16,396 | EliXa: A Modular and Flexible ABSA Platform | cs.CL | This paper presents a supervised Aspect Based Sentiment Analysis (ABSA)
system. Our aim is to develop a modular platform which allows to easily conduct
experiments by replacing the modules or adding new features. We obtain the best
result in the Opinion Target Extraction (OTE) task (slot 2) using an
off-the-shelf seque... | computer science |
16,397 | Fast and Accurate Entity Recognition with Iterated Dilated Convolutions | cs.CL | Today when many practitioners run basic NLP on the entire web and
large-volume traffic, faster methods are paramount to saving time and energy
costs. Recent advances in GPU hardware have led to the emergence of
bi-directional LSTMs as a standard method for obtaining per-token vector
representations serving as input to ... | computer science |
16,398 | How to evaluate word embeddings? On importance of data efficiency and
simple supervised tasks | cs.CL | Maybe the single most important goal of representation learning is making
subsequent learning faster. Surprisingly, this fact is not well reflected in
the way embeddings are evaluated. In addition, recent practice in word
embeddings points towards importance of learning specialized representations.
We argue that focus ... | computer science |
16,399 | Question Answering through Transfer Learning from Large Fine-grained
Supervision Data | cs.CL | We show that the task of question answering (QA) can significantly benefit
from the transfer learning of models trained on a different large, fine-grained
QA dataset. We achieve the state of the art in two well-studied QA datasets,
WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique
from SQuA... | computer science |
16,400 | Fixing the Infix: Unsupervised Discovery of Root-and-Pattern Morphology | cs.CL | We present an unsupervised and language-agnostic method for learning
root-and-pattern morphology in Semitic languages. This form of morphology,
abundant in Semitic languages, has not been handled in prior unsupervised
approaches. We harness the syntactico-semantic information in distributed word
representations to solv... | computer science |
16,401 | MORSE: Semantic-ally Drive-n MORpheme SEgment-er | cs.CL | We present in this paper a novel framework for morpheme segmentation which
uses the morpho-syntactic regularities preserved by word representations, in
addition to orthographic features, to segment words into morphemes. This
framework is the first to consider vocabulary-wide syntactico-semantic
information for this tas... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.