Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
17,102 | On the Contribution of Discourse Structure on Text Complexity Assessment | cs.CL | This paper investigates the influence of discourse features on text
complexity assessment. To do so, we created two data sets based on the Penn
Discourse Treebank and the Simple English Wikipedia corpora and compared the
influence of coherence, cohesion, surface, lexical and syntactic features to
assess text complexity... | computer science |
17,103 | ClaC: Semantic Relatedness of Words and Phrases | cs.CL | The measurement of phrasal semantic relatedness is an important metric for
many natural language processing applications. In this paper, we present three
approaches for measuring phrasal semantics, one based on a semantic network
model, another on a distributional similarity model, and a hybrid between the
two. Our hyb... | computer science |
17,104 | Measuring the Effect of Discourse Relations on Blog Summarization | cs.CL | The work presented in this paper attempts to evaluate and quantify the use of
discourse relations in the context of blog summarization and compare their use
to more traditional and factual texts. Specifically, we measured the usefulness
of 6 discourse relations - namely comparison, contingency, illustration,
attributio... | computer science |
17,105 | The CLaC Discourse Parser at CoNLL-2015 | cs.CL | This paper describes our submission (kosseim15) to the CoNLL-2015 shared task
on shallow discourse parsing. We used the UIMA framework to develop our parser
and used ClearTK to add machine learning functionality to the UIMA framework.
Overall, our parser achieves a result of 17.3 F1 on the identification of
discourse r... | computer science |
17,106 | Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM | cs.CL | Arabic word segmentation is essential for a variety of NLP applications such
as machine translation and information retrieval. Segmentation entails breaking
words into their constituent stems, affixes and clitics. In this paper, we
compare two approaches for segmenting four major Arabic dialects using only
several thou... | computer science |
17,107 | The Helsinki Neural Machine Translation System | cs.CL | We introduce the Helsinki Neural Machine Translation system (HNMT) and how it
is applied in the news translation task at WMT 2017, where it ranked first in
both the human and automatic evaluations for English--Finnish. We discuss the
success of English--Finnish translations and the overall advantage of NMT over
a stron... | computer science |
17,108 | Neural Machine Translation with Extended Context | cs.CL | We investigate the use of extended context in attention-based neural machine
translation. We base our experiments on translated movie subtitles and discuss
the effect of increasing the segments beyond single translation units. We study
the use of extended source language context as well as bilingual context
extensions.... | computer science |
17,109 | An End-to-End Trainable Neural Network Model with Belief Tracking for
Task-Oriented Dialog | cs.CL | We present a novel end-to-end trainable neural network model for
task-oriented dialog systems. The model is able to track dialog state, issue
API calls to knowledge base (KB), and incorporate structured KB query results
into system responses to successfully complete task-oriented dialogs. The
proposed model produces we... | computer science |
17,110 | LSTM Network for Inflected Abbreviation Expansion | cs.CL | In this paper, the problem of recovery of morphological information lost in
abbreviated forms is addressed with a focus on highly inflected languages.
Evidence is presented that the correct inflected form of an expanded
abbreviation can in many cases be deduced solely from morphosyntactic tags of
the context. The predi... | computer science |
17,111 | Learning to Paraphrase for Question Answering | cs.CL | Question answering (QA) systems are sensitive to the many different ways
natural language expresses the same information need. In this paper we turn to
paraphrases as a means of capturing this knowledge and present a general
framework which learns felicitous paraphrases for various QA tasks. Our method
is trained end-t... | computer science |
17,112 | Portuguese Word Embeddings: Evaluating on Word Analogies and Natural
Language Tasks | cs.CL | Word embeddings have been found to provide meaningful representations for
words in an efficient way; therefore, they have become common in Natural
Language Processing sys- tems. In this paper, we evaluated different word
embedding models trained on a large Portuguese corpus, including both Brazilian
and European varian... | computer science |
17,113 | Vector Space Model as Cognitive Space for Text Classification | cs.CL | In this era of digitization, knowing the user's sociolect aspects have become
essential features to build the user specific recommendation systems. These
sociolect aspects could be found by mining the user's language sharing in the
form of text in social media and reviews. This paper describes about the
experiment that... | computer science |
17,114 | The Microsoft 2017 Conversational Speech Recognition System | cs.CL | We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acousti... | computer science |
17,115 | Scientific Information Extraction with Semi-supervised Neural Tagging | cs.CL | This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition.... | computer science |
17,116 | Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator | cs.CL | The paper describes experiments on estimating emotion intensity in tweets
using a generalized regressor system. The system combines lexical, syntactic
and pre-trained word embedding features, trains them on general regressors and
finally combines the best performing models to create an ensemble. The proposed
system sto... | computer science |
17,117 | Cold Fusion: Training Seq2Seq Models Together with Language Models | cs.CL | Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks
which involve generating natural language sentences such as machine
translation, image captioning and speech recognition. Performance has further
been improved by leveraging unlabeled data, often in the form of a language
model. In this work, w... | computer science |
17,118 | Handling Homographs in Neural Machine Translation | cs.CL | Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account glo... | computer science |
17,119 | A rule based algorithm for detecting negative words in Persian | cs.CL | In this paper, we present a novel method for detecting negative words in
Persian. We first used an algorithm to an exceptions list which was later
modified by hand. We then used the mentioned lists and a Persian polarity
corpus in our rule based algorithm to detect negative words. | computer science |
17,120 | Automatic Detection of Fake News | cs.CL | The proliferation of misleading information in everyday access media outlets
such as social media feeds, news blogs, and online newspapers have made it
challenging to identify trustworthy news sources, thus increasing the need for
computational tools able to provide insights into the reliability of online
content. In t... | computer science |
17,121 | NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit | cs.CL | This paper demonstrates neural network-based toolkit namely NNVLP for
essential Vietnamese language processing tasks including part-of-speech (POS)
tagging, chunking, named entity recognition (NER). Our toolkit is a combination
of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
(CNN), Condi... | computer science |
17,122 | Combining Discrete and Neural Features for Sequence Labeling | cs.CL | Neural network models have recently received heated research attention in the
natural language processing community. Compared with traditional models with
discrete features, neural models have two main advantages. First, they take
low-dimensional, real-valued embedding vectors as inputs, which can be trained
over large... | computer science |
17,123 | CloudScan - A configuration-free invoice analysis system using recurrent
neural networks | cs.CL | We present CloudScan; an invoice analysis system that requires zero
configuration or upfront annotation. In contrast to previous work, CloudScan
does not rely on templates of invoice layout, instead it learns a single global
model of invoices that naturally generalizes to unseen invoice layouts. The
model is trained us... | computer science |
17,124 | M2D: Monolog to Dialog Generation for Conversational Story Telling | cs.CL | Storytelling serves many different social functions, e.g. stories are used to
persuade, share troubles, establish shared values, learn social behaviors, and
entertain. Moreover, stories are often told conversationally through dialog,
and previous work suggests that information provided dialogically is more
engaging tha... | computer science |
17,125 | Revisiting the Centroid-based Method: A Strong Baseline for
Multi-Document Summarization | cs.CL | The centroid-based model for extractive document summarization is a simple
and fast baseline that ranks sentences based on their similarity to a centroid
vector. In this paper, we apply this ranking to possible summaries instead of
sentences and use a simple greedy algorithm to find the best summary.
Furthermore, we sh... | computer science |
17,126 | MTIL17: English to Indian Langauge Statistical Machine Translation | cs.CL | English to Indian language machine translation poses the challenge of
structural and morphological divergence. This paper describes English to Indian
language statistical machine translation using pre-ordering and suffix
separation. The pre-ordering uses rules to transfer the structure of the source
sentences prior to ... | computer science |
17,127 | Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank | cs.CL | Discourse parsing has long been treated as a stand-alone problem independent
from constituency or dependency parsing. Most attempts at this problem are
pipelined rather than end-to-end, sophisticated, and not self-contained: they
assume gold-standard text segmentations (Elementary Discourse Units), and use
external par... | computer science |
17,128 | Really? Well. Apparently Bootstrapping Improves the Performance of
Sarcasm and Nastiness Classifiers for Online Dialogue | cs.CL | More and more of the information on the web is dialogic, from Facebook
newsfeeds, to forum conversations, to comment threads on news articles. In
contrast to traditional, monologic Natural Language Processing resources such
as news, highly social dialogue is frequent in social media, making it a
challenging context for... | computer science |
17,129 | Generating Different Story Tellings from Semantic Representations of
Narrative | cs.CL | In order to tell stories in different voices for different audiences,
interactive story systems require: (1) a semantic representation of story
structure, and (2) the ability to automatically generate story and dialogue
from this semantic representation using some form of Natural Language
Generation (NLG). However, the... | computer science |
17,130 | Identifying Subjective and Figurative Language in Online Dialogue | cs.CL | More and more of the information on the web is dialogic, from Facebook
newsfeeds, to forum conversations, to comment threads on news articles. In
contrast to traditional, monologic resources such as news, highly social
dialogue is very frequent in social media. We aim to automatically identify
sarcastic and nasty utter... | computer science |
17,131 | Generating Sentence Planning Variations for Story Telling | cs.CL | There has been a recent explosion in applications for dialogue interaction
ranging from direction-giving and tourist information to interactive story
systems. Yet the natural language generation (NLG) component for many of these
systems remains largely handcrafted. This limitation greatly restricts the
range of applica... | computer science |
17,132 | Narrative Variations in a Virtual Storyteller | cs.CL | Research on storytelling over the last 100 years has distinguished at least
two levels of narrative representation (1) story, or fabula; and (2) discourse,
or sujhet. We use this distinction to create Fabula Tales, a computational
framework for a virtual storyteller that can tell the same story in different
ways throug... | computer science |
17,133 | Comparing Human and Machine Errors in Conversational Speech
Transcription | cs.CL | Recent work in automatic recognition of conversational telephone speech (CTS)
has achieved accuracy levels comparable to human transcribers, although there
is some debate how to precisely quantify human performance on this task, using
the NIST 2000 CTS evaluation set. This raises the question what systematic
difference... | computer science |
17,134 | Neural Machine Translation Training in a Multi-Domain Scenario | cs.CL | In this paper, we explore alternative ways to train a neural machine
translation system in a multi-domain scenario. We investigate data
concatenation (with fine tuning), model stacking (multi-level fine tuning),
data selection and weighted ensemble. We evaluate these methods based on three
criteria: i) translation qual... | computer science |
17,135 | A Simple LSTM model for Transition-based Dependency Parsing | cs.CL | We present a simple LSTM-based transition-based dependency parser. Our model
is composed of a single LSTM hidden layer replacing the hidden layer in the
usual feed-forward network architecture. We also propose a new initialization
method that uses the pre-trained weights from a feed-forward neural network to
initialize... | computer science |
17,136 | PersonaBank: A Corpus of Personal Narratives and Their Story Intention
Graphs | cs.CL | We present a new corpus, PersonaBank, consisting of 108 personal stories from
weblogs that have been annotated with their Story Intention Graphs, a deep
representation of the fabula of a story. We describe the topics of the stories
and the basis of the Story Intention Graph representation, as well as the
process of ann... | computer science |
17,137 | Argument Strength is in the Eye of the Beholder: Audience Effects in
Persuasion | cs.CL | Americans spend about a third of their time online, with many participating
in online conversations on social and political issues. We hypothesize that
social media arguments on such issues may be more engaging and persuasive than
traditional media summaries, and that particular types of people may be more or
less conv... | computer science |
17,138 | Automating Direct Speech Variations in Stories and Games | cs.CL | Dialogue authoring in large games requires not only content creation but the
subtlety of its delivery, which can vary from character to character. Manually
authoring this dialogue can be tedious, time-consuming, or even altogether
infeasible. This paper utilizes a rich narrative representation for modeling
dialogue and... | computer science |
17,139 | Paradigm Completion for Derivational Morphology | cs.CL | The generation of complex derived word forms has been an overlooked problem
in NLP; we fill this gap by applying neural sequence-to-sequence models to the
task. We overview the theoretical motivation for a paradigmatic treatment of
derivational morphology, and introduce the task of derivational paradigm
completion as a... | computer science |
17,140 | Cross-lingual, Character-Level Neural Morphological Tagging | cs.CL | Even for common NLP tasks, sufficient supervision is not available in many
languages -- morphological tagging is no exception. In the work presented here,
we explore a transfer learning scheme, whereby we train character-level
recurrent neural taggers to predict morphological taggings for high-resource
languages and lo... | computer science |
17,141 | An Empirical Study of Discriminative Sequence Labeling Models for
Vietnamese Text Processing | cs.CL | This paper presents an empirical study of two widely-used sequence prediction
models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks
(LSTMs), on two fundamental tasks for Vietnamese text processing, including
part-of-speech tagging and named entity recognition. We show that a strong
lower bound fo... | computer science |
17,142 | Look-ahead Attention for Generation in Neural Machine Translation | cs.CL | The attention model has become a standard component in neural machine
translation (NMT) and it guides translation process by selectively focusing on
parts of the source sentence when predicting each target word. However, we find
that the generation of a target word does not only depend on the source
sentence, but also ... | computer science |
17,143 | TANKER: Distributed Architecture for Named Entity Recognition and
Disambiguation | cs.CL | Named Entity Recognition and Disambiguation (NERD) systems have recently been
widely researched to deal with the significant growth of the Web. NERD systems
are crucial for several Natural Language Processing (NLP) tasks such as
summarization, understanding, and machine translation. However, there is no
standard interf... | computer science |
17,144 | Fighting with the Sparsity of Synonymy Dictionaries | cs.CL | Graph-based synset induction methods, such as MaxMax and Watset, induce
synsets by performing a global clustering of a synonymy graph. However, such
methods are sensitive to the structure of the input synonymy graph: sparseness
of the input dictionary can substantially reduce the quality of the extracted
synsets. In th... | computer science |
17,145 | Fast(er) Exact Decoding and Global Training for Transition-Based
Dependency Parsing via a Minimal Feature Set | cs.CL | We first present a minimal feature set for transition-based dependency
parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a)
and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our
minimal feature set into the dynamic-programming framework of Huang and Sagae
(2010) and Ku... | computer science |
17,146 | LangPro: Natural Language Theorem Prover | cs.CL | LangPro is an automated theorem prover for natural language
(https://github.com/kovvalsky/LangPro). Given a set of premises and a
hypothesis, it is able to prove semantic relations between them. The prover is
based on a version of analytic tableau method specially designed for natural
logic. The proof procedure operate... | computer science |
17,147 | Inferring Narrative Causality between Event Pairs in Films | cs.CL | To understand narrative, humans draw inferences about the underlying
relations between narrative events. Cognitive theories of narrative
understanding define these inferences as four different types of causality,
that include pairs of events A, B where A physically causes B (X drop, X
break), to pairs of events where A... | computer science |
17,148 | Unsupervised Induction of Contingent Event Pairs from Film Scenes | cs.CL | Human engagement in narrative is partially driven by reasoning about
discourse relations between narrative events, and the expectations about what
is likely to happen next that results from such reasoning. Researchers in NLP
have tackled modeling such expectations from a range of perspectives, including
treating it as ... | computer science |
17,149 | Identifying Products in Online Cybercrime Marketplaces: A Dataset for
Fine-grained Domain Adaptation | cs.CL | One weakness of machine-learned NLP models is that they typically perform
poorly on out-of-domain data. In this work, we study the task of identifying
products being bought and sold in online cybercrime forums, which exhibits
particularly challenging cross-domain effects. We formulate a task that
represents a hybrid of... | computer science |
17,150 | Human and Machine Judgements for Russian Semantic Relatedness | cs.CL | Semantic relatedness of terms represents similarity of meaning by a numerical
score. On the one hand, humans easily make judgments about semantic
relatedness. On the other hand, this kind of information is useful in language
processing systems. While semantic relatedness has been extensively studied for
English using n... | computer science |
17,151 | Learning Lexico-Functional Patterns for First-Person Affect | cs.CL | Informal first-person narratives are a unique resource for computational
models of everyday events and people's affective reactions to them. People
blogging about their day tend not to explicitly say I am happy. Instead they
describe situations from which other humans can readily infer their affective
reactions. Howeve... | computer science |
17,152 | Transfer Learning across Low-Resource, Related Languages for Neural
Machine Translation | cs.CL | We present a simple method to improve neural translation of a low-resource
language pair using parallel data from a related, also low-resource, language
pair. The method is based on the transfer method of Zoph et al., but whereas
their method ignores any source vocabulary overlap, ours exploits it. First, we
split word... | computer science |
17,153 | Glyph-aware Embedding of Chinese Characters | cs.CL | Given the advantage and recent success of English character-level and
subword-unit models in several NLP tasks, we consider the equivalent modeling
problem for Chinese. Chinese script is logographic and many Chinese logograms
are composed of common substructures that provide semantic, phonetic and
syntactic hints. In t... | computer science |
17,154 | Linguistic Reflexes of Well-Being and Happiness in Echo | cs.CL | Different theories posit different sources for feelings of well-being and
happiness. Appraisal theory grounds our emotional responses in our goals and
desires and their fulfillment, or lack of fulfillment. Self Determination
theory posits that the basis for well-being rests on our assessment of our
competence, autonomy... | computer science |
17,155 | Variational Inference for Logical Inference | cs.CL | Functional Distributional Semantics is a framework that aims to learn, from
text, semantic representations which can be interpreted in terms of truth. Here
we make two contributions to this framework. The first is to show how a type of
logical inference can be performed by evaluating conditional probabilities. The
seco... | computer science |
17,156 | Semantic Composition via Probabilistic Model Theory | cs.CL | Semantic composition remains an open problem for vector space models of
semantics. In this paper, we explain how the probabilistic graphical model used
in the framework of Functional Distributional Semantics can be interpreted as a
probabilistic version of model theory. Building on this, we explain how various
semantic... | computer science |
17,157 | Arc-Standard Spinal Parsing with Stack-LSTMs | cs.CL | We present a neural transition-based parser for spinal trees, a dependency
representation of constituent trees. The parser uses Stack-LSTMs that compose
constituent nodes with dependency-based derivations. In experiments, we show
that this model adapts to different styles of dependency relations, but this
choice has li... | computer science |
17,158 | Challenging Language-Dependent Segmentation for Arabic: An Application
to Machine Translation and Part-of-Speech Tagging | cs.CL | Word segmentation plays a pivotal role in improving any Arabic NLP
application. Therefore, a lot of research has been spent in improving its
accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii)
domain/dialect dependent. We explore three language-independent alternatives to
morphological segmentati... | computer science |
17,159 | Investigating how well contextual features are captured by
bi-directional recurrent neural network models | cs.CL | Learning algorithms for natural language processing (NLP) tasks traditionally
rely on manually defined relevant contextual features. On the other hand,
neural network models using an only distributional representation of words have
been successfully applied for several NLP tasks. Such models learn features
automaticall... | computer science |
17,160 | Disentangling ASR and MT Errors in Speech Translation | cs.CL | The main aim of this paper is to investigate automatic quality assessment for
spoken language translation (SLT). More precisely, we investigate SLT errors
that can be due to transcription (ASR) or to translation (MT) modules. This
paper investigates automatic detection of SLT errors using a single classifier
based on j... | computer science |
17,161 | From Review to Rating: Exploring Dependency Measures for Text
Classification | cs.CL | Various text analysis techniques exist, which attempt to uncover unstructured
information from text. In this work, we explore using statistical dependence
measures for textual classification, representing text as word vectors. Student
satisfaction scores on a 3-point scale and their free text comments written
about uni... | computer science |
17,162 | Hypothesis Testing based Intrinsic Evaluation of Word Embeddings | cs.CL | We introduce the cross-match test - an exact, distribution free,
high-dimensional hypothesis test as an intrinsic evaluation metric for word
embeddings. We show that cross-match is an effective means of measuring
distributional similarity between different vector representations and of
evaluating the statistical signif... | computer science |
17,163 | Getting Reliable Annotations for Sarcasm in Online Dialogues | cs.CL | The language used in online forums differs in many ways from that of
traditional language resources such as news. One difference is the use and
frequency of nonliteral, subjective dialogue acts such as sarcasm. Whether the
aim is to develop a theory of sarcasm in dialogue, or engineer automatic
methods for reliably det... | computer science |
17,164 | A Unified Query-based Generative Model for Question Generation and
Question Answering | cs.CL | We propose a query-based generative model for solving both tasks of question
generation (QG) and question an- swering (QA). The model follows the classic
encoder- decoder framework. The encoder takes a passage and a query as input
then performs query understanding by matching the query with the passage from
multiple pe... | computer science |
17,165 | Do latent tree learning models identify meaningful structure in
sentences? | cs.CL | Recent work on the problem of latent tree learning has made it possible to
train neural networks that learn to both parse a sentence and use the resulting
parse to interpret the sentence, all without exposure to ground-truth parse
trees at training time. Surprisingly, these models often perform better at
sentence under... | computer science |
17,166 | Learning Neural Word Salience Scores | cs.CL | Measuring the salience of a word is an essential step in numerous NLP tasks.
Heuristic approaches such as tfidf have been used so far to estimate the
salience of words. We propose \emph{Neural Word Salience} (NWS) scores, unlike
heuristics, are learnt from a corpus. Specifically, we learn word salience
scores such that... | computer science |
17,167 | Satirical News Detection and Analysis using Attention Mechanism and
Linguistic Features | cs.CL | Satirical news is considered to be entertainment, but it is potentially
deceptive and harmful. Despite the embedded genre in the article, not everyone
can recognize the satirical cues and therefore believe the news as true news.
We observe that satirical cues are often reflected in certain paragraphs rather
than the wh... | computer science |
17,168 | Compositional Approaches for Representing Relations Between Words: A
Comparative Study | cs.CL | Identifying the relations that exist between words (or entities) is important
for various natural language processing tasks such as, relational search,
noun-modifier classification and analogy detection. A popular approach to
represent the relations between a pair of words is to extract the patterns in
which the words ... | computer science |
17,169 | Using $k$-way Co-occurrences for Learning Word Embeddings | cs.CL | Co-occurrences between two words provide useful insights into the semantics
of those words. Consequently, numerous prior work on word embedding learning
have used co-occurrences between two words as the training signal for learning
word embeddings. However, in natural language texts it is common for multiple
words to b... | computer science |
17,170 | Optimizing for Measure of Performance in Max-Margin Parsing | cs.CL | Many statistical learning problems in the area of natural language processing
including sequence tagging, sequence segmentation and syntactic parsing has
been successfully approached by means of structured prediction methods. An
appealing property of the corresponding discriminative learning algorithms is
their ability... | computer science |
17,171 | The Voynich Manuscript is Written in Natural Language: The Pahlavi
Hypothesis | cs.CL | The late medieval Voynich Manuscript (VM) has resisted decryption and was
considered a meaningless hoax or an unsolvable cipher. Here, we provide
evidence that the VM is written in natural language by establishing a relation
of the Voynich alphabet and the Iranian Pahlavi script. Many of the Voynich
characters are upsi... | computer science |
17,172 | A Neural Language Model for Dynamically Representing the Meanings of
Unknown Words and Entities in a Discourse | cs.CL | This study addresses the problem of identifying the meaning of unknown words
or entities in a discourse with respect to the word embedding approaches used
in neural language models. We proposed a method for on-the-fly construction and
exploitation of word embeddings in both the input and output layers of a neural
model... | computer science |
17,173 | Information-Propogation-Enhanced Neural Machine Translation by Relation
Model | cs.CL | Even though sequence-to-sequence neural machine translation (NMT) model have
achieved state-of-art performance in the recent fewer years, but it is widely
concerned that the recurrent neural network (RNN) units are very hard to
capture the long-distance state information, which means RNN can hardly find
the feature wit... | computer science |
17,174 | Depression and Self-Harm Risk Assessment in Online Forums | cs.CL | Users suffering from mental health conditions often turn to online resources
for support, including specialized online support communities or general
communities such as Twitter and Reddit. In this work, we present a neural
framework for supporting and studying users in both types of communities. We
propose methods for... | computer science |
17,175 | A Semi-Supervised Approach to Detecting Stance in Tweets | cs.CL | Stance classification aims to identify, for a particular issue under
discussion, whether the speaker or author of a conversational turn has Pro
(Favor) or Con (Against) stance on the issue. Detecting stance in tweets is a
new task proposed for SemEval-2016 Task6, involving predicting stance for a
dataset of tweets on t... | computer science |
17,176 | "Having 2 hours to write a paper is fun!": Detecting Sarcasm in
Numerical Portions of Text | cs.CL | Sarcasm occurring due to the presence of numerical portions in text has been
quoted as an error made by automatic sarcasm detection approaches in the past.
We present a first study in detecting sarcasm in numbers, as in the case of the
sentence 'Love waking up at 4 am'. We analyze the challenges of the problem,
and pre... | computer science |
17,177 | Translating Domain-Specific Expressions in Knowledge Bases with Neural
Machine Translation | cs.CL | Our work presented in this paper focuses on the translation of
domain-specific expressions represented in semantically structured resources,
like ontologies or knowledge graphs. To make knowledge accessible beyond
language borders, these resources need to be translated into different
languages. The challenge of transla... | computer science |
17,178 | Leveraging Discourse Information Effectively for Authorship Attribution | cs.CL | We explore techniques to maximize the effectiveness of discourse information
in the task of authorship attribution. We present a novel method to embed
discourse features in a Convolutional Neural Network text classifier, which
achieves a state-of-the-art result by a substantial margin. We empirically
investigate severa... | computer science |
17,179 | Cynical Selection of Language Model Training Data | cs.CL | The Moore-Lewis method of "intelligent selection of language model training
data" is very effective, cheap, efficient... and also has structural problems.
(1) The method defines relevance by playing language models trained on the
in-domain and the out-of-domain (or data pool) corpora against each other. This
powerful i... | computer science |
17,180 | A Statistical Comparison of Some Theories of NP Word Order | cs.CL | A frequent object of study in linguistic typology is the order of elements
{demonstrative, adjective, numeral, noun} in the noun phrase. The goal is to
predict the relative frequencies of these orders across languages. Here we use
Poisson regression to statistically compare some prominent accounts of this
variation. We... | computer science |
17,181 | Globally Normalized Reader | cs.CL | Rapid progress has been made towards question answering (QA) systems that can
extract answers from text. Existing neural approaches make use of expensive
bi-directional attention mechanisms or score all possible answer spans,
limiting scalability. We propose instead to cast extractive QA as an iterative
search problem:... | computer science |
17,182 | Combining LSTM and Latent Topic Modeling for Mortality Prediction | cs.CL | There is a great need for technologies that can predict the mortality of
patients in intensive care units with both high accuracy and accountability. We
present joint end-to-end neural network architectures that combine long
short-term memory (LSTM) and a latent topic model to simultaneously train a
classifier for mort... | computer science |
17,183 | CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic
Features for Complex Word Identification | cs.CL | This paper describes the system deployed by the CLaC-EDLK team to the
"SemEval 2016, Complex Word Identification task". The goal of the task is to
identify if a given word in a given context is "simple" or "complex". Our
system relies on linguistic features and cognitive complexity. We used several
supervised models, h... | computer science |
17,184 | Semi-Supervised Instance Population of an Ontology using Word Vector
Embeddings | cs.CL | In many modern day systems such as information extraction and knowledge
management agents, ontologies play a vital role in maintaining the concept
hierarchies of the selected domain. However, ontology population has become a
problematic process due to its nature of heavy coupling with manual human
intervention. With th... | computer science |
17,185 | Steering Output Style and Topic in Neural Response Generation | cs.CL | We propose simple and flexible training and decoding methods for influencing
output style and topic in neural encoder-decoder based language generation.
This capability is desirable in a variety of applications, including
conversational systems, where successful agents need to produce language in a
specific style and g... | computer science |
17,186 | AppTechMiner: Mining Applications and Techniques from Scientific
Articles | cs.CL | This paper presents AppTechMiner, a rule-based information extraction
framework that automatically constructs a knowledge base of all application
areas and problem solving techniques. Techniques include tools, methods,
datasets or evaluation metrics. We also categorize individual research articles
based on their applic... | computer science |
17,187 | Debbie, the Debate Bot of the Future | cs.CL | Chatbots are a rapidly expanding application of dialogue systems with
companies switching to bot services for customer support, and new applications
for users interested in casual conversation. One style of casual conversation
is argument, many people love nothing more than a good argument. Moreover,
there are a number... | computer science |
17,188 | Data-Driven Dialogue Systems for Social Agents | cs.CL | In order to build dialogue systems to tackle the ambitious task of holding
social conversations, we argue that we need a data driven approach that
includes insight into human conversational chit chat, and which incorporates
different natural language processing modules. Our strategy is to analyze and
index large corpor... | computer science |
17,189 | KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition | cs.CL | KnowNER is a multilingual Named Entity Recognition (NER) system that
leverages different degrees of external knowledge. A novel modular framework
divides the knowledge into four categories according to the depth of knowledge
they convey. Each category consists of a set of features automatically
generated from different... | computer science |
17,190 | Capturing Long-range Contextual Dependencies with Memory-enhanced
Conditional Random Fields | cs.CL | Despite successful applications across a broad range of NLP tasks,
conditional random fields ("CRFs"), in particular the linear-chain variant, are
only able to model local features. While this has important benefits in terms
of inference tractability, it limits the ability of the model to capture
long-range dependencie... | computer science |
17,191 | Small-footprint Keyword Spotting Using Deep Neural Network and
Connectionist Temporal Classifier | cs.CL | Mainly for the sake of solving the lack of keyword-specific data, we propose
one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and
Connectionist Temporal Classifier (CTC) on power-constrained small-footprint
mobile devices, taking full advantage of general corpus from continuous speech
recognition which... | computer science |
17,192 | Cross-lingual Word Segmentation and Morpheme Segmentation as Sequence
Labelling | cs.CL | This paper presents our segmentation system developed for the MLP 2017 shared
tasks on cross-lingual word segmentation and morpheme segmentation. We model
both word and morpheme segmentation as character-level sequence labelling
tasks. The prevalent bidirectional recurrent neural network with conditional
random fields ... | computer science |
17,193 | Language Models of Spoken Dutch | cs.CL | In Flanders, all TV shows are subtitled. However, the process of subtitling
is a very time-consuming one and can be sped up by providing the output of a
speech recognizer run on the audio of the TV show, prior to the subtitling.
Naturally, this speech recognition will perform much better if the employed
language model ... | computer science |
17,194 | SYSTRAN Purely Neural MT Engines for WMT2017 | cs.CL | This paper describes SYSTRAN's systems submitted to the WMT 2017 shared news
translation task for English-German, in both translation directions. Our
systems are built using OpenNMT, an open-source neural machine translation
system, implementing sequence-to-sequence models with LSTM encoder/decoders and
attention. We e... | computer science |
17,195 | OpenNMT: Open-source Toolkit for Neural Machine Translation | cs.CL | We introduce an open-source toolkit for neural machine translation (NMT) to
support research into model architectures, feature representations, and source
modalities, while maintaining competitive performance, modularity and
reasonable training requirements. | computer science |
17,196 | StarSpace: Embed All The Things! | cs.CL | We present StarSpace, a general-purpose neural embedding model that can solve
a wide variety of problems: labeling tasks such as text classification, ranking
tasks such as information retrieval/web search, collaborative filtering-based
or content-based recommendation, embedding of multi-relational graphs, and
learning ... | computer science |
17,197 | Human Associations Help to Detect Conventionalized Multiword Expressions | cs.CL | In this paper we show that if we want to obtain human evidence about
conventionalization of some phrases, we should ask native speakers about
associations they have to a given phrase and its component words. We have shown
that if component words of a phrase have each other as frequent associations,
then this phrase can... | computer science |
17,198 | Hash Embeddings for Efficient Word Representations | cs.CL | We present hash embeddings, an efficient method for representing words in a
continuous vector form. A hash embedding may be seen as an interpolation
between a standard word embedding and a word embedding created using a random
hash function (the hashing trick). In hash embeddings each token is represented
by $k$ $d$-di... | computer science |
17,199 | Addressee and Response Selection in Multi-Party Conversations with
Speaker Interaction RNNs | cs.CL | In this paper, we study the problem of addressee and response selection in
multi-party conversations. Understanding multi-party conversations is
challenging because of complex speaker interactions: multiple speakers exchange
messages with each other, playing different roles (sender, addressee,
observer), and these role... | computer science |
17,200 | Dialogue Act Sequence Labeling using Hierarchical encoder with CRF | cs.CL | Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to
utterances in a conversation. The problem of associating semantic labels to
utterances can be treated as a sequence labeling problem. In this work, we
build a hierarchical recurrent neural network using bidirectional LSTM as a
base unit and the... | computer science |
17,201 | Flexible End-to-End Dialogue System for Knowledge Grounded Conversation | cs.CL | In knowledge grounded conversation, domain knowledge plays an important role
in a special domain such as Music. The response of knowledge grounded
conversation might contain multiple answer entities or no entity at all.
Although existing generative question answering (QA) systems can be applied to
knowledge grounded co... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.