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17,502 | Event Representations with Tensor-based Compositions | cs.CL | Robust and flexible event representations are important to many core areas in
language understanding. Scripts were proposed early on as a way of representing
sequences of events for such understanding, and has recently attracted renewed
attention. However, obtaining effective representations for modeling
script-like ev... | computer science |
17,503 | Evaluating Machine Translation Performance on Chinese Idioms with a
Blacklist Method | cs.CL | Idiom translation is a challenging problem in machine translation because the
meaning of idioms is non-compositional, and a literal (word-by-word)
translation is likely to be wrong. In this paper, we focus on evaluating the
quality of idiom translation of MT systems. We introduce a new evaluation
method based on an idi... | computer science |
17,504 | Effective Strategies in Zero-Shot Neural Machine Translation | cs.CL | In this paper, we proposed two strategies which can be applied to a
multilingual neural machine translation system in order to better tackle
zero-shot scenarios despite not having any parallel corpus. The experiments
show that they are effective in terms of both performance and computing
resources, especially in multil... | computer science |
17,505 | Effective Use of Bidirectional Language Modeling for Medical Named
Entity Recognition | cs.CL | Biomedical named entity recognition (NER) is a fundamental task in text
mining of medical documents and has a lot of applications. Existing approaches
for NER require manual feature engineering in order to represent words and its
corresponding contextual information. Deep learning based approaches have been
gaining inc... | computer science |
17,506 | 10Sent: A Stable Sentiment Analysis Method Based on the Combination of
Off-The-Shelf Approaches | cs.CL | Sentiment analysis has become a very important tool for analysis of social
media data. There are several methods developed for this research field, many
of them working very differently from each other, covering distinct aspects of
the problem and disparate strategies. Despite the large number of existent
techniques, t... | computer science |
17,507 | Mastering the Dungeon: Grounded Language Learning by Mechanical Turker
Descent | cs.CL | Contrary to most natural language processing research, which makes use of
static datasets, humans learn language interactively, grounded in an
environment. In this work we propose an interactive learning procedure called
Mechanical Turker Descent (MTD) and use it to train agents to execute natural
language commands gro... | computer science |
17,508 | Application of Natural Language Processing to Determine User
Satisfaction in Public Services | cs.CL | Research on customer satisfaction has increased substantially in recent
years. However, the relative importance and relationships between different
determinants of satisfaction remains uncertain. Moreover, quantitative studies
to date tend to test for significance of pre-determined factors thought to have
an influence ... | computer science |
17,509 | Does Higher Order LSTM Have Better Accuracy in Chunking and Named Entity
Recognition? | cs.CL | Current researches usually employ single order setting by default when
dealing with sequence labeling tasks. In our work, "order" means the number of
tags that a prediction involves at every time step. High order models tend to
capture more dependency information among tags. We first propose a simple
method that low or... | computer science |
17,510 | Customized Nonlinear Bandits for Online Response Selection in Neural
Conversation Models | cs.CL | Dialog response selection is an important step towards natural response
generation in conversational agents. Existing work on neural conversational
models mainly focuses on offline supervised learning using a large set of
context-response pairs. In this paper, we focus on online learning of response
selection in retrie... | computer science |
17,511 | Modelling Domain Relationships for Transfer Learning on Retrieval-based
Question Answering Systems in E-commerce | cs.CL | In this paper, we study transfer learning for the PI and NLI problems, aiming
to propose a general framework, which can effectively and efficiently adapt the
shared knowledge learned from a resource-rich source domain to a resource- poor
target domain. Specifically, since most existing transfer learning methods only
fo... | computer science |
17,512 | SPINE: SParse Interpretable Neural Embeddings | cs.CL | Prediction without justification has limited utility. Much of the success of
neural models can be attributed to their ability to learn rich, dense and
expressive representations. While these representations capture the underlying
complexity and latent trends in the data, they are far from being
interpretable. We propos... | computer science |
17,513 | Ethical Challenges in Data-Driven Dialogue Systems | cs.CL | The use of dialogue systems as a medium for human-machine interaction is an
increasingly prevalent paradigm. A growing number of dialogue systems use
conversation strategies that are learned from large datasets. There are well
documented instances where interactions with these system have resulted in
biased or even off... | computer science |
17,514 | An Exploration of Word Embedding Initialization in Deep-Learning Tasks | cs.CL | Word embeddings are the interface between the world of discrete units of text
processing and the continuous, differentiable world of neural networks. In this
work, we examine various random and pretrained initialization methods for
embeddings used in deep networks and their effect on the performance on four
NLP tasks w... | computer science |
17,515 | Towards Accurate Deceptive Opinion Spam Detection based on Word
Order-preserving CNN | cs.CL | Nowadays, deep learning has been widely used. In natural language learning,
the analysis of complex semantics has been achieved because of its high degree
of flexibility. The deceptive opinions detection is an important application
area in deep learning model, and related mechanisms have been given attention
and resear... | computer science |
17,516 | Acronym Disambiguation: A Domain Independent Approach | cs.CL | Acronyms are omnipresent. They usually express information that is repetitive
and well known. But acronyms can also be ambiguous because there can be
multiple expansions for the same acronym. In this paper, we propose a general
system for acronym disambiguation that can work on any acronym given some
context informatio... | computer science |
17,517 | Experiential, Distributional and Dependency-based Word Embeddings have
Complementary Roles in Decoding Brain Activity | cs.CL | We evaluate 8 different word embedding models on their usefulness for
predicting the neural activation patterns associated with concrete nouns. The
models we consider include an experiential model, based on crowd-sourced
association data, several popular neural and distributional models, and a model
that reflects the s... | computer science |
17,518 | Learning to Remember Translation History with a Continuous Cache | cs.CL | Existing neural machine translation (NMT) models generally translate
sentences in isolation, missing the opportunity to take advantage of
document-level information. In this work, we propose to augment NMT models with
a very light-weight cache-like memory network, which stores recent hidden
representations as translati... | computer science |
17,519 | Machine Translation Using Semantic Web Technologies: A Survey | cs.CL | A large number of machine translation approaches have been developed recently
with the aim of migrating content easily across languages. However, the
literature suggests that many obstacles must be dealt with to achieve better
automatic translations. A central issue that machine translation systems must
handle is ambig... | computer science |
17,520 | Modeling Past and Future for Neural Machine Translation | cs.CL | Existing neural machine translation systems do not explicitly model what has
been translated and what has not during the decoding phase. To address this
problem, we propose a novel mechanism that separates the source information
into two parts: translated Past contents and untranslated Future contents,
which are modele... | computer science |
17,521 | Lexical-semantic resources: yet powerful resources for automatic
personality classification | cs.CL | In this paper, we aim to reveal the impact of lexical-semantic resources,
used in particular for word sense disambiguation and sense-level semantic
categorization, on automatic personality classification task. While stylistic
features (e.g., part-of-speech counts) have been shown their power in this
task, the impact of... | computer science |
17,522 | Slim Embedding Layers for Recurrent Neural Language Models | cs.CL | Recurrent neural language models are the state-of-the-art models for language
modeling. When the vocabulary size is large, the space taken to store the model
parameters becomes the bottleneck for the use of recurrent neural language
models. In this paper, we introduce a simple space compression method that
randomly sha... | computer science |
17,523 | Surfacing contextual hate speech words within social media | cs.CL | Social media platforms have recently seen an increase in the occurrence of
hate speech discourse which has led to calls for improved detection methods.
Most of these rely on annotated data, keywords, and a classification technique.
While this approach provides good coverage, it can fall short when dealing with
new term... | computer science |
17,524 | End-to-end Adversarial Learning for Generative Conversational Agents | cs.CL | This paper presents a new adversarial learning method for generative
conversational agents (GCA) besides a new model of GCA. Similar to previous
works on adversarial learning for dialogue generation, our method assumes the
GCA as a generator that aims at fooling a discriminator that labels dialogues
as human-generated ... | computer science |
17,525 | Vietnamese Semantic Role Labelling | cs.CL | In this paper, we study semantic role labelling (SRL), a subtask of semantic
parsing of natural language sentences and its application for the Vietnamese
language. We present our effort in building Vietnamese PropBank, the first
Vietnamese SRL corpus and a software system for labelling semantic roles of
Vietnamese text... | computer science |
17,526 | Unsupervised Discovery of Structured Acoustic Tokens with Applications
to Spoken Term Detection | cs.CL | In this paper, we compare two paradigms for unsupervised discovery of
structured acoustic tokens directly from speech corpora without any human
annotation. The Multigranular Paradigm seeks to capture all available
information in the corpora with multiple sets of tokens for different model
granularities. The Hierarchica... | computer science |
17,527 | Acoustic-To-Word Model Without OOV | cs.CL | Recently, the acoustic-to-word model based on the Connectionist Temporal
Classification (CTC) criterion was shown as a natural end-to-end model directly
targeting words as output units. However, this type of word-based CTC model
suffers from the out-of-vocabulary (OOV) issue as it can only model limited
number of words... | computer science |
17,528 | Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese
Dependency Parsing | cs.CL | In the training of transition-based dependency parsers, an oracle is used to
predict a transition sequence for a sentence and its gold tree. However, the
transition system may exhibit ambiguity, that is, there can be multiple correct
transition sequences that form the gold tree. We propose to make use of the
property i... | computer science |
17,529 | Visualisation and 'diagnostic classifiers' reveal how recurrent and
recursive neural networks process hierarchical structure | cs.CL | We investigate how neural networks can learn and process languages with
hierarchical, compositional semantics. To this end, we define the artificial
task of processing nested arithmetic expressions, and study whether different
types of neural networks can learn to compute their meaning. We find that
recursive neural ne... | computer science |
17,530 | Speaker-Sensitive Dual Memory Networks for Multi-Turn Slot Tagging | cs.CL | In multi-turn dialogs, natural language understanding models can introduce
obvious errors by being blind to contextual information. To incorporate dialog
history, we present a neural architecture with Speaker-Sensitive Dual Memory
Networks which encode utterances differently depending on the speaker. This
addresses the... | computer science |
17,531 | End-to-End Optimization of Task-Oriented Dialogue Model with Deep
Reinforcement Learning | cs.CL | In this paper, we present a neural network based task-oriented dialogue
system that can be optimized end-to-end with deep reinforcement learning (RL).
The system is able to track dialogue state, interface with knowledge bases, and
incorporate query results into agent's responses to successfully complete
task-oriented d... | computer science |
17,532 | Curriculum Q-Learning for Visual Vocabulary Acquisition | cs.CL | The structure of curriculum plays a vital role in our learning process, both
as children and adults. Presenting material in ascending order of difficulty
that also exploits prior knowledge can have a significant impact on the rate of
learning. However, the notion of difficulty and prior knowledge differs from
person to... | computer science |
17,533 | Identifying Patterns of Associated-Conditions through Topic Models of
Electronic Medical Records | cs.CL | Multiple adverse health conditions co-occurring in a patient are typically
associated with poor prognosis and increased office or hospital visits.
Developing methods to identify patterns of co-occurring conditions can assist
in diagnosis. Thus identifying patterns of associations among co-occurring
conditions is of gro... | computer science |
17,534 | Improved Twitter Sentiment Analysis Using Naive Bayes and Custom
Language Model | cs.CL | In the last couple decades, social network services like Twitter have
generated large volumes of data about users and their interests, providing
meaningful business intelligence so organizations can better understand and
engage their customers. All businesses want to know who is promoting their
products, who is complai... | computer science |
17,535 | Multimodal Attribute Extraction | cs.CL | The broad goal of information extraction is to derive structured information
from unstructured data. However, most existing methods focus solely on text,
ignoring other types of unstructured data such as images, video and audio which
comprise an increasing portion of the information on the web. To address this
shortcom... | computer science |
17,536 | Predicting and Explaining Human Semantic Search in a Cognitive Model | cs.CL | Recent work has attempted to characterize the structure of semantic memory
and the search algorithms which, together, best approximate human patterns of
search revealed in a semantic fluency task. There are a number of models that
seek to capture semantic search processes over networks, but they vary in the
cognitive p... | computer science |
17,537 | Neural Response Generation with Dynamic Vocabularies | cs.CL | We study response generation for open domain conversation in chatbots.
Existing methods assume that words in responses are generated from an identical
vocabulary regardless of their inputs, which not only makes them vulnerable to
generic patterns and irrelevant noise, but also causes a high cost in decoding.
We propose... | computer science |
17,538 | Cache-based Document-level Neural Machine Translation | cs.CL | Sentences in a well-formed text are connected to each other via various links
to form the cohesive structure of the text. Current neural machine translation
(NMT) systems translate a text in a conventional sentence-by-sentence fashion,
ignoring such cross-sentence links and dependencies. This may lead to generate
an in... | computer science |
17,539 | Multi-Domain Adversarial Learning for Slot Filling in Spoken Language
Understanding | cs.CL | The goal of this paper is to learn cross-domain representations for slot
filling task in spoken language understanding (SLU). Most of the recently
published SLU models are domain-specific ones that work on individual task
domains. Annotating data for each individual task domain is both financially
costly and non-scalab... | computer science |
17,540 | Lexical and Derivational Meaning in Vector-Based Models of
Relativisation | cs.CL | Sadrzadeh et al (2013) present a compositional distributional analysis of
relative clauses in English in terms of the Frobenius algebraic structure of
finite dimensional vector spaces. The analysis relies on distinct type
assignments and lexical recipes for subject vs object relativisation. The
situation for Dutch is d... | computer science |
17,541 | On the importance of normative data in speech-based assessment | cs.CL | Data sets for identifying Alzheimer's disease (AD) are often relatively
sparse, which limits their ability to train generalizable models. Here, we
augment such a data set, DementiaBank, with each of two normative data sets,
the Wisconsin Longitudinal Study and Talk2Me, each of which employs a
speech-based picture-descr... | computer science |
17,542 | Text Generation Based on Generative Adversarial Nets with Latent
Variable | cs.CL | In this paper, we propose a model using generative adversarial net (GAN) to
generate realistic text. Instead of using standard GAN, we combine variational
autoencoder (VAE) with generative adversarial net. The use of high-level latent
random variables is helpful to learn the data distribution and solve the
problem that... | computer science |
17,543 | Improving Visually Grounded Sentence Representations with Self-Attention | cs.CL | Sentence representation models trained only on language could potentially
suffer from the grounding problem. Recent work has shown promising results in
improving the qualities of sentence representations by jointly training them
with associated image features. However, the grounding capability is limited
due to distant... | computer science |
17,544 | AWE-CM Vectors: Augmenting Word Embeddings with a Clinical Metathesaurus | cs.CL | In recent years, word embeddings have been surprisingly effective at
capturing intuitive characteristics of the words they represent. These vectors
achieve the best results when training corpora are extremely large, sometimes
billions of words. Clinical natural language processing datasets, however, tend
to be much sma... | computer science |
17,545 | Sequence Mining and Pattern Analysis in Drilling Reports with Deep
Natural Language Processing | cs.CL | Drilling activities in the oil and gas industry have been reported over
decades for thousands of wells on a daily basis, yet the analysis of this text
at large-scale for information retrieval, sequence mining, and pattern analysis
is very challenging. Drilling reports contain interpretations written by
drillers from no... | computer science |
17,546 | Deep Semantic Role Labeling with Self-Attention | cs.CL | Semantic Role Labeling (SRL) is believed to be a crucial step towards natural
language understanding and has been widely studied. Recent years, end-to-end
SRL with recurrent neural networks (RNN) has gained increasing attention.
However, it remains a major challenge for RNNs to handle structural information
and long ra... | computer science |
17,547 | Phylogenetics of Indo-European Language families via an
Algebro-Geometric Analysis of their Syntactic Structures | cs.CL | Using Phylogenetic Algebraic Geometry, we analyze computationally the
phylogenetic tree of subfamilies of the Indo-European language family, using
data of syntactic structures. The two main sources of syntactic data are the
SSWL database and Longobardi's recent data of syntactic parameters. We compute
phylogenetic inva... | computer science |
17,548 | Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing
and Best-Worst Scaling | cs.CL | Access to word-sentiment associations is useful for many applications,
including sentiment analysis, stance detection, and linguistic analysis.
However, manually assigning fine-grained sentiment association scores to words
has many challenges with respect to keeping annotations consistent. We apply
the annotation techn... | computer science |
17,549 | Best-Worst Scaling More Reliable than Rating Scales: A Case Study on
Sentiment Intensity Annotation | cs.CL | Rating scales are a widely used method for data annotation; however, they
present several challenges, such as difficulty in maintaining inter- and
intra-annotator consistency. Best-worst scaling (BWS) is an alternative method
of annotation that is claimed to produce high-quality annotations while keeping
the required n... | computer science |
17,550 | The Effect of Negators, Modals, and Degree Adverbs on Sentiment
Composition | cs.CL | Negators, modals, and degree adverbs can significantly affect the sentiment
of the words they modify. Often, their impact is modeled with simple
heuristics; although, recent work has shown that such heuristics do not capture
the true sentiment of multi-word phrases. We created a dataset of phrases that
include various ... | computer science |
17,551 | One for All: Towards Language Independent Named Entity Linking | cs.CL | Entity linking (EL) is the task of disambiguating mentions in text by
associating them with entries in a predefined database of mentions (persons,
organizations, etc). Most previous EL research has focused mainly on one
language, English, with less attention being paid to other languages, such as
Spanish or Chinese. In... | computer science |
17,552 | Neural Cross-Lingual Entity Linking | cs.CL | A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the ... | computer science |
17,553 | Neural Machine Translation by Generating Multiple Linguistic Factors | cs.CL | Factored neural machine translation (FNMT) is founded on the idea of using
the morphological and grammatical decomposition of the words (factors) at the
output side of the neural network. This architecture addresses two well-known
problems occurring in MT, namely the size of target language vocabulary and the
number of... | computer science |
17,554 | Strong Baselines for Simple Question Answering over Knowledge Graphs
with and without Neural Networks | cs.CL | We examine the problem of question answering over knowledge graphs, focusing
on simple questions that can be answered by the lookup of a single fact.
Adopting a straightforward decomposition of the problem into entity detection,
entity linking, relation prediction, and evidence combination, we explore
simple yet strong... | computer science |
17,555 | Dual Attention Network for Product Compatibility and Function
Satisfiability Analysis | cs.CL | Product compatibility and their functionality are of utmost importance to
customers when they purchase products, and to sellers and manufacturers when
they sell products. Due to the huge number of products available online, it is
infeasible to enumerate and test the compatibility and functionality of every
product. In ... | computer science |
17,556 | Multi-channel Encoder for Neural Machine Translation | cs.CL | Attention-based Encoder-Decoder has the effective architecture for neural
machine translation (NMT), which typically relies on recurrent neural networks
(RNN) to build the blocks that will be lately called by attentive reader during
the decoding process. This design of encoder yields relatively uniform
composition on s... | computer science |
17,557 | A Novel Embedding Model for Knowledge Base Completion Based on
Convolutional Neural Network | cs.CL | In this paper, we propose a novel embedding model, named ConvKB, for
knowledge base completion. Our model ConvKB advances state-of-the-art models by
employing a convolutional neural network, so that it can capture global
relationships and transitional characteristics between entities and relations
in knowledge bases. I... | computer science |
17,558 | Product Function Need Recognition via Semi-supervised Attention Network | cs.CL | Functionality is of utmost importance to customers when they purchase
products. However, it is unclear to customers whether a product can really
satisfy their needs on functions. Further, missing functions may be
intentionally hidden by the manufacturers or the sellers. As a result, a
customer needs to spend a fair amo... | computer science |
17,559 | A Corpus of Deep Argumentative Structures as an Explanation to
Argumentative Relations | cs.CL | In this paper, we compose a new task for deep argumentative structure
analysis that goes beyond shallow discourse structure analysis. The idea is
that argumentative relations can reasonably be represented with a small set of
predefined patterns. For example, using value judgment and bipolar causality,
we can explain a ... | computer science |
17,560 | Hungarian Layer: Logics Empowered Neural Architecture | cs.CL | Neural architecture is a purely numeric framework, which fits the data as a
continuous function. However, lacking of logic flow (e.g. \textit{if, for,
while}), traditional algorithms (e.g. \textit{Hungarian algorithm, A$^*$
searching, decision tress algorithm}) could not be embedded into this paradigm,
which limits the... | computer science |
17,561 | Convolutional Neural Networks for Medical Diagnosis from Admission Notes | cs.CL | $\textbf{Objective}$ Develop an automatic diagnostic system which only uses
textual admission information from Electronic Health Records (EHRs) and assist
clinicians with a timely and statistically proved decision tool. The hope is
that the tool can be used to reduce mis-diagnosis.
$\textbf{Materials and Methods}$ We... | computer science |
17,562 | Effective Neural Solution for Multi-Criteria Word Segmentation | cs.CL | We present a simple yet elegant solution to train a single joint model on
multi-criteria corpora for Chinese Word Segmentation (CWS). Our novel design
requires no private layers in model architecture, instead, introduces two
artificial tokens at the beginning and ending of input sentence to specify the
required target ... | computer science |
17,563 | Sequence to Sequence Networks for Roman-Urdu to Urdu Transliteration | cs.CL | Neural Machine Translation models have replaced the conventional phrase based
statistical translation methods since the former takes a generic, scalable,
data-driven approach rather than relying on manual, hand-crafted features. The
neural machine translation system is based on one neural network that is
composed of tw... | computer science |
17,564 | Characterizing the hyper-parameter space of LSTM language models for
mixed context applications | cs.CL | Applying state of the art deep learning models to novel real world datasets
gives a practical evaluation of the generalizability of these models. Of
importance in this process is how sensitive the hyper parameters of such models
are to novel datasets as this would affect the reproducibility of a model. We
present work ... | computer science |
17,565 | Word Sense Disambiguation with LSTM: Do We Really Need 100 Billion
Words? | cs.CL | Recently, Yuan et al. (2016) have shown the effectiveness of using Long
Short-Term Memory (LSTM) for performing Word Sense Disambiguation (WSD). Their
proposed technique outperformed the previous state-of-the-art with several
benchmarks, but neither the training data nor the source code was released.
This paper present... | computer science |
17,566 | Aspect Extraction and Sentiment Classification of Mobile Apps using
App-Store Reviews | cs.CL | Understanding of customer sentiment can be useful for product development. On
top of that if the priorities for the development order can be known, then
development procedure become simpler. This work has tried to address this issue
in the mobile app domain. Along with aspect and opinion extraction this work
has also c... | computer science |
17,567 | Modulating and attending the source image during encoding improves
Multimodal Translation | cs.CL | We propose a new and fully end-to-end approach for multimodal translation
where the source text encoder modulates the entire visual input processing
using conditional batch normalization, in order to compute the most informative
image features for our task. Additionally, we propose a new attention mechanism
derived fro... | computer science |
17,568 | Learning Interpretable Spatial Operations in a Rich 3D Blocks World | cs.CL | In this paper, we study the problem of mapping natural language instructions
to complex spatial actions in a 3D blocks world. We first introduce a new
dataset that pairs complex 3D spatial operations to rich natural language
descriptions that require complex spatial and pragmatic interpretations such as
"mirroring", "t... | computer science |
17,569 | Multi-Task Learning for Mental Health using Social Media Text | cs.CL | We introduce initial groundwork for estimating suicide risk and mental health
in a deep learning framework. By modeling multiple conditions, the system
learns to make predictions about suicide risk and mental health at a low false
positive rate. Conditions are modeled as tasks in a multi-task learning (MTL)
framework, ... | computer science |
17,570 | Inducing Interpretability in Knowledge Graph Embeddings | cs.CL | We study the problem of inducing interpretability in KG embeddings.
Specifically, we explore the Universal Schema (Riedel et al., 2013) and propose
a method to induce interpretability. There have been many vector space models
proposed for the problem, however, most of these methods don't address the
interpretability (s... | computer science |
17,571 | Stochastic Answer Networks for Machine Reading Comprehension | cs.CL | We propose a simple yet robust stochastic answer network (SAN) that simulates
multi-step reasoning in machine reading comprehension. Compared to previous
work such as ReasoNet, the unique feature is the use of a kind of stochastic
prediction dropout on the answer module (final layer) of the neural network
during the tr... | computer science |
17,572 | Contextualized Word Representations for Reading Comprehension | cs.CL | Reading a document and extracting an answer to a question about its content
has attracted substantial attention recently. While most work has focused on
the interaction between the question and the document, in this work we evaluate
the importance of context when the question and document are processed
independently. W... | computer science |
17,573 | Scale Up Event Extraction Learning via Automatic Training Data
Generation | cs.CL | The task of event extraction has long been investigated in a supervised
learning paradigm, which is bound by the number and the quality of the training
instances. Existing training data must be manually generated through a
combination of expert domain knowledge and extensive human involvement.
However, due to drastic e... | computer science |
17,574 | On the Benefit of Combining Neural, Statistical and External Features
for Fake News Identification | cs.CL | Identifying the veracity of a news article is an interesting problem while
automating this process can be a challenging task. Detection of a news article
as fake is still an open question as it is contingent on many factors which the
current state-of-the-art models fail to incorporate. In this paper, we explore
a subta... | computer science |
17,575 | Tracing a Loose Wordhood for Chinese Input Method Engine | cs.CL | Chinese input methods are used to convert pinyin sequence or other Latin
encoding systems into Chinese character sentences. For more effective
pinyin-to-character conversion, typical Input Method Engines (IMEs) rely on a
predefined vocabulary that demands manually maintenance on schedule. For the
purpose of removing th... | computer science |
17,576 | The Zero Resource Speech Challenge 2017 | cs.CL | We describe a new challenge aimed at discovering subword and word units from
raw speech. This challenge is the followup to the Zero Resource Speech
Challenge 2015. It aims at constructing systems that generalize across
languages and adapt to new speakers. The design features and evaluation metrics
of the challenge are ... | computer science |
17,577 | Social Media Writing Style Fingerprint | cs.CL | We present our approach for computer-aided social media text authorship
attribution based on recent advances in short text authorship verification. We
use various natural language techniques to create word-level and
character-level models that act as hidden layers to simulate a simple neural
network. The choice of word... | computer science |
17,578 | A User-Study on Online Adaptation of Neural Machine Translation to Human
Post-Edits | cs.CL | The advantages of neural machine translation (NMT) have been extensively
validated for offline translation of several language pairs for different
domains of spoken and written language. However, research on interactive
learning of NMT by adaptation to human post-edits has so far been confined to
simulation experiments... | computer science |
17,579 | Passing the Brazilian OAB Exam: data preparation and some experiments | cs.CL | In Brazil, all legal professionals must demonstrate their knowledge of the
law and its application by passing the OAB exams, the national bar exams. The
OAB exams therefore provide an excellent benchmark for the performance of legal
information systems since passing the exam would arguably signal that the
system has ac... | computer science |
17,580 | Learning when to skim and when to read | cs.CL | Many recent advances in deep learning for natural language processing have
come at increasing computational cost, but the power of these state-of-the-art
models is not needed for every example in a dataset. We demonstrate two
approaches to reducing unnecessary computation in cases where a fast but weak
baseline classie... | computer science |
17,581 | Avoiding Echo-Responses in a Retrieval-Based Conversation System | cs.CL | Retrieval-based conversation systems generally tend to rank high responses
that are semantically similar or even identical to the given conversation
context. While the system's goal is to find the most appropriate response,
rather than just semantically similar, this tendency results in low-quality
responses. This chal... | computer science |
17,582 | Hierarchical Text Generation and Planning for Strategic Dialogue | cs.CL | End-to-end models for strategic dialogue are challenging to train, because
linguistic and strategic aspects are entangled in latent state vectors. We
introduce an approach to generating latent representations of dialogue moves,
by inducing sentence representations to maximize the likelihood of subsequent
sentences and ... | computer science |
17,583 | Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions | cs.CL | This paper describes Tacotron 2, a neural network architecture for speech
synthesis directly from text. The system is composed of a recurrent
sequence-to-sequence feature prediction network that maps character embeddings
to mel-scale spectrograms, followed by a modified WaveNet model acting as a
vocoder to synthesize t... | computer science |
17,584 | NegBio: a high-performance tool for negation and uncertainty detection
in radiology reports | cs.CL | Negative and uncertain medical findings are frequent in radiology reports,
but discriminating them from positive findings remains challenging for
information extraction. Here, we propose a new algorithm, NegBio, to detect
negative and uncertain findings in radiology reports. Unlike previous
rule-based methods, NegBio u... | computer science |
17,585 | Train Once, Test Anywhere: Zero-Shot Learning for Text Classification | cs.CL | Zero-shot Learners are models capable of predicting unseen classes. In this
work, we propose a Zero-shot Learning approach for text categorization. Our
method involves training model on a large corpus of sentences to learn the
relationship between a sentence and embedding of sentence's tags. Learning such
relationship ... | computer science |
17,586 | Query-Based Abstractive Summarization Using Neural Networks | cs.CL | In this paper, we present a model for generating summaries of text documents
with respect to a query. This is known as query-based summarization. We adapt
an existing dataset of news article summaries for the task and train a
pointer-generator model using this dataset. The generated summaries are
evaluated by measuring... | computer science |
17,587 | Towards a science of human stories: using sentiment analysis and
emotional arcs to understand the building blocks of complex social systems | cs.CL | Given the growing assortment of sentiment measuring instruments, it is
imperative to understand which aspects of sentiment dictionaries contribute to
both their classification accuracy and their ability to provide richer
understanding of texts. Here, we perform detailed, quantitative tests and
qualitative assessments o... | computer science |
17,588 | Low Resourced Machine Translation via Morpho-syntactic Modeling: The
Case of Dialectal Arabic | cs.CL | We present the second ever evaluated Arabic dialect-to-dialect machine
translation effort, and the first to leverage external resources beyond a small
parallel corpus. The subject has not previously received serious attention due
to lack of naturally occurring parallel data; yet its importance is evidenced
by dialectal... | computer science |
17,589 | A Chinese Dataset with Negative Full Forms for General Abbreviation
Prediction | cs.CL | Abbreviation is a common phenomenon across languages, especially in Chinese.
In most cases, if an expression can be abbreviated, its abbreviation is used
more often than its fully expanded forms, since people tend to convey
information in a most concise way. For various language processing tasks,
abbreviation is an obs... | computer science |
17,590 | Detecting Hate Speech in Social Media | cs.CL | In this paper we examine methods to detect hate speech in social media, while
distinguishing this from general profanity. We aim to establish lexical
baselines for this task by applying supervised classification methods using a
recently released dataset annotated for this purpose. As features, our system
uses character... | computer science |
17,591 | word representation or word embedding in Persian text | cs.CL | Text processing is one of the sub-branches of natural language processing.
Recently, the use of machine learning and neural networks methods has been
given greater consideration. For this reason, the representation of words has
become very important. This article is about word representation or converting
words into ve... | computer science |
17,592 | Subword and Crossword Units for CTC Acoustic Models | cs.CL | This paper proposes a novel approach to create an unit set for CTC based
speech recognition systems. By using Byte Pair Encoding we learn an unit set of
an arbitrary size on a given training text. In contrast to using characters or
words as units this allows us to find a good trade-off between the size of our
unit set ... | computer science |
17,593 | Analogy Mining for Specific Design Needs | cs.CL | Finding analogical inspirations in distant domains is a powerful way of
solving problems. However, as the number of inspirations that could be matched
and the dimensions on which that matching could occur grow, it becomes
challenging for designers to find inspirations relevant to their needs.
Furthermore, designers are... | computer science |
17,594 | Unsupervised Word Mapping Using Structural Similarities in Monolingual
Embeddings | cs.CL | Most existing methods for automatic bilingual dictionary induction rely on
prior alignments between the source and target languages, such as parallel
corpora or seed dictionaries. For many language pairs, such supervised
alignments are not readily available. We propose an unsupervised approach for
learning a bilingual ... | computer science |
17,595 | DeepNorm-A Deep Learning Approach to Text Normalization | cs.CL | This paper presents an simple yet sophisticated approach to the challenge by
Sproat and Jaitly (2016)- given a large corpus of written text aligned to its
normalized spoken form, train an RNN to learn the correct normalization
function. Text normalization for a token seems very straightforward without
it's context. But... | computer science |
17,596 | Attentive Memory Networks: Efficient Machine Reading for Conversational
Search | cs.CL | Recent advances in conversational systems have changed the search paradigm.
Traditionally, a user poses a query to a search engine that returns an answer
based on its index, possibly leveraging external knowledge bases and
conditioning the response on earlier interactions in the search session. In a
natural conversatio... | computer science |
17,597 | An Ensemble Model with Ranking for Social Dialogue | cs.CL | Open-domain social dialogue is one of the long-standing goals of Artificial
Intelligence. This year, the Amazon Alexa Prize challenge was announced for the
first time, where real customers get to rate systems developed by leading
universities worldwide. The aim of the challenge is to converse "coherently and
engagingly... | computer science |
17,598 | The Character Thinks Ahead: creative writing with deep learning nets and
its stylistic assessment | cs.CL | We discuss how to control outputs from deep learning models of text corpora
so as to create contemporary poetic works. We assess whether these controls are
successful in the immediate sense of creating stylo- metric distinctiveness.
The specific context is our piece The Character Thinks Ahead (2016/17); the
potential a... | computer science |
17,599 | Variational Attention for Sequence-to-Sequence Models | cs.CL | The variational encoder-decoder (VED) encodes source information as a set of
random variables using a neural network, which in turn is decoded into target
data using another neural network. In natural language processing,
sequence-to-sequence (Seq2Seq) models typically serve as encoder-decoder
networks. When combined w... | computer science |
17,600 | TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social
Aspects of Internet Slang | cs.CL | Slang is ubiquitous on the Internet. The emergence of new social contexts
like micro-blogs, question-answering forums, and social networks has enabled
slang and non-standard expressions to abound on the web. Despite this, slang
has been traditionally viewed as a form of non-standard language -- a form of
language that ... | computer science |
17,601 | Source-side Prediction for Neural Headline Generation | cs.CL | The encoder-decoder model is widely used in natural language generation
tasks. However, the model sometimes suffers from repeated redundant generation,
misses important phrases, and includes irrelevant entities. Toward solving
these problems we propose a novel source-side token prediction module. Our
method jointly est... | computer science |
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