Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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17,402 | Shallow Discourse Parsing with Maximum Entropy Model | cs.CL | In recent years, more research has been devoted to studying the subtask of
the complete shallow discourse parsing, such as indentifying discourse
connective and arguments of connective. There is a need to design a full
discourse parser to pull these subtasks together. So we develop a discourse
parser turning the free t... | computer science |
17,403 | A Sequential Matching Framework for Multi-turn Response Selection in
Retrieval-based Chatbots | cs.CL | We study the problem of response selection for multi-turn conversation in
retrieval-based chatbots. The task requires matching a response candidate with
a conversation context, whose challenges include how to recognize important
parts of the context, and how to model the relationships among utterances in
the context. E... | computer science |
17,404 | Grammar induction for mildly context sensitive languages using
variational Bayesian inference | cs.CL | The following technical report presents a formal approach to probabilistic
minimalist grammar induction. We describe a formalization of a minimalist
grammar. Based on this grammar, we define a generative model for minimalist
derivations. We then present a generalized algorithm for the application of
variational Bayesia... | computer science |
17,405 | A Neural-Symbolic Approach to Natural Language Tasks | cs.CL | Deep learning (DL) has in recent years been widely used in natural language
processing (NLP) applications due to its superior performance. However, while
natural languages are rich in grammatical structure, DL has not been able to
explicitly represent and enforce such structures. This paper proposes a new
architecture ... | computer science |
17,406 | Summarizing Dialogic Arguments from Social Media | cs.CL | Online argumentative dialog is a rich source of information on popular
beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary shou... | computer science |
17,407 | Neural Wikipedian: Generating Textual Summaries from Knowledge Base
Triples | cs.CL | Most people do not interact with Semantic Web data directly. Unless they have
the expertise to understand the underlying technology, they need textual or
visual interfaces to help them make sense of it. We explore the problem of
generating natural language summaries for Semantic Web data. This is
non-trivial, especiall... | computer science |
17,408 | Keyword-based Query Comprehending via Multiple Optimized-Demand
Augmentation | cs.CL | In this paper, we consider the problem of machine reading task when the
questions are in the form of keywords, rather than natural language. In recent
years, researchers have achieved significant success on machine reading
comprehension tasks, such as SQuAD and TriviaQA. These datasets provide a
natural language questi... | computer science |
17,409 | Improved Text Language Identification for the South African Languages | cs.CL | Virtual assistants and text chatbots have recently been gaining popularity.
Given the short message nature of text-based chat interactions, the language
identification systems of these bots might only have 15 or 20 characters to
make a prediction. However, accurate text language identification is important,
especially ... | computer science |
17,410 | Paraphrase Generation with Deep Reinforcement Learning | cs.CL | Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generatio... | computer science |
17,411 | Towards Automatic Generation of Entertaining Dialogues in Chinese
Crosstalks | cs.CL | Crosstalk, also known by its Chinese name xiangsheng, is a traditional
Chinese comedic performing art featuring jokes and funny dialogues, and one of
China's most popular cultural elements. It is typically in the form of a
dialogue between two performers for the purpose of bringing laughter to the
audience, with one pe... | computer science |
17,412 | Improving Neural Machine Translation through Phrase-based Forced
Decoding | cs.CL | Compared to traditional statistical machine translation (SMT), neural machine
translation (NMT) often sacrifices adequacy for the sake of fluency. We propose
a method to combine the advantages of traditional SMT and NMT by exploiting an
existing phrase-based SMT model to compute the phrase-based decoding cost for
an NM... | computer science |
17,413 | Semantic Structure and Interpretability of Word Embeddings | cs.CL | Dense word embeddings, which encode semantic meanings of words to low
dimensional vector spaces have become very popular in natural language
processing (NLP) research due to their state-of-the-art performances in many
NLP tasks. Word embeddings are substantially successful in capturing semantic
relations among words, s... | computer science |
17,414 | JSUT corpus: free large-scale Japanese speech corpus for end-to-end
speech synthesis | cs.CL | Thanks to improvements in machine learning techniques including deep
learning, a free large-scale speech corpus that can be shared between academic
institutions and commercial companies has an important role. However, such a
corpus for Japanese speech synthesis does not exist. In this paper, we designed
a novel Japanes... | computer science |
17,415 | Evaluating Discourse Phenomena in Neural Machine Translation | cs.CL | For machine translation to tackle discourse phenomena, models must have
access to extra-sentential linguistic context. There has been recent interest
in modelling context in neural machine translation (NMT), but models have been
principally evaluated with standard automatic metrics, poorly adapted to
evaluating discour... | computer science |
17,416 | Text Annotation Graphs: Annotating Complex Natural Language Phenomena | cs.CL | This paper introduces a new web-based software tool for annotating text, Text
Annotation Graphs, or TAG. It provides functionality for representing complex
relationships between words and word phrases that are not available in other
software tools, including the ability to define and visualize relationships
between the... | computer science |
17,417 | SRL4ORL: Improving Opinion Role Labelling using Multi-task Learning with
Semantic Role Labeling | cs.CL | For over 12 years, machine learning is used to extract opinion-holder-target
structures from text to answer the question: Who expressed what kind of
sentiment towards what?. However, recent neural approaches do not outperform
the state-of-the-art feature-based model for Opinion Role Labelling (ORL). We
suspect this is ... | computer science |
17,418 | Multi-Mention Learning for Reading Comprehension with Neural Cascades | cs.CL | Reading comprehension is a challenging task, especially when executed across
longer or across multiple evidence documents, where the answer is likely to
reoccur. Existing neural architectures typically do not scale to the entire
evidence, and hence, resort to selecting a single passage in the document
(either via trunc... | computer science |
17,419 | A Comparison of Feature-Based and Neural Scansion of Poetry | cs.CL | Automatic analysis of poetic rhythm is a challenging task that involves
linguistics, literature, and computer science. When the language to be analyzed
is known, rule-based systems or data-driven methods can be used. In this paper,
we analyze poetic rhythm in English and Spanish. We show that the
representations of dat... | computer science |
17,420 | Towards Neural Machine Translation with Partially Aligned Corpora | cs.CL | While neural machine translation (NMT) has become the new paradigm, the
parameter optimization requires large-scale parallel data which is scarce in
many domains and language pairs. In this paper, we address a new translation
scenario in which there only exists monolingual corpora and phrase pairs. We
propose a new met... | computer science |
17,421 | Dual Language Models for Code Mixed Speech Recognition | cs.CL | In this work, we present a new approach to language modeling for bilingual
code-switched text. This technique, called dual language models, involves
building two complementary monolingual language models and combining them using
a probabilistic model for switching between the two. The objective of this
technique is to ... | computer science |
17,422 | Compressing Word Embeddings via Deep Compositional Code Learning | cs.CL | Natural language processing (NLP) models often require a massive number of
parameters for word embeddings, resulting in a large storage or memory
footprint. Deploying neural NLP models to mobile devices requires compressing
the word embeddings without any significant sacrifices in performance. For this
purpose, we prop... | computer science |
17,423 | One Model to Rule them all: Multitask and Multilingual Modelling for
Lexical Analysis | cs.CL | When learning a new skill, you take advantage of your preexisting skills and
knowledge. For instance, if you are a skilled violinist, you will likely have
an easier time learning to play cello. Similarly, when learning a new language
you take advantage of the languages you already speak. For instance, if your
native la... | computer science |
17,424 | Learning Filterbanks from Raw Speech for Phone Recognition | cs.CL | We train a bank of complex filters that operates on the raw waveform and
feeds into a convolutional neural network for end-to-end phone recognition.
These time-domain filterbanks (TD-filterbanks) are initialized as an
approximation of mel-filterbanks (MFSC, for mel-frequency spectral
coefficients), and then fine-tuned ... | computer science |
17,425 | "Attention" for Detecting Unreliable News in the Information Age | cs.CL | An Unreliable news is any piece of information which is false or misleading,
deliberately spread to promote political, ideological and financial agendas.
Recently the problem of unreliable news has got a lot of attention as the
number instances of using news and social media outlets for propaganda have
increased rapidl... | computer science |
17,426 | Predicting Discharge Medications at Admission Time Based on Deep
Learning | cs.CL | Predicting discharge medications right after a patient being admitted is an
important clinical decision, which provides physicians with guidance on what
type of medication regimen to plan for and what possible changes on initial
medication may occur during an inpatient stay. It also facilitates medication
reconciliatio... | computer science |
17,427 | Deep Stacking Networks for Low-Resource Chinese Word Segmentation with
Transfer Learning | cs.CL | In recent years, neural networks have proven to be effective in Chinese word
segmentation. However, this promising performance relies on large-scale
training data. Neural networks with conventional architectures cannot achieve
the desired results in low-resource datasets due to the lack of labelled
training data. In th... | computer science |
17,428 | Towards Linguistically Generalizable NLP Systems: A Workshop and Shared
Task | cs.CL | This paper presents a summary of the first Workshop on Building
Linguistically Generalizable Natural Language Processing Systems, and the
associated Build It Break It, The Language Edition shared task. The goal of
this workshop was to bring together researchers in NLP and linguistics with a
shared task aimed at testing... | computer science |
17,429 | Learning Word Embeddings from Speech | cs.CL | In this paper, we propose a novel deep neural network architecture,
Sequence-to-Sequence Audio2Vec, for unsupervised learning of fixed-length
vector representations of audio segments excised from a speech corpus, where
the vectors contain semantic information pertaining to the segments, and are
close to other vectors i... | computer science |
17,430 | Authorship Analysis of Xenophon's Cyropaedia | cs.CL | In the past several decades, many authorship attribution studies have used
computational methods to determine the authors of disputed texts. Disputed
authorship is a common problem in Classics, since little information about
ancient documents has survived the centuries. Many scholars have questioned the
authenticity of... | computer science |
17,431 | Distributed Representation for Traditional Chinese Medicine Herb via
Deep Learning Models | cs.CL | Traditional Chinese Medicine (TCM) has accumulated a big amount of precious
resource in the long history of development. TCM prescriptions that consist of
TCM herbs are an important form of TCM treatment, which are similar to natural
language documents, but in a weakly ordered fashion. Directly adapting language
modeli... | computer science |
17,432 | A Survey on Dialogue Systems: Recent Advances and New Frontiers | cs.CL | Dialogue systems have attracted more and more attention. Recent advances on
dialogue systems are overwhelmingly contributed by deep learning techniques,
which have been employed to enhance a wide range of big data applications such
as computer vision, natural language processing, and recommender systems. For
dialogue s... | computer science |
17,433 | Evaluation of Croatian Word Embeddings | cs.CL | Croatian is poorly resourced and highly inflected language from Slavic
language family. Nowadays, research is focusing mostly on English. We created a
new word analogy corpus based on the original English Word2vec word analogy
corpus and added some of the specific linguistic aspects from Croatian
language. Next, we cre... | computer science |
17,434 | Fine-tuning Tree-LSTM for phrase-level sentiment classification on a
Polish dependency treebank. Submission to PolEval task 2 | cs.CL | We describe a variant of Child-Sum Tree-LSTM deep neural network (Tai et al,
2015) fine-tuned for working with dependency trees and morphologically rich
languages using the example of Polish. Fine-tuning included applying a custom
regularization technique (zoneout, described by (Krueger et al., 2016), and
further adapt... | computer science |
17,435 | Neural Speed Reading via Skim-RNN | cs.CL | Inspired by the principles of speed reading, we introduce Skim-RNN, a
recurrent neural network (RNN) that dynamically decides to update only a small
fraction of the hidden state for relatively unimportant input tokens. Skim-RNN
gives computational advantage over an RNN that always updates the entire hidden
state. Skim-... | computer science |
17,436 | TAMU at KBP 2017: Event Nugget Detection and Coreference Resolution | cs.CL | In this paper, we describe TAMU's system submitted to the TAC KBP 2017 event
nugget detection and coreference resolution task. Our system builds on the
statistical and empirical observations made on training and development data.
We found that modifiers of event nuggets tend to have unique syntactic
distribution. Their... | computer science |
17,437 | Towards Language-Universal End-to-End Speech Recognition | cs.CL | Building speech recognizers in multiple languages typically involves
replicating a monolingual training recipe for each language, or utilizing a
multi-task learning approach where models for different languages have separate
output labels but share some internal parameters. In this work, we exploit
recent progress in e... | computer science |
17,438 | Improved training for online end-to-end speech recognition systems | cs.CL | Achieving high accuracy with end-to-end speech recognizers requires careful
parameter initialization prior to training. Otherwise, the networks may fail to
find a good local optimum. This is particularly true for low-latency online
networks, such as unidirectional LSTMs. Currently, the best strategy to train
such syste... | computer science |
17,439 | Structure Regularized Bidirectional Recurrent Convolutional Neural
Network for Relation Classification | cs.CL | Relation classification is an important semantic processing task in the field
of natural language processing (NLP). In this paper, we present a novel model,
Structure Regularized Bidirectional Recurrent Convolutional Neural
Network(SR-BRCNN), to classify the relation of two entities in a sentence, and
the new dataset o... | computer science |
17,440 | Extractive Multi-document Summarization Using Multilayer Networks | cs.CL | Huge volumes of textual information has been produced every single day. In
order to organize and understand such large datasets, in recent years,
summarization techniques have become popular. These techniques aims at finding
relevant, concise and non-redundant content from such a big data. While network
methods have be... | computer science |
17,441 | RubyStar: A Non-Task-Oriented Mixture Model Dialog System | cs.CL | RubyStar is a dialog system designed to create "human-like" conversation by
combining different response generation strategies. RubyStar conducts a
non-task-oriented conversation on general topics by using an ensemble of
rule-based, retrieval-based and generative methods. Topic detection, engagement
monitoring, and con... | computer science |
17,442 | Improving Hypernymy Extraction with Distributional Semantic Classes | cs.CL | In this paper, we show how distributionally-induced semantic classes can be
helpful for extracting hypernyms. We present methods for inducing sense-aware
semantic classes using distributional semantics and using these induced
semantic classes for filtering noisy hypernymy relations. Denoising of
hypernyms is performed ... | computer science |
17,443 | Weakly-supervised Relation Extraction by Pattern-enhanced Embedding
Learning | cs.CL | Extracting relations from text corpora is an important task in text mining.
It becomes particularly challenging when focusing on weakly-supervised relation
extraction, that is, utilizing a few relation instances (i.e., a pair of
entities and their relation) as seeds to extract more instances from corpora.
Existing dist... | computer science |
17,444 | An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading
Comprehension Tasks | cs.CL | Reading comprehension (RC) is a challenging task that requires synthesis of
information across sentences and multiple turns of reasoning. Using a
state-of-the-art RC model, we empirically investigate the performance of
single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The
RC model is an end-to... | computer science |
17,445 | Tracking of enriched dialog states for flexible conversational
information access | cs.CL | Dialog state tracking (DST) is a crucial component in a task-oriented dialog
system for conversational information access. A common practice in current
dialog systems is to define the dialog state by a set of slot-value pairs. Such
representation of dialog states and the slot-filling based DST have been widely
employed... | computer science |
17,446 | Language Modeling for Code-Switched Data: Challenges and Approaches | cs.CL | Lately, the problem of code-switching has gained a lot of attention and has
emerged as an active area of research. In bilingual communities, the speakers
commonly embed the words and phrases of a non-native language into the syntax
of a native language in their day-to-day communications. The code-switching is
a global ... | computer science |
17,447 | The Lifted Matrix-Space Model for Semantic Composition | cs.CL | Recent advances in tree structured sentence encoding models have shown that
explicitly modeling syntax can help handle compositionality. More specifically,
recent works by \citetext{Socher2012}, \citetext{Socher2013}, and
\citetext{Chen2013} have shown that using more powerful composition functions
with multiplicative ... | computer science |
17,448 | Document Context Neural Machine Translation with Memory Networks | cs.CL | We present a document-level neural machine translation model which takes both
the source and target document contexts into account using memory networks. We
model the problem as a structured prediction problem with interdependencies
among the observed and hidden variables, i.e., the source sentences and their
unobserve... | computer science |
17,449 | Integrating User and Agent Models: A Deep Task-Oriented Dialogue System | cs.CL | Task-oriented dialogue systems can efficiently serve a large number of
customers and relieve people from tedious works. However, existing
task-oriented dialogue systems depend on handcrafted actions and states or
extra semantic labels, which sometimes degrades user experience despite the
intensive human intervention. M... | computer science |
17,450 | Neural Skill Transfer from Supervised Language Tasks to Reading
Comprehension | cs.CL | Reading comprehension is a challenging task in natural language processing
and requires a set of skills to be solved. While current approaches focus on
solving the task as a whole, in this paper, we propose to use a neural network
`skill' transfer approach. We transfer knowledge from several lower-level
language tasks ... | computer science |
17,451 | YEDDA: A Lightweight Collaborative Text Span Annotation Tool | cs.CL | In this paper, we introduce YEDDA, a lightweight but efficient and
comprehensive open-source tool for text span annotation. YEDDA provides a
systematic solution for text span annotation, ranging from collaborative user
annotation to administrator evaluation and analysis. It overcomes the low
efficiency of traditional t... | computer science |
17,452 | Towards the Use of Deep Reinforcement Learning with Global Policy For
Query-based Extractive Summarisation | cs.CL | Supervised approaches for text summarisation suffer from the problem of
mismatch between the target labels/scores of individual sentences and the
evaluation score of the final summary. Reinforcement learning can solve this
problem by providing a learning mechanism that uses the score of the final
summary as a guide to ... | computer science |
17,453 | Towards Automated ICD Coding Using Deep Learning | cs.CL | International Classification of Diseases(ICD) is an authoritative health care
classification system of different diseases and conditions for clinical and
management purposes. Considering the complicated and dedicated process to
assign correct codes to each patient admission based on overall diagnosis, we
propose a hier... | computer science |
17,454 | Discovering conversational topics and emotions associated with
Demonetization tweets in India | cs.CL | Social media platforms contain great wealth of information which provides us
opportunities explore hidden patterns or unknown correlations, and understand
people's satisfaction with what they are discussing. As one showcase, in this
paper, we summarize the data set of Twitter messages related to recent
demonetization o... | computer science |
17,455 | Interpretable probabilistic embeddings: bridging the gap between topic
models and neural networks | cs.CL | We consider probabilistic topic models and more recent word embedding
techniques from a perspective of learning hidden semantic representations.
Inspired by a striking similarity of the two approaches, we merge them and
learn probabilistic embeddings with online EM-algorithm on word co-occurrence
data. The resulting em... | computer science |
17,456 | Syntax-Directed Attention for Neural Machine Translation | cs.CL | Attention mechanism, including global attention and local attention, plays a
key role in neural machine translation (NMT). Global attention attends to all
source words for word prediction. In comparison, local attention selectively
looks at fixed-window source words. However, alignment weights for the current
target wo... | computer science |
17,457 | Natural Language Inference with External Knowledge | cs.CL | Modeling informal inference in natural language is very challenging. With the
recent availability of large annotated data, it has become feasible to train
complex models such as neural networks to perform natural language inference
(NLI), which have achieved state-of-the-art performance. Although there exist
relatively... | computer science |
17,458 | Fast Reading Comprehension with ConvNets | cs.CL | State-of-the-art deep reading comprehension models are dominated by recurrent
neural nets. Their sequential nature is a natural fit for language, but it also
precludes parallelization within an instances and often becomes the bottleneck
for deploying such models to latency critical scenarios. This is particularly
probl... | computer science |
17,459 | Convolutional Neural Network with Word Embeddings for Chinese Word
Segmentation | cs.CL | Character-based sequence labeling framework is flexible and efficient for
Chinese word segmentation (CWS). Recently, many character-based neural models
have been applied to CWS. While they obtain good performance, they have two
obvious weaknesses. The first is that they heavily rely on manually designed
bigram feature,... | computer science |
17,460 | Word, Subword or Character? An Empirical Study of Granularity in
Chinese-English NMT | cs.CL | Neural machine translation (NMT), a new approach to machine translation, has
been proved to outperform conventional statistical machine translation (SMT)
across a variety of language pairs. Translation is an open-vocabulary problem,
but most existing NMT systems operate with a fixed vocabulary, which causes the
incapab... | computer science |
17,461 | Zero-Shot Style Transfer in Text Using Recurrent Neural Networks | cs.CL | Zero-shot translation is the task of translating between a language pair
where no aligned data for the pair is provided during training. In this work we
employ a model that creates paraphrases which are written in the style of
another existing text. Since we provide the model with no paired examples from
the source sty... | computer science |
17,462 | QuickEdit: Editing Text & Translations via Simple Delete Actions | cs.CL | We propose a framework for computer-assisted text editing. It applies to
translation post-editing and to paraphrasing and relies on very simple
interactions: a human editor modifies a sentence by marking tokens they would
like the system to change. Our model then generates a new sentence which
reformulates the initial ... | computer science |
17,463 | From Word Segmentation to POS Tagging for Vietnamese | cs.CL | This paper presents an empirical comparison of two strategies for Vietnamese
Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy
where we consider the output of a word segmenter as the input of a POS tagger,
and (ii) a joint strategy where we predict a combined segmentation and POS tag
for each ... | computer science |
17,464 | Classical Structured Prediction Losses for Sequence to Sequence Learning | cs.CL | There has been much recent work on training neural attention models at the
sequence-level using either reinforcement learning-style methods or by
optimizing the beam. In this paper, we survey a range of classical objective
functions that have been widely used to train linear models for structured
prediction and apply t... | computer science |
17,465 | Dynamic Fusion Networks for Machine Reading Comprehension | cs.CL | This paper presents a novel neural model - Dynamic Fusion Network (DFN), for
machine reading comprehension (MRC). DFNs differ from most state-of-the-art
models in their use of a dynamic multi-strategy attention process, in which
passages, questions and answer candidates are jointly fused into attention
vectors, along w... | computer science |
17,466 | Unified Pragmatic Models for Generating and Following Instructions | cs.CL | We extend models for both following and generating natural language
instructions by adding an explicit pragmatic layer. These pragmatics-enabled
models explicitly reason about why speakers produce certain instructions, and
about how listeners will react upon hearing them. Given learned base listener
and speaker models,... | computer science |
17,467 | Learning an Executable Neural Semantic Parser | cs.CL | This paper describes a neural semantic parser that maps natural language
utterances onto logical forms which can be executed against a task-specific
environment, such as a knowledge base or a database, to produce a response. The
parser generates tree-structured logical forms with a transition-based approach
which combi... | computer science |
17,468 | DuReader: a Chinese Machine Reading Comprehension Dataset from
Real-world Applications | cs.CL | In this paper, we introduce DuReader, a new large-scale, open-domain Chinese
machine reading comprehension (MRC) dataset, aiming to tackle real-world MRC
problems. In comparison to prior datasets, DuReader has the following
characteristics: (a) the questions and the documents are all extracted from
real application dat... | computer science |
17,469 | False Positive and Cross-relation Signals in Distant Supervision Data | cs.CL | Distant supervision (DS) is a well-established method for relation extraction
from text, based on the assumption that when a knowledge-base contains a
relation between a term pair, then sentences that contain that pair are likely
to express the relation. In this paper, we use the results of a crowdsourcing
relation ext... | computer science |
17,470 | Unsupervised patient representations from clinical notes with
interpretable classification decisions | cs.CL | We have two main contributions in this work: 1. We explore the usage of a
stacked denoising autoencoder, and a paragraph vector model to learn
task-independent dense patient representations directly from clinical notes. We
evaluate these representations by using them as features in multiple supervised
setups, and compa... | computer science |
17,471 | Controllable Abstractive Summarization | cs.CL | Current models for document summarization ignore user preferences such as the
desired length, style or entities that the user has a preference for. We
present a neural summarization model that enables users to specify such high
level attributes in order to control the shape of the final summaries to better
suit their n... | computer science |
17,472 | Modeling Semantic Relatedness using Global Relation Vectors | cs.CL | Word embedding models such as GloVe rely on co-occurrence statistics from a
large corpus to learn vector representations of word meaning. These vectors
have proven to capture surprisingly fine-grained semantic and syntactic
information. While we may similarly expect that co-occurrence statistics can be
used to capture ... | computer science |
17,473 | Simulating Action Dynamics with Neural Process Networks | cs.CL | Understanding procedural language requires anticipating the causal effects of
actions, even when they are not explicitly stated. In this work, we introduce
Neural Process Networks to understand procedural text through (neural)
simulation of action dynamics. Our model complements existing memory
architectures with dynam... | computer science |
17,474 | Supervised and Unsupervised Transfer Learning for Question Answering | cs.CL | Although transfer learning has been shown to be successful for tasks like
object and speech recognition, its applicability to question answering (QA) has
yet to be well-studied. In this paper, we conduct extensive experiments to
investigate the transferability of knowledge learned from a source QA dataset
to a target d... | computer science |
17,475 | A Deep Learning Approach for Expert Identification in Question Answering
Communities | cs.CL | In this paper, we describe an effective convolutional neural network
framework for identifying the expert in question answering community. This
approach uses the convolutional neural network and combines user feature
representations with question feature representations to compute scores that
the user who gets the high... | computer science |
17,476 | Bridging Source and Target Word Embeddings for Neural Machine
Translation | cs.CL | Neural machine translation systems encode a source sequence into a vector
from which a target sequence is generated via a decoder. Different from the
traditional statistical machine translation, source and target words are not
directly mapped to each other in translation rules. They are at the two ends of
a long inform... | computer science |
17,477 | A Sequential Neural Encoder with Latent Structured Description for
Modeling Sentences | cs.CL | In this paper, we propose a sequential neural encoder with latent structured
description (SNELSD) for modeling sentences. This model introduces latent
chunk-level representations into conventional sequential neural encoders, i.e.,
recurrent neural networks (RNNs) with long short-term memory (LSTM) units, to
consider th... | computer science |
17,478 | Aicyber's System for NLPCC 2017 Shared Task 2: Voting of Baselines | cs.CL | This paper presents Aicyber's system for NLPCC 2017 shared task 2. It is
formed by a voting of three deep learning based system trained on
character-enhanced word vectors and a well known bag-of-word model. | computer science |
17,479 | Tracking Typological Traits of Uralic Languages in Distributed Language
Representations | cs.CL | Although linguistic typology has a long history, computational approaches
have only recently gained popularity. The use of distributed representations in
computational linguistics has also become increasingly popular. A recent
development is to learn distributed representations of language, such that
typologically simi... | computer science |
17,480 | Investigating Inner Properties of Multimodal Representation and Semantic
Compositionality with Brain-based Componential Semantics | cs.CL | Multimodal models have been proven to outperform text-based approaches on
learning semantic representations. However, it still remains unclear what
properties are encoded in multimodal representations, in what aspects do they
outperform the single-modality representations, and what happened in the
process of semantic c... | computer science |
17,481 | Detecting and assessing contextual change in diachronic text documents
using context volatility | cs.CL | Terms in diachronic text corpora may exhibit a high degree of semantic
dynamics that is only partially captured by the common notion of semantic
change. The new measure of context volatility that we propose models the degree
by which terms change context in a text collection over time. The computation
of context volati... | computer science |
17,482 | Dialogue Act Recognition via CRF-Attentive Structured Network | cs.CL | Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from han... | computer science |
17,483 | Words are Malleable: Computing Semantic Shifts in Political and Media
Discourse | cs.CL | Recently, researchers started to pay attention to the detection of temporal
shifts in the meaning of words. However, most (if not all) of these approaches
restricted their efforts to uncovering change over time, thus neglecting other
valuable dimensions such as social or political variability. We propose an
approach fo... | computer science |
17,484 | Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time | cs.CL | Dynamic topic modeling facilitates the identification of topical trends over
time in temporal collections of unstructured documents. We introduce a novel
unsupervised neural dynamic topic model known as Recurrent Neural
Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
time influence the to... | computer science |
17,485 | Unsupervised Morphological Expansion of Small Datasets for Improving
Word Embeddings | cs.CL | We present a language independent, unsupervised method for building word
embeddings using morphological expansion of text. Our model handles the problem
of data sparsity and yields improved word embeddings by relying on training
word embeddings on artificially generated sentences. We evaluate our method
using small siz... | computer science |
17,486 | An Unsupervised Approach for Mapping between Vector Spaces | cs.CL | We present a language independent, unsupervised approach for transforming
word embeddings from source language to target language using a transformation
matrix. Our model handles the problem of data scarcity which is faced by many
languages in the world and yields improved word embeddings for words in the
target langua... | computer science |
17,487 | Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of
Machine Translations | cs.CL | We extend the work of Wieting et al. (2017), back-translating a large
parallel corpus to produce a dataset of more than 51 million English-English
sentential paraphrase pairs in a dataset we call ParaNMT-50M. We find this
corpus to be cover many domains and styles of text, in addition to being rich
in paraphrases with ... | computer science |
17,488 | Detecting Egregious Conversations between Customers and Virtual Agents | cs.CL | Virtual agents are becoming a prominent channel of interaction in customer
service. Not all customer interactions are smooth, however, and some can become
almost comically bad. In such instances, a human agent might need to step in
and salvage the conversation. Detecting bad conversations is important since
disappointi... | computer science |
17,489 | Crowdsourcing Question-Answer Meaning Representations | cs.CL | We introduce Question-Answer Meaning Representations (QAMRs), which represent
the predicate-argument structure of a sentence as a set of question-answer
pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled
with very little training, and gather a dataset with over 5,000 sentences and
100,000 q... | computer science |
17,490 | An Encoder-Decoder Framework Translating Natural Language to Database
Queries | cs.CL | Machine translation is going through a radical revolution, driven by the
explosive development of deep learning techniques using Convolutional Neural
Network (CNN) and Recurrent Neural Network (RNN). In this paper, we consider a
special case in machine translation problems, targeting to translate natural
language into ... | computer science |
17,491 | ConvAMR: Abstract meaning representation parsing for legal document | cs.CL | Convolutional neural networks (CNN) have recently achieved remarkable
performance in a wide range of applications. In this research, we equip
convolutional sequence-to-sequence (seq2seq) model with an efficient graph
linearization technique for abstract meaning representation parsing. Our
linearization method is better... | computer science |
17,492 | An Abstractive approach to Question Answering | cs.CL | Question Answering has come a long way from answer sentence selection,
relational QA to reading and comprehension. We move our attention to
abstractive question answering by which we facilitate machine to read passages
and answer questions by generating them. We frame the problem as a sequence to
sequence learning wher... | computer science |
17,493 | Phonological (un)certainty weights lexical activation | cs.CL | Spoken word recognition involves at least two basic computations. First is
matching acoustic input to phonological categories (e.g. /b/, /p/, /d/). Second
is activating words consistent with those phonological categories. Here we test
the hypothesis that the listener's probability distribution over lexical items
is wei... | computer science |
17,494 | Low-dimensional Embeddings for Interpretable Anchor-based Topic
Inference | cs.CL | The anchor words algorithm performs provably efficient topic model inference
by finding an approximate convex hull in a high-dimensional word co-occurrence
space. However, the existing greedy algorithm often selects poor anchor words,
reducing topic quality and interpretability. Rather than finding an approximate
conve... | computer science |
17,495 | Style Transfer in Text: Exploration and Evaluation | cs.CL | Style transfer is an important problem in natural language processing (NLP).
However, the progress in language style transfer is lagged behind other
domains, such as computer vision, mainly because of the lack of parallel data
and principle evaluation metrics. In this paper, we propose to learn style
transfer with non-... | computer science |
17,496 | Automatically Extracting Action Graphs from Materials Science Synthesis
Procedures | cs.CL | Computational synthesis planning approaches have achieved recent success in
organic chemistry, where tabulated synthesis procedures are readily available
for supervised learning. The syntheses of inorganic materials, however, exist
primarily as natural language narratives contained within scientific journal
articles. T... | computer science |
17,497 | Is China Entering WTO or shijie maoyi zuzhi--a Corpus Study of English
Acronyms in Chinese Newspapers | cs.CL | This is one of the first studies that quantitatively examine the usage of
English acronyms (e.g. WTO) in Chinese texts. Using newspaper corpora, I try to
answer 1) for all instances of a concept that has an English acronym (e.g.
World Trade Organization), what percentage is expressed in the English acronym
(WTO), and w... | computer science |
17,498 | A Discourse-Level Named Entity Recognition and Relation Extraction
Dataset for Chinese Literature Text | cs.CL | Named Entity Recognition and Relation Extraction for Chinese literature text
is regarded as the highly difficult problem, partially because of the lack of
tagging sets. In this paper, we build a discourse-level dataset from hundreds
of Chinese literature articles for improving this task. To build a high quality
dataset... | computer science |
17,499 | Incorporating Syntactic Uncertainty in Neural Machine Translation with
Forest-to-Sequence Model | cs.CL | Incorporating syntactic information in Neural Machine Translation models is a
method to compensate their requirement for a large amount of parallel training
text, especially for low-resource language pairs. Previous works on using
syntactic information provided by (inevitably error-prone) parsers has been
promising. In... | computer science |
17,500 | Fast BTG-Forest-Based Hierarchical Sub-sentential Alignment | cs.CL | In this paper, we propose a novel BTG-forest-based alignment method. Based on
a fast unsupervised initialization of parameters using variational IBM models,
we synchronously parse parallel sentences top-down and align hierarchically
under the constraint of BTG. Our two-step method can achieve the same run-time
and comp... | computer science |
17,501 | Non-Contextual Modeling of Sarcasm using a Neural Network Benchmark | cs.CL | One of the most crucial components of natural human-robot interaction is
artificial intuition and its influence on dialog systems. The intuitive
capability that humans have is undeniably extraordinary, and so remains one of
the greatest challenges for natural communicative dialogue between humans and
robots. In this pa... | computer science |
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