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16,802 | Content-Based Table Retrieval for Web Queries | cs.CL | Understanding the connections between unstructured text and semi-structured
table is an important yet neglected problem in natural language processing. In
this work, we focus on content-based table retrieval. Given a query, the task
is to find the most relevant table from a collection of tables. Further
progress toward... | computer science |
16,803 | Improving Semantic Relevance for Sequence-to-Sequence Learning of
Chinese Social Media Text Summarization | cs.CL | Current Chinese social media text summarization models are based on an
encoder-decoder framework. Although its generated summaries are similar to
source texts literally, they have low semantic relevance. In this work, our
goal is to improve semantic relevance between source texts and summaries for
Chinese social media ... | computer science |
16,804 | The Algorithmic Inflection of Russian and Generation of Grammatically
Correct Text | cs.CL | We present a deterministic algorithm for Russian inflection. This algorithm
is implemented in a publicly available web-service www.passare.ru which
provides functions for inflection of single words, word matching and synthesis
of grammatically correct Russian text. The inflectional functions have been
tested against th... | computer science |
16,805 | Advances in Joint CTC-Attention based End-to-End Speech Recognition with
a Deep CNN Encoder and RNN-LM | cs.CL | We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)
model. We learn to listen and write characters with a joint Connectionist
Temporal Classification (CTC) and attention-based encoder-decoder network. The
encoder is a deep Convolutional Neural Network (CNN) based on the VGG network.
The CTC netw... | computer science |
16,806 | Learning to Embed Words in Context for Syntactic Tasks | cs.CL | We present models for embedding words in the context of surrounding words.
Such models, which we refer to as token embeddings, represent the
characteristics of a word that are specific to a given context, such as word
sense, syntactic category, and semantic role. We explore simple, efficient
token embedding models base... | computer science |
16,807 | Assigning personality/identity to a chatting machine for coherent
conversation generation | cs.CL | Endowing a chatbot with personality or an identity is quite challenging but
critical to deliver more realistic and natural conversations. In this paper, we
address the issue of generating responses that are coherent to a pre-specified
agent profile. We design a model consisting of three modules: a profile
detector to d... | computer science |
16,808 | Overview of the NLPCC 2017 Shared Task: Chinese News Headline
Categorization | cs.CL | In this paper, we give an overview for the shared task at the CCF Conference
on Natural Language Processing \& Chinese Computing (NLPCC 2017): Chinese News
Headline Categorization. The dataset of this shared task consists 18 classes,
12,000 short texts along with corresponded labels for each class. The dataset
and exam... | computer science |
16,809 | Deriving a Representative Vector for Ontology Classes with Instance Word
Vector Embeddings | cs.CL | Selecting a representative vector for a set of vectors is a very common
requirement in many algorithmic tasks. Traditionally, the mean or median vector
is selected. Ontology classes are sets of homogeneous instance objects that can
be converted to a vector space by word vector embeddings. This study proposes a
methodol... | computer science |
16,810 | Trimming and Improving Skip-thought Vectors | cs.CL | The skip-thought model has been proven to be effective at learning sentence
representations and capturing sentence semantics. In this paper, we propose a
suite of techniques to trim and improve it. First, we validate a hypothesis
that, given a current sentence, inferring the previous and inferring the next
sentence pro... | computer science |
16,811 | Classification of Questions and Learning Outcome Statements (LOS) Into
Blooms Taxonomy (BT) By Similarity Measurements Towards Extracting Of
Learning Outcome from Learning Material | cs.CL | Blooms Taxonomy (BT) have been used to classify the objectives of learning
outcome by dividing the learning into three different domains; the cognitive
domain, the effective domain and the psychomotor domain. In this paper, we are
introducing a new approach to classify the questions and learning outcome
statements (LOS... | computer science |
16,812 | Articulation rate in Swedish child-directed speech increases as a
function of the age of the child even when surprisal is controlled for | cs.CL | In earlier work, we have shown that articulation rate in Swedish
child-directed speech (CDS) increases as a function of the age of the child,
even when utterance length and differences in articulation rate between
subjects are controlled for. In this paper we show on utterance level in
spontaneous Swedish speech that i... | computer science |
16,813 | Exploring Automated Essay Scoring for Nonnative English Speakers | cs.CL | Automated Essay Scoring (AES) has been quite popular and is being widely
used. However, lack of appropriate methodology for rating nonnative English
speakers' essays has meant a lopsided advancement in this field. In this paper,
we report initial results of our experiments with nonnative AES that learns
from manual eva... | computer science |
16,814 | A Full Non-Monotonic Transition System for Unrestricted Non-Projective
Parsing | cs.CL | Restricted non-monotonicity has been shown beneficial for the projective
arc-eager dependency parser in previous research, as posterior decisions can
repair mistakes made in previous states due to the lack of information. In this
paper, we propose a novel, fully non-monotonic transition system based on the
non-projecti... | computer science |
16,815 | Dialog Structure Through the Lens of Gender, Gender Environment, and
Power | cs.CL | Understanding how the social context of an interaction affects our dialog
behavior is of great interest to social scientists who study human behavior, as
well as to computer scientists who build automatic methods to infer those
social contexts. In this paper, we study the interaction of power, gender, and
dialog behavi... | computer science |
16,816 | SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological
Inflection with Attentional Sequence-to-Sequence Models | cs.CL | This paper describes the Stockholm University/University of Groningen
(SU-RUG) system for the SIGMORPHON 2017 shared task on morphological
inflection. Our system is based on an attentional sequence-to-sequence neural
network model using Long Short-Term Memory (LSTM) cells, with joint training of
morphological inflectio... | computer science |
16,817 | Candidate sentence selection for language learning exercises: from a
comprehensive framework to an empirical evaluation | cs.CL | We present a framework and its implementation relying on Natural Language
Processing methods, which aims at the identification of exercise item
candidates from corpora. The hybrid system combining heuristics and machine
learning methods includes a number of relevant selection criteria. We focus on
two fundamental aspec... | computer science |
16,818 | Exploring the Syntactic Abilities of RNNs with Multi-task Learning | cs.CL | Recent work has explored the syntactic abilities of RNNs using the
subject-verb agreement task, which diagnoses sensitivity to sentence structure.
RNNs performed this task well in common cases, but faltered in complex
sentences (Linzen et al., 2016). We test whether these errors are due to
inherent limitations of the a... | computer science |
16,819 | Acoustic data-driven lexicon learning based on a greedy pronunciation
selection framework | cs.CL | Speech recognition systems for irregularly-spelled languages like English
normally require hand-written pronunciations. In this paper, we describe a
system for automatically obtaining pronunciations of words for which
pronunciations are not available, but for which transcribed data exists. Our
method integrates informa... | computer science |
16,820 | Verb Physics: Relative Physical Knowledge of Actions and Objects | cs.CL | Learning commonsense knowledge from natural language text is nontrivial due
to reporting bias: people rarely state the obvious, e.g., "My house is bigger
than me." However, while rarely stated explicitly, this trivial everyday
knowledge does influence the way people talk about the world, which provides
indirect clues t... | computer science |
16,821 | Query-by-Example Search with Discriminative Neural Acoustic Word
Embeddings | cs.CL | Query-by-example search often uses dynamic time warping (DTW) for comparing
queries and proposed matching segments. Recent work has shown that comparing
speech segments by representing them as fixed-dimensional vectors --- acoustic
word embeddings --- and measuring their vector distance (e.g., cosine distance)
can disc... | computer science |
16,822 | Attention-based Vocabulary Selection for NMT Decoding | cs.CL | Neural Machine Translation (NMT) models usually use large target vocabulary
sizes to capture most of the words in the target language. The vocabulary size
is a big factor when decoding new sentences as the final softmax layer
normalizes over all possible target words. To address this problem, it is
widely common to res... | computer science |
16,823 | Six Challenges for Neural Machine Translation | cs.CL | We explore six challenges for neural machine translation: domain mismatch,
amount of training data, rare words, long sentences, word alignment, and beam
search. We show both deficiencies and improvements over the quality of
phrase-based statistical machine translation. | computer science |
16,824 | Modelling prosodic structure using Artificial Neural Networks | cs.CL | The ability to accurately perceive whether a speaker is asking a question or
is making a statement is crucial for any successful interaction. However,
learning and classifying tonal patterns has been a challenging task for
automatic speech recognition and for models of tonal representation, as tonal
contours are charac... | computer science |
16,825 | An Exploration of Neural Sequence-to-Sequence Architectures for
Automatic Post-Editing | cs.CL | In this work, we explore multiple neural architectures adapted for the task
of automatic post-editing of machine translation output. We focus on neural
end-to-end models that combine both inputs $mt$ (raw MT output) and $src$
(source language input) in a single neural architecture, modeling $\{mt, src\}
\rightarrow pe$... | computer science |
16,826 | Fine-grained human evaluation of neural versus phrase-based machine
translation | cs.CL | We compare three approaches to statistical machine translation (pure
phrase-based, factored phrase-based and neural) by performing a fine-grained
manual evaluation via error annotation of the systems' outputs. The error types
in our annotation are compliant with the multidimensional quality metrics
(MQM), and the annot... | computer science |
16,827 | Idea density for predicting Alzheimer's disease from transcribed speech | cs.CL | Idea Density (ID) measures the rate at which ideas or elementary predications
are expressed in an utterance or in a text. Lower ID is found to be associated
with an increased risk of developing Alzheimer's disease (AD) (Snowdon et al.,
1996; Engelman et al., 2010). ID has been used in two different versions:
propositio... | computer science |
16,828 | S-Net: From Answer Extraction to Answer Generation for Machine Reading
Comprehension | cs.CL | In this paper, we present a novel approach to machine reading comprehension
for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a
question with exact text spans in a passage, the MS-MARCO dataset defines the
task as answering a question from multiple passages and the words in the answer
are not neces... | computer science |
16,829 | German in Flux: Detecting Metaphoric Change via Word Entropy | cs.CL | This paper explores the information-theoretic measure entropy to detect
metaphoric change, transferring ideas from hypernym detection to research on
language change. We also build the first diachronic test set for German as a
standard for metaphoric change annotation. Our model shows high performance, is
unsupervised, ... | computer science |
16,830 | Extracting Formal Models from Normative Texts | cs.CL | We are concerned with the analysis of normative texts - documents based on
the deontic notions of obligation, permission, and prohibition. Our goal is to
make queries about these notions and verify that a text satisfies certain
properties concerning causality of actions and timing constraints. This
requires taking the ... | computer science |
16,831 | Ensembling Factored Neural Machine Translation Models for Automatic
Post-Editing and Quality Estimation | cs.CL | This work presents a novel approach to Automatic Post-Editing (APE) and
Word-Level Quality Estimation (QE) using ensembles of specialized Neural
Machine Translation (NMT) systems. Word-level features that have proven
effective for QE are included as input factors, expanding the representation of
the original source and... | computer science |
16,832 | A Mixture Model for Learning Multi-Sense Word Embeddings | cs.CL | Word embeddings are now a standard technique for inducing meaning
representations for words. For getting good representations, it is important to
take into account different senses of a word. In this paper, we propose a
mixture model for learning multi-sense word embeddings. Our model generalizes
the previous works in ... | computer science |
16,833 | An Automatic Approach for Document-level Topic Model Evaluation | cs.CL | Topic models jointly learn topics and document-level topic distribution.
Extrinsic evaluation of topic models tends to focus exclusively on topic-level
evaluation, e.g. by assessing the coherence of topics. We demonstrate that
there can be large discrepancies between topic- and document-level model
quality, and that ba... | computer science |
16,834 | Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural
Network Approach | cs.CL | Knowledge base completion (KBC) aims to predict missing information in a
knowledge base.In this paper, we address the out-of-knowledge-base (OOKB)
entity problem in KBC:how to answer queries concerning test entities not
observed at training time. Existing embedding-based KBC models assume that all
test entities are ava... | computer science |
16,835 | Detecting Large Concept Extensions for Conceptual Analysis | cs.CL | When performing a conceptual analysis of a concept, philosophers are
interested in all forms of expression of a concept in a text---be it direct or
indirect, explicit or implicit. In this paper, we experiment with topic-based
methods of automating the detection of concept expressions in order to
facilitate philosophica... | computer science |
16,836 | An Empirical Study of Mini-Batch Creation Strategies for Neural Machine
Translation | cs.CL | Training of neural machine translation (NMT) models usually uses mini-batches
for efficiency purposes. During the mini-batched training process, it is
necessary to pad shorter sentences in a mini-batch to be equal in length to the
longest sentence therein for efficient computation. Previous work has noted
that sorting ... | computer science |
16,837 | Topic Modeling for Classification of Clinical Reports | cs.CL | Electronic health records (EHRs) contain important clinical information about
patients. Efficient and effective use of this information could supplement or
even replace manual chart review as a means of studying and improving the
quality and safety of healthcare delivery. However, some of these clinical data
are in the... | computer science |
16,838 | Improving text classification with vectors of reduced precision | cs.CL | This paper presents the analysis of the impact of a floating-point number
precision reduction on the quality of text classification. The precision
reduction of the vectors representing the data (e.g. TF-IDF representation in
our case) allows for a decrease of computing time and memory footprint on
dedicated hardware pl... | computer science |
16,839 | THUMT: An Open Source Toolkit for Neural Machine Translation | cs.CL | This paper introduces THUMT, an open-source toolkit for neural machine
translation (NMT) developed by the Natural Language Processing Group at
Tsinghua University. THUMT implements the standard attention-based
encoder-decoder framework on top of Theano and supports three training
criteria: maximum likelihood estimation... | computer science |
16,840 | Extract with Order for Coherent Multi-Document Summarization | cs.CL | In this work, we aim at developing an extractive summarizer in the
multi-document setting. We implement a rank based sentence selection using
continuous vector representations along with key-phrases. Furthermore, we
propose a model to tackle summary coherence for increasing readability. We
conduct experiments on the Do... | computer science |
16,841 | Cross-language Learning with Adversarial Neural Networks: Application to
Community Question Answering | cs.CL | We address the problem of cross-language adaptation for question-question
similarity reranking in community question answering, with the objective to
port a system trained on one input language to another input language given
labeled training data for the first language and only unlabeled data for the
second language. ... | computer science |
16,842 | JaTeCS an open-source JAva TExt Categorization System | cs.CL | JaTeCS is an open source Java library that supports research on automatic
text categorization and other related problems, such as ordinal regression and
quantification, which are of special interest in opinion mining applications.
It covers all the steps of an experimental activity, from reading the corpus to
the evalu... | computer science |
16,843 | Stance Detection in Turkish Tweets | cs.CL | Stance detection is a classification problem in natural language processing
where for a text and target pair, a class result from the set {Favor, Against,
Neither} is expected. It is similar to the sentiment analysis problem but
instead of the sentiment of the text author, the stance expressed for a
particular target i... | computer science |
16,844 | Effective Spoken Language Labeling with Deep Recurrent Neural Networks | cs.CL | Understanding spoken language is a highly complex problem, which can be
decomposed into several simpler tasks. In this paper, we focus on Spoken
Language Understanding (SLU), the module of spoken dialog systems responsible
for extracting a semantic interpretation from the user utterance. The task is
treated as a labeli... | computer science |
16,845 | Automatic Quality Estimation for ASR System Combination | cs.CL | Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and othe... | computer science |
16,846 | End-to-end Conversation Modeling Track in DSTC6 | cs.CL | End-to-end training of neural networks is a promising approach to automatic
construction of dialog systems using a human-to-human dialog corpus. Recently,
Vinyals et al. tested neural conversation models using OpenSubtitles. Lowe et
al. released the Ubuntu Dialogue Corpus for researching unstructured multi-turn
dialogu... | computer science |
16,847 | Personalization in Goal-Oriented Dialog | cs.CL | The main goal of modeling human conversation is to create agents which can
interact with people in both open-ended and goal-oriented scenarios. End-to-end
trained neural dialog systems are an important line of research for such
generalized dialog models as they do not resort to any situation-specific
handcrafting of ru... | computer science |
16,848 | Neural Machine Translation with Gumbel-Greedy Decoding | cs.CL | Previous neural machine translation models used some heuristic search
algorithms (e.g., beam search) in order to avoid solving the maximum a
posteriori problem over translation sentences at test time. In this paper, we
propose the Gumbel-Greedy Decoding which trains a generative network to predict
translation under a t... | computer science |
16,849 | Named Entity Recognition with stack residual LSTM and trainable bias
decoding | cs.CL | Recurrent Neural Network models are the state-of-the-art for Named Entity
Recognition (NER). We present two innovations to improve the performance of
these models. The first innovation is the introduction of residual connections
between the Stacked Recurrent Neural Network model to address the degradation
problem of de... | computer science |
16,850 | Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques
in Statistical Language Model of Bahasa Indonesia | cs.CL | Smoothing is one technique to overcome data sparsity in statistical language
model. Although in its mathematical definition there is no explicit dependency
upon specific natural language, different natures of natural languages result
in different effects of smoothing techniques. This is true for Russian language
as sho... | computer science |
16,851 | Encoder-Decoder Shift-Reduce Syntactic Parsing | cs.CL | Starting from NMT, encoder-decoder neu- ral networks have been used for many
NLP problems. Graph-based models and transition-based models borrowing the en-
coder components achieve state-of-the-art performance on dependency parsing and
constituent parsing, respectively. How- ever, there has not been work
empirically st... | computer science |
16,852 | A Deep Neural Architecture for Sentence-level Sentiment Classification
in Twitter Social Networking | cs.CL | This paper introduces a novel deep learning framework including a
lexicon-based approach for sentence-level prediction of sentiment label
distribution. We propose to first apply semantic rules and then use a Deep
Convolutional Neural Network (DeepCNN) for character-level embeddings in order
to increase information for ... | computer science |
16,853 | Automated text summarisation and evidence-based medicine: A survey of
two domains | cs.CL | The practice of evidence-based medicine (EBM) urges medical practitioners to
utilise the latest research evidence when making clinical decisions. Because of
the massive and growing volume of published research on various medical topics,
practitioners often find themselves overloaded with information. As such,
natural l... | computer science |
16,854 | Automatic Synonym Discovery with Knowledge Bases | cs.CL | Recognizing entity synonyms from text has become a crucial task in many
entity-leveraging applications. However, discovering entity synonyms from
domain-specific text corpora (e.g., news articles, scientific papers) is rather
challenging. Current systems take an entity name string as input to find out
other names that ... | computer science |
16,855 | English-Japanese Neural Machine Translation with
Encoder-Decoder-Reconstructor | cs.CL | Neural machine translation (NMT) has recently become popular in the field of
machine translation. However, NMT suffers from the problem of repeating or
missing words in the translation. To address this problem, Tu et al. (2017)
proposed an encoder-decoder-reconstructor framework for NMT using
back-translation. In this ... | computer science |
16,856 | Memory-augmented Chinese-Uyghur Neural Machine Translation | cs.CL | Neural machine translation (NMT) has achieved notable performance recently.
However, this approach has not been widely applied to the translation task
between Chinese and Uyghur, partly due to the limited parallel data resource
and the large proportion of rare words caused by the agglutinative nature of
Uyghur. In this... | computer science |
16,857 | CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection
in 52 Languages | cs.CL | The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation
required systems to be trained and tested in each of 52 typologically diverse
languages. In sub-task 1, submitted systems were asked to predict a specific
inflected form of a given lemma. In sub-task 2, systems were given a lemma and
some of i... | computer science |
16,858 | Named Entity Disambiguation for Noisy Text | cs.CL | We address the task of Named Entity Disambiguation (NED) for noisy text. We
present WikilinksNED, a large-scale NED dataset of text fragments from the web,
which is significantly noisier and more challenging than existing news-based
datasets. To capture the limited and noisy local context surrounding each
mention, we d... | computer science |
16,859 | The E2E Dataset: New Challenges For End-to-End Generation | cs.CL | This paper describes the E2E data, a new dataset for training end-to-end,
data-driven natural language generation systems in the restaurant domain, which
is ten times bigger than existing, frequently used datasets in this area. The
E2E dataset poses new challenges: (1) its human reference texts show more
lexical richne... | computer science |
16,860 | Generating Appealing Brand Names | cs.CL | Providing appealing brand names to newly launched products, newly formed
companies or for renaming existing companies is highly important as it can play
a crucial role in deciding its success or failure. In this work, we propose a
computational method to generate appealing brand names based on the description
of such e... | computer science |
16,861 | Data-driven Natural Language Generation: Paving the Road to Success | cs.CL | We argue that there are currently two major bottlenecks to the commercial use
of statistical machine learning approaches for natural language generation
(NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b)
The scarcity of high quality in-domain corpora. We address the first problem by
thoroughl... | computer science |
16,862 | Toward Computation and Memory Efficient Neural Network Acoustic Models
with Binary Weights and Activations | cs.CL | Neural network acoustic models have significantly advanced state of the art
speech recognition over the past few years. However, they are usually
computationally expensive due to the large number of matrix-vector
multiplications and nonlinearity operations. Neural network models also require
significant amounts of memo... | computer science |
16,863 | Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a
Syntactic Scaffold | cs.CL | We present a new, efficient frame-semantic parser that labels semantic
arguments to FrameNet predicates. Built using an extension to the segmental RNN
that emphasizes recall, our basic system achieves competitive performance
without any calls to a syntactic parser. We then introduce a method that uses
phrase-syntactic ... | computer science |
16,864 | Frame-Based Continuous Lexical Semantics through Exponential Family
Tensor Factorization and Semantic Proto-Roles | cs.CL | We study how different frame annotations complement one another when learning
continuous lexical semantics. We learn the representations from a tensorized
skip-gram model that consistently encodes syntactic-semantic content better,
with multiple 10% gains over baselines. | computer science |
16,865 | Recurrent neural networks with specialized word embeddings for
health-domain named-entity recognition | cs.CL | Background. Previous state-of-the-art systems on Drug Name Recognition (DNR)
and Clinical Concept Extraction (CCE) have focused on a combination of text
"feature engineering" and conventional machine learning algorithms such as
conditional random fields and support vector machines. However, developing good
features is ... | computer science |
16,866 | Improving Distributed Representations of Tweets - Present and Future | cs.CL | Unsupervised representation learning for tweets is an important research
field which helps in solving several business applications such as sentiment
analysis, hashtag prediction, paraphrase detection and microblog ranking. A
good tweet representation learning model must handle the idiosyncratic nature
of tweets which ... | computer science |
16,867 | Stronger Baselines for Trustable Results in Neural Machine Translation | cs.CL | Interest in neural machine translation has grown rapidly as its effectiveness
has been demonstrated across language and data scenarios. New research
regularly introduces architectural and algorithmic improvements that lead to
significant gains over "vanilla" NMT implementations. However, these new
techniques are rarely... | computer science |
16,868 | AP17-OLR Challenge: Data, Plan, and Baseline | cs.CL | We present the data profile and the evaluation plan of the second oriental
language recognition (OLR) challenge AP17-OLR. Compared to the event last year
(AP16-OLR), the new challenge involves more languages and focuses more on short
utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two
types o... | computer science |
16,869 | Two-Stage Synthesis Networks for Transfer Learning in Machine
Comprehension | cs.CL | We develop a technique for transfer learning in machine comprehension (MC)
using a novel two-stage synthesis network (SynNet). Given a high-performing MC
model in one domain, our technique aims to answer questions about documents in
another domain, where we use no labeled data of question-answer pairs. Using
the propos... | computer science |
16,870 | Relevance of Unsupervised Metrics in Task-Oriented Dialogue for
Evaluating Natural Language Generation | cs.CL | Automated metrics such as BLEU are widely used in the machine translation
literature. They have also been used recently in the dialogue community for
evaluating dialogue response generation. However, previous work in dialogue
response generation has shown that these metrics do not correlate strongly with
human judgment... | computer science |
16,871 | Automatic Mapping of French Discourse Connectives to PDTB Discourse
Relations | cs.CL | In this paper, we present an approach to exploit phrase tables generated by
statistical machine translation in order to map French discourse connectives to
discourse relations. Using this approach, we created ConcoLeDisCo, a lexicon of
French discourse connectives and their PDTB relations. When evaluated against
LEXCON... | computer science |
16,872 | Synthetic Data for Neural Machine Translation of Spoken-Dialects | cs.CL | In this paper, we introduce a novel approach to generate synthetic data for
training Neural Machine Translation systems. The proposed approach transforms a
given parallel corpus between a written language and a target language to a
parallel corpus between a spoken dialect variant and the target language. Our
approach i... | computer science |
16,873 | Efficient Attention using a Fixed-Size Memory Representation | cs.CL | The standard content-based attention mechanism typically used in
sequence-to-sequence models is computationally expensive as it requires the
comparison of large encoder and decoder states at each time step. In this work,
we propose an alternative attention mechanism based on a fixed size memory
representation that is m... | computer science |
16,874 | Heterogeneous Supervision for Relation Extraction: A Representation
Learning Approach | cs.CL | Relation extraction is a fundamental task in information extraction. Most
existing methods have heavy reliance on annotations labeled by human experts,
which are costly and time-consuming. To overcome this drawback, we propose a
novel framework, REHession, to conduct relation extractor learning using
annotations from h... | computer science |
16,875 | DAG-based Long Short-Term Memory for Neural Word Segmentation | cs.CL | Neural word segmentation has attracted more and more research interests for
its ability to alleviate the effort of feature engineering and utilize the
external resource by the pre-trained character or word embeddings. In this
paper, we propose a new neural model to incorporate the word-level information
for Chinese wor... | computer science |
16,876 | Grammatical Error Correction with Neural Reinforcement Learning | cs.CL | We propose a neural encoder-decoder model with reinforcement learning (NRL)
for grammatical error correction (GEC). Unlike conventional maximum likelihood
estimation (MLE), the model directly optimizes towards an objective that
considers a sentence-level, task-specific evaluation metric, avoiding the
exposure bias issu... | computer science |
16,877 | Including Dialects and Language Varieties in Author Profiling | cs.CL | This paper presents a computational approach to author profiling taking
gender and language variety into account. We apply an ensemble system with the
output of multiple linear SVM classifiers trained on character and word
$n$-grams. We evaluate the system using the dataset provided by the organizers
of the 2017 PAN la... | computer science |
16,878 | Improving LSTM-CTC based ASR performance in domains with limited
training data | cs.CL | This paper addresses the observed performance gap between automatic speech
recognition (ASR) systems based on Long Short Term Memory (LSTM) neural
networks trained with the connectionist temporal classification (CTC) loss
function and systems based on hybrid Deep Neural Networks (DNNs) trained with
the cross entropy (C... | computer science |
16,879 | Multilingual Hierarchical Attention Networks for Document Classification | cs.CL | Hierarchical attention networks have recently achieved remarkable performance
for document classification in a given language. However, when multilingual
document collections are considered, training such models separately for each
language entails linear parameter growth and lack of cross-language transfer.
Learning a... | computer science |
16,880 | An empirical study on the effectiveness of images in Multimodal Neural
Machine Translation | cs.CL | In state-of-the-art Neural Machine Translation (NMT), an attention mechanism
is used during decoding to enhance the translation. At every step, the decoder
uses this mechanism to focus on different parts of the source sentence to
gather the most useful information before outputting its target word. Recently,
the effect... | computer science |
16,881 | Visually Grounded Word Embeddings and Richer Visual Features for
Improving Multimodal Neural Machine Translation | cs.CL | In Multimodal Neural Machine Translation (MNMT), a neural model generates a
translated sentence that describes an image, given the image itself and one
source descriptions in English. This is considered as the multimodal image
caption translation task. The images are processed with Convolutional Neural
Network (CNN) to... | computer science |
16,882 | Zero-Shot Transfer Learning for Event Extraction | cs.CL | Most previous event extraction studies have relied heavily on features
derived from annotated event mentions, thus cannot be applied to new event
types without annotation effort. In this work, we take a fresh look at event
extraction and model it as a grounding problem. We design a transferable neural
architecture, map... | computer science |
16,883 | Improving Slot Filling Performance with Attentive Neural Networks on
Dependency Structures | cs.CL | Slot Filling (SF) aims to extract the values of certain types of attributes
(or slots, such as person:cities\_of\_residence) for a given entity from a
large collection of source documents. In this paper we propose an effective DNN
architecture for SF with the following new strategies: (1). Take a regularized
dependency... | computer science |
16,884 | Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence
Models | cs.CL | Variations in writing styles are commonly used to adapt the content to a
specific context, audience, or purpose. However, applying stylistic variations
is still by and large a manual process, and there have been little efforts
towards automating it. In this paper we explore automated methods to transform
text from mode... | computer science |
16,885 | CharManteau: Character Embedding Models For Portmanteau Creation | cs.CL | Portmanteaus are a word formation phenomenon where two words are combined to
form a new word. We propose character-level neural sequence-to-sequence (S2S)
methods for the task of portmanteau generation that are end-to-end-trainable,
language independent, and do not explicitly use additional phonetic
information. We pro... | computer science |
16,886 | Multiple Range-Restricted Bidirectional Gated Recurrent Units with
Attention for Relation Classification | cs.CL | Most of neural approaches to relation classification have focused on finding
short patterns that represent the semantic relation using Convolutional Neural
Networks (CNNs) and those approaches have generally achieved better
performances than using Recurrent Neural Networks (RNNs). In a similar
intuition to the CNN mode... | computer science |
16,887 | The Influence of Feature Representation of Text on the Performance of
Document Classification | cs.CL | In this paper we perform a comparative analysis of three models for feature
representation of text documents in the context of document classification. In
particular, we consider the most often used family of models bag-of-words,
recently proposed continuous space models word2vec and doc2vec, and the model
based on the... | computer science |
16,888 | Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological
Reinflection | cs.CL | This paper presents the submissions by the University of Zurich to the
SIGMORPHON 2017 shared task on morphological reinflection. The task is to
predict the inflected form given a lemma and a set of morpho-syntactic
features. We focus on neural network approaches that can tackle the task in a
limited-resource setting. ... | computer science |
16,889 | An Attention Mechanism for Answer Selection Using a Combined Global and
Local View | cs.CL | We propose a new attention mechanism for neural based question answering,
which depends on varying granularities of the input. Previous work focused on
augmenting recurrent neural networks with simple attention mechanisms which are
a function of the similarity between a question embedding and an answer
embeddings acros... | computer science |
16,890 | Context Aware Document Embedding | cs.CL | Recently, doc2vec has achieved excellent results in different tasks. In this
paper, we present a context aware variant of doc2vec. We introduce a novel
weight estimating mechanism that generates weights for each word occurrence
according to its contribution in the context, using deep neural networks. Our
context aware ... | computer science |
16,891 | Cross-Lingual Sentiment Analysis Without (Good) Translation | cs.CL | Current approaches to cross-lingual sentiment analysis try to leverage the
wealth of labeled English data using bilingual lexicons, bilingual vector space
embeddings, or machine translation systems. Here we show that it is possible to
use a single linear transformation, with as few as 2000 word pairs, to capture
fine-g... | computer science |
16,892 | An Embedded Deep Learning based Word Prediction | cs.CL | Recent developments in deep learning with application to language modeling
have led to success in tasks of text processing, summarizing and machine
translation. However, deploying huge language models for mobile device such as
on-device keyboards poses computation as a bottle-neck due to their puny
computation capaciti... | computer science |
16,893 | A Simple Approach to Learn Polysemous Word Embeddings | cs.CL | Many NLP applications require disambiguating polysemous words. Existing
methods that learn polysemous word vector representations involve first
detecting various senses and optimizing the sense-specific embeddings
separately, which are invariably more involved than single sense learning
methods such as word2vec. Evalua... | computer science |
16,894 | Single-Queue Decoding for Neural Machine Translation | cs.CL | Neural machine translation models rely on the beam search algorithm for
decoding. In practice, we found that the quality of hypotheses in the search
space is negatively affected owing to the fixed beam size. To mitigate this
problem, we store all hypotheses in a single priority queue and use a universal
score function ... | computer science |
16,895 | A Nested Attention Neural Hybrid Model for Grammatical Error Correction | cs.CL | Grammatical error correction (GEC) systems strive to correct both global
errors in word order and usage, and local errors in spelling and inflection.
Further developing upon recent work on neural machine translation, we propose a
new hybrid neural model with nested attention layers for GEC. Experiments show
that the ne... | computer science |
16,896 | External Evaluation of Event Extraction Classifiers for Automatic
Pathway Curation: An extended study of the mTOR pathway | cs.CL | This paper evaluates the impact of various event extraction systems on
automatic pathway curation using the popular mTOR pathway. We quantify the
impact of training data sets as well as different machine learning classifiers
and show that some improve the quality of automatically extracted pathways. | computer science |
16,897 | Computational Models of Tutor Feedback in Language Acquisition | cs.CL | This paper investigates the role of tutor feedback in language learning using
computational models. We compare two dominant paradigms in language learning:
interactive learning and cross-situational learning - which differ primarily in
the role of social feedback such as gaze or pointing. We analyze the
relationship be... | computer science |
16,898 | Text Summarization Techniques: A Brief Survey | cs.CL | In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review t... | computer science |
16,899 | Controlling Linguistic Style Aspects in Neural Language Generation | cs.CL | Most work on neural natural language generation (NNLG) focus on controlling
the content of the generated text. We experiment with controlling several
stylistic aspects of the generated text, in addition to its content. The method
is based on conditioned RNN language model, where the desired content as well
as the styli... | computer science |
16,900 | Learning to Compose Task-Specific Tree Structures | cs.CL | For years, recursive neural networks (RvNNs) have been shown to be suitable
for representing text into fixed-length vectors and achieved good performance
on several natural language processing tasks. However, the main drawback of
RvNNs is that they require structured input, which makes data preparation and
model implem... | computer science |
16,901 | A Generalized Recurrent Neural Architecture for Text Classification with
Multi-Task Learning | cs.CL | Multi-task learning leverages potential correlations among related tasks to
extract common features and yield performance gains. However, most previous
works only consider simple or weak interactions, thereby failing to model
complex correlations among three or more tasks. In this paper, we propose a
multi-task learnin... | computer science |
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