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15,802 | A Proposal for Linguistic Similarity Datasets Based on Commonality Lists | cs.CL | Similarity is a core notion that is used in psychology and two branches of
linguistics: theoretical and computational. The similarity datasets that come
from the two fields differ in design: psychological datasets are focused around
a certain topic such as fruit names, while linguistic datasets contain words
from vario... | computer science |
15,803 | Syntactically Guided Neural Machine Translation | cs.CL | We investigate the use of hierarchical phrase-based SMT lattices in
end-to-end neural machine translation (NMT). Weight pushing transforms the
Hiero scores for complete translation hypotheses, with the full translation
grammar score and full n-gram language model score, into posteriors compatible
with NMT predictive pr... | computer science |
15,804 | Log-linear Combinations of Monolingual and Bilingual Neural Machine
Translation Models for Automatic Post-Editing | cs.CL | This paper describes the submission of the AMU (Adam Mickiewicz University)
team to the Automatic Post-Editing (APE) task of WMT 2016. We explore the
application of neural translation models to the APE problem and achieve good
results by treating different models as components in a log-linear model,
allowing for multip... | computer science |
15,805 | The AMU-UEDIN Submission to the WMT16 News Translation Task:
Attention-based NMT Models as Feature Functions in Phrase-based SMT | cs.CL | This paper describes the AMU-UEDIN submissions to the WMT 2016 shared task on
news translation. We explore methods of decode-time integration of
attention-based neural translation models with phrase-based statistical machine
translation. Efficient batch-algorithms for GPU-querying are proposed and
implemented. For Engl... | computer science |
15,806 | Recurrent Neural Network for Text Classification with Multi-Task
Learning | cs.CL | Neural network based methods have obtained great progress on a variety of
natural language processing tasks. However, in most previous works, the models
are learned based on single-task supervised objectives, which often suffer from
insufficient training data. In this paper, we use the multi-task learning
framework to ... | computer science |
15,807 | Incorporating Loose-Structured Knowledge into Conversation Modeling via
Recall-Gate LSTM | cs.CL | Modeling human conversations is the essence for building satisfying chat-bots
with multi-turn dialog ability. Conversation modeling will notably benefit from
domain knowledge since the relationships between sentences can be clarified due
to semantic hints introduced by knowledge. In this paper, a deep neural network
is... | computer science |
15,808 | Siamese convolutional networks based on phonetic features for cognate
identification | cs.CL | In this paper, we explore the use of convolutional networks (ConvNets) for
the purpose of cognate identification. We compare our architecture with binary
classifiers based on string similarity measures on different language families.
Our experiments show that convolutional networks achieve competitive results
across co... | computer science |
15,809 | Leveraging Lexical Resources for Learning Entity Embeddings in
Multi-Relational Data | cs.CL | Recent work in learning vector-space embeddings for multi-relational data has
focused on combining relational information derived from knowledge bases with
distributional information derived from large text corpora. We propose a simple
approach that leverages the descriptions of entities or phrases available in
lexical... | computer science |
15,810 | Modelling Interaction of Sentence Pair with coupled-LSTMs | cs.CL | Recently, there is rising interest in modelling the interactions of two
sentences with deep neural networks. However, most of the existing methods
encode two sequences with separate encoders, in which a sentence is encoded
with little or no information from the other sentence. In this paper, we
propose a deep architect... | computer science |
15,811 | Automatic TM Cleaning through MT and POS Tagging: Autodesk's Submission
to the NLP4TM 2016 Shared Task | cs.CL | We describe a machine learning based method to identify incorrect entries in
translation memories. It extends previous work by Barbu (2015) through
incorporating recall-based machine translation and part-of-speech-tagging
features. Our system ranked first in the Binary Classification (II) task for
two out of three lang... | computer science |
15,812 | Phrase-based Machine Translation is State-of-the-Art for Automatic
Grammatical Error Correction | cs.CL | In this work, we study parameter tuning towards the M^2 metric, the standard
metric for automatic grammar error correction (GEC) tasks. After implementing
M^2 as a scorer in the Moses tuning framework, we investigate interactions of
dense and sparse features, different optimizers, and tuning strategies for the
CoNLL-20... | computer science |
15,813 | Automatic Construction of Discourse Corpora for Dialogue Translation | cs.CL | In this paper, a novel approach is proposed to automatically construct
parallel discourse corpus for dialogue machine translation. Firstly, the
parallel subtitle data and its corresponding monolingual movie script data are
crawled and collected from Internet. Then tags such as speaker and discourse
boundary from the sc... | computer science |
15,814 | Combining Recurrent and Convolutional Neural Networks for Relation
Classification | cs.CL | This paper investigates two different neural architectures for the task of
relation classification: convolutional neural networks and recurrent neural
networks. For both models, we demonstrate the effect of different architectural
choices. We present a new context representation for convolutional neural
networks for re... | computer science |
15,815 | Multi-Level Analysis and Annotation of Arabic Corpora for Text-to-Sign
Language MT | cs.CL | In this paper, we present an ongoing effort in lexical semantic analysis and
annotation of Modern Standard Arabic (MSA) text, a semi automatic annotation
tool concerned with the morphologic, syntactic, and semantic levels of
description. | computer science |
15,816 | Experiments in Linear Template Combination using Genetic Algorithms | cs.CL | Natural Language Generation systems typically have two parts - strategic
('what to say') and tactical ('how to say'). We present our experiments in
building an unsupervised corpus-driven template based tactical NLG system. We
consider templates as a sequence of words containing gaps. Our idea is based on
the observatio... | computer science |
15,817 | Neural Semantic Role Labeling with Dependency Path Embeddings | cs.CL | This paper introduces a novel model for semantic role labeling that makes use
of neural sequence modeling techniques. Our approach is motivated by the
observation that complex syntactic structures and related phenomena, such as
nested subordinations and nominal predicates, are not handled well by existing
models. Our m... | computer science |
15,818 | Learning End-to-End Goal-Oriented Dialog | cs.CL | Traditional dialog systems used in goal-oriented applications require a lot
of domain-specific handcrafting, which hinders scaling up to new domains.
End-to-end dialog systems, in which all components are trained from the dialogs
themselves, escape this limitation. But the encouraging success recently
obtained in chit-... | computer science |
15,819 | Integrating Distributional Lexical Contrast into Word Embeddings for
Antonym-Synonym Distinction | cs.CL | We propose a novel vector representation that integrates lexical contrast
into distributional vectors and strengthens the most salient features for
determining degrees of word similarity. The improved vectors significantly
outperform standard models and distinguish antonyms from synonyms with an
average precision of 0.... | computer science |
15,820 | Unsupervised Word and Dependency Path Embeddings for Aspect Term
Extraction | cs.CL | In this paper, we develop a novel approach to aspect term extraction based on
unsupervised learning of distributed representations of words and dependency
paths. The basic idea is to connect two words (w1 and w2) with the dependency
path (r) between them in the embedding space. Specifically, our method
optimizes the ob... | computer science |
15,821 | Variational Neural Machine Translation | cs.CL | Models of neural machine translation are often from a discriminative family
of encoderdecoders that learn a conditional distribution of a target sentence
given a source sentence. In this paper, we propose a variational model to learn
this conditional distribution for neural machine translation: a variational
encoderdec... | computer science |
15,822 | BattRAE: Bidimensional Attention-Based Recursive Autoencoders for
Learning Bilingual Phrase Embeddings | cs.CL | In this paper, we propose a bidimensional attention based recursive
autoencoder (BattRAE) to integrate clues and sourcetarget interactions at
multiple levels of granularity into bilingual phrase representations. We employ
recursive autoencoders to generate tree structures of phrases with embeddings
at different levels ... | computer science |
15,823 | Boosting Question Answering by Deep Entity Recognition | cs.CL | In this paper an open-domain factoid question answering system for Polish,
RAFAEL, is presented. The system goes beyond finding an answering sentence; it
also extracts a single string, corresponding to the required entity. Herein the
focus is placed on different approaches to entity recognition, essential for
retrievin... | computer science |
15,824 | Building an Evaluation Scale using Item Response Theory | cs.CL | Evaluation of NLP methods requires testing against a previously vetted
gold-standard test set and reporting standard metrics
(accuracy/precision/recall/F1). The current assumption is that all items in a
given test set are equal with regards to difficulty and discriminating power.
We propose Item Response Theory (IRT) f... | computer science |
15,825 | Aspect Level Sentiment Classification with Deep Memory Network | cs.CL | We introduce a deep memory network for aspect level sentiment classification.
Unlike feature-based SVM and sequential neural models such as LSTM, this
approach explicitly captures the importance of each context word when inferring
the sentiment polarity of an aspect. Such importance degree and text
representation are c... | computer science |
15,826 | Learning Natural Language Inference using Bidirectional LSTM model and
Inner-Attention | cs.CL | In this paper, we proposed a sentence encoding-based model for recognizing
text entailment. In our approach, the encoding of sentence is a two-stage
process. Firstly, average pooling was used over word-level bidirectional LSTM
(biLSTM) to generate a first-stage sentence representation. Secondly, attention
mechanism was... | computer science |
15,827 | Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change | cs.CL | Understanding how words change their meanings over time is key to models of
language and cultural evolution, but historical data on meaning is scarce,
making theories hard to develop and test. Word embeddings show promise as a
diachronic tool, but have not been carefully evaluated. We develop a robust
methodology for q... | computer science |
15,828 | Implementing a Reverse Dictionary, based on word definitions, using a
Node-Graph Architecture | cs.CL | In this paper, we outline an approach to build graph-based reverse
dictionaries using word definitions. A reverse dictionary takes a phrase as an
input and outputs a list of words semantically similar to that phrase. It is a
solution to the Tip-of-the-Tongue problem. We use a distance-based similarity
measure, computed... | computer science |
15,829 | Neural Network Translation Models for Grammatical Error Correction | cs.CL | Phrase-based statistical machine translation (SMT) systems have previously
been used for the task of grammatical error correction (GEC) to achieve
state-of-the-art accuracy. The superiority of SMT systems comes from their
ability to learn text transformations from erroneous to corrected text, without
explicitly modelin... | computer science |
15,830 | Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical
Error Correction | cs.CL | Grammatical error correction (GEC) is the task of detecting and correcting
grammatical errors in texts written by second language learners. The
statistical machine translation (SMT) approach to GEC, in which sentences
written by second language learners are translated to grammatically correct
sentences, has achieved st... | computer science |
15,831 | Improved Parsing for Argument-Clusters Coordination | cs.CL | Syntactic parsers perform poorly in prediction of Argument-Cluster
Coordination (ACC). We change the PTB representation of ACC to be more suitable
for learning by a statistical PCFG parser, affecting 125 trees in the training
set. Training on the modified trees yields a slight improvement in EVALB scores
on sections 22... | computer science |
15,832 | Generalizing and Hybridizing Count-based and Neural Language Models | cs.CL | Language models (LMs) are statistical models that calculate probabilities
over sequences of words or other discrete symbols. Currently two major
paradigms for language modeling exist: count-based n-gram models, which have
advantages of scalability and test-time speed, and neural LMs, which often
achieve superior modeli... | computer science |
15,833 | Single-Model Encoder-Decoder with Explicit Morphological Representation
for Reinflection | cs.CL | Morphological reinflection is the task of generating a target form given a
source form, a source tag and a target tag. We propose a new way of modeling
this task with neural encoder-decoder models. Our approach reduces the amount
of required training data for this architecture and achieves state-of-the-art
results, mak... | computer science |
15,834 | Matrix Factorization using Window Sampling and Negative Sampling for
Improved Word Representations | cs.CL | In this paper, we propose LexVec, a new method for generating distributed
word representations that uses low-rank, weighted factorization of the Positive
Point-wise Mutual Information matrix via stochastic gradient descent, employing
a weighting scheme that assigns heavier penalties for errors on frequent
co-occurrence... | computer science |
15,835 | Using Neural Generative Models to Release Synthetic Twitter Corpora with
Reduced Stylometric Identifiability of Users | cs.CL | We present a method for generating synthetic versions of Twitter data using
neural generative models. The goal is to protect individuals in the source data
from stylometric re-identification attacks while still releasing data that
carries research value. To generate tweet corpora that maintain user-level word
distribut... | computer science |
15,836 | Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task
Learning | cs.CL | Various treebanks have been released for dependency parsing. Despite that
treebanks may belong to different languages or have different annotation
schemes, they contain syntactic knowledge that is potential to benefit each
other. This paper presents an universal framework for exploiting these
multi-typed treebanks to i... | computer science |
15,837 | Enhancing the LexVec Distributed Word Representation Model Using
Positional Contexts and External Memory | cs.CL | In this paper we take a state-of-the-art model for distributed word
representation that explicitly factorizes the positive pointwise mutual
information (PPMI) matrix using window sampling and negative sampling and
address two of its shortcomings. We improve syntactic performance by using
positional contexts, and solve ... | computer science |
15,838 | Improving Coreference Resolution by Learning Entity-Level Distributed
Representations | cs.CL | A long-standing challenge in coreference resolution has been the
incorporation of entity-level information - features defined over clusters of
mentions instead of mention pairs. We present a neural network based
coreference system that produces high-dimensional vector representations for
pairs of coreference clusters. ... | computer science |
15,839 | Neural Architectures for Fine-grained Entity Type Classification | cs.CL | In this work, we investigate several neural network architectures for
fine-grained entity type classification. Particularly, we consider extensions
to a recently proposed attentive neural architecture and make three key
contributions. Previous work on attentive neural architectures do not consider
hand-crafted features... | computer science |
15,840 | Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint
Inference for Clinical Temporal Information Extraction | cs.CL | We submitted two systems to the SemEval-2016 Task 12: Clinical TempEval
challenge, participating in Phase 1, where we identified text spans of time and
event expressions in clinical notes and Phase 2, where we predicted a relation
between an event and its parent document creation time.
For temporal entity extraction,... | computer science |
15,841 | Deep Reinforcement Learning for Dialogue Generation | cs.CL | Recent neural models of dialogue generation offer great promise for
generating responses for conversational agents, but tend to be shortsighted,
predicting utterances one at a time while ignoring their influence on future
outcomes. Modeling the future direction of a dialogue is crucial to generating
coherent, interesti... | computer science |
15,842 | Neural Net Models for Open-Domain Discourse Coherence | cs.CL | Discourse coherence is strongly associated with text quality, making it
important to natural language generation and understanding. Yet existing models
of coherence focus on measuring individual aspects of coherence (lexical
overlap, rhetorical structure, entity centering) in narrow domains.
In this paper, we describ... | computer science |
15,843 | Generating and Exploiting Large-scale Pseudo Training Data for Zero
Pronoun Resolution | cs.CL | Most existing approaches for zero pronoun resolution are heavily relying on
annotated data, which is often released by shared task organizers. Therefore,
the lack of annotated data becomes a major obstacle in the progress of zero
pronoun resolution task. Also, it is expensive to spend manpower on labeling
the data for ... | computer science |
15,844 | Adversarial Deep Averaging Networks for Cross-Lingual Sentiment
Classification | cs.CL | In recent years deep neural networks have achieved great success in sentiment
classification for English, thanks in part to the availability of copious
annotated resources. Unfortunately, most other languages do not enjoy such an
abundance of annotated data for sentiment analysis. To tackle this problem, we
propose the... | computer science |
15,845 | Gated Word-Character Recurrent Language Model | cs.CL | We introduce a recurrent neural network language model (RNN-LM) with long
short-term memory (LSTM) units that utilizes both character-level and
word-level inputs. Our model has a gate that adaptively finds the optimal
mixture of the character-level and word-level inputs. The gate creates the
final vector representation... | computer science |
15,846 | Neural Machine Translation with External Phrase Memory | cs.CL | In this paper, we propose phraseNet, a neural machine translator with a
phrase memory which stores phrase pairs in symbolic form, mined from corpus or
specified by human experts. For any given source sentence, phraseNet scans the
phrase memory to determine the candidate phrase pairs and integrates tagging
information i... | computer science |
15,847 | A Decomposable Attention Model for Natural Language Inference | cs.CL | We propose a simple neural architecture for natural language inference. Our
approach uses attention to decompose the problem into subproblems that can be
solved separately, thus making it trivially parallelizable. On the Stanford
Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results
with almost ... | computer science |
15,848 | Neural Network Models for Implicit Discourse Relation Classification in
English and Chinese without Surface Features | cs.CL | Inferring implicit discourse relations in natural language text is the most
difficult subtask in discourse parsing. Surface features achieve good
performance, but they are not readily applicable to other languages without
semantic lexicons. Previous neural models require parses, surface features, or
a small label set t... | computer science |
15,849 | CFO: Conditional Focused Neural Question Answering with Large-scale
Knowledge Bases | cs.CL | How can we enable computers to automatically answer questions like "Who
created the character Harry Potter"? Carefully built knowledge bases provide
rich sources of facts. However, it remains a challenge to answer factoid
questions raised in natural language due to numerous expressions of one
question. In particular, w... | computer science |
15,850 | Memory-enhanced Decoder for Neural Machine Translation | cs.CL | We propose to enhance the RNN decoder in a neural machine translator (NMT)
with external memory, as a natural but powerful extension to the state in the
decoding RNN. This memory-enhanced RNN decoder is called \textsc{MemDec}. At
each time during decoding, \textsc{MemDec} will read from this memory and write
to this me... | computer science |
15,851 | Incorporating Discrete Translation Lexicons into Neural Machine
Translation | cs.CL | Neural machine translation (NMT) often makes mistakes in translating
low-frequency content words that are essential to understanding the meaning of
the sentence. We propose a method to alleviate this problem by augmenting NMT
systems with discrete translation lexicons that efficiently encode translations
of these low-f... | computer science |
15,852 | Can neural machine translation do simultaneous translation? | cs.CL | We investigate the potential of attention-based neural machine translation in
simultaneous translation. We introduce a novel decoding algorithm, called
simultaneous greedy decoding, that allows an existing neural machine
translation model to begin translating before a full source sentence is
received. This approach is ... | computer science |
15,853 | Supervised Syntax-based Alignment between English Sentences and Abstract
Meaning Representation Graphs | cs.CL | As alignment links are not given between English sentences and Abstract
Meaning Representation (AMR) graphs in the AMR annotation, automatic alignment
becomes indispensable for training an AMR parser. Previous studies formalize it
as a string-to-string problem and solve it in an unsupervised way, which
suffers from dat... | computer science |
15,854 | Natural Language Comprehension with the EpiReader | cs.CL | We present the EpiReader, a novel model for machine comprehension of text.
Machine comprehension of unstructured, real-world text is a major research goal
for natural language processing. Current tests of machine comprehension pose
questions whose answers can be inferred from some supporting text, and evaluate
a model'... | computer science |
15,855 | Optimizing Spectral Learning for Parsing | cs.CL | We describe a search algorithm for optimizing the number of latent states
when estimating latent-variable PCFGs with spectral methods. Our results show
that contrary to the common belief that the number of latent states for each
nonterminal in an L-PCFG can be decided in isolation with spectral methods,
parsing results... | computer science |
15,856 | Learning Semantically and Additively Compositional Distributional
Representations | cs.CL | This paper connects a vector-based composition model to a formal semantics,
the Dependency-based Compositional Semantics (DCS). We show theoretical
evidence that the vector compositions in our model conform to the logic of DCS.
Experimentally, we show that vector-based composition brings a strong ability
to calculate s... | computer science |
15,857 | DefExt: A Semi Supervised Definition Extraction Tool | cs.CL | We present DefExt, an easy to use semi supervised Definition Extraction Tool.
DefExt is designed to extract from a target corpus those textual fragments
where a term is explicitly mentioned together with its core features, i.e. its
definition. It works on the back of a Conditional Random Fields based
sequential labelin... | computer science |
15,858 | Coordination Annotation Extension in the Penn Tree Bank | cs.CL | Coordination is an important and common syntactic construction which is not
handled well by state of the art parsers. Coordinations in the Penn Treebank
are missing internal structure in many cases, do not include explicit marking
of the conjuncts and contain various errors and inconsistencies. In this work,
we initiat... | computer science |
15,859 | A Joint Model for Word Embedding and Word Morphology | cs.CL | This paper presents a joint model for performing unsupervised morphological
analysis on words, and learning a character-level composition function from
morphemes to word embeddings. Our model splits individual words into segments,
and weights each segment according to its ability to predict context words. Our
morpholog... | computer science |
15,860 | Addressing Limited Data for Textual Entailment Across Domains | cs.CL | We seek to address the lack of labeled data (and high cost of annotation) for
textual entailment in some domains. To that end, we first create (for
experimental purposes) an entailment dataset for the clinical domain, and a
highly competitive supervised entailment system, ENT, that is effective (out of
the box) on two ... | computer science |
15,861 | First Result on Arabic Neural Machine Translation | cs.CL | Neural machine translation has become a major alternative to widely used
phrase-based statistical machine translation. We notice however that much of
research on neural machine translation has focused on European languages
despite its language agnostic nature. In this paper, we apply neural machine
translation to the t... | computer science |
15,862 | Neural Network-Based Abstract Generation for Opinions and Arguments | cs.CL | We study the problem of generating abstractive summaries for opinionated
text. We propose an attention-based neural network model that is able to absorb
information from multiple text units to construct informative, concise, and
fluent summaries. An importance-based sampling method is designed to allow the
encoder to i... | computer science |
15,863 | Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora | cs.CL | A word's sentiment depends on the domain in which it is used. Computational
social science research thus requires sentiment lexicons that are specific to
the domains being studied. We combine domain-specific word embeddings with a
label propagation framework to induce accurate domain-specific sentiment
lexicons using s... | computer science |
15,864 | Cultural Shift or Linguistic Drift? Comparing Two Computational Measures
of Semantic Change | cs.CL | Words shift in meaning for many reasons, including cultural factors like new
technologies and regular linguistic processes like subjectification.
Understanding the evolution of language and culture requires disentangling
these underlying causes. Here we show how two different distributional measures
can be used to dete... | computer science |
15,865 | Edinburgh Neural Machine Translation Systems for WMT 16 | cs.CL | We participated in the WMT 2016 shared news translation task by building
neural translation systems for four language pairs, each trained in both
directions: English<->Czech, English<->German, English<->Romanian and
English<->Russian. Our systems are based on an attentional encoder-decoder,
using BPE subword segmentati... | computer science |
15,866 | Linguistic Input Features Improve Neural Machine Translation | cs.CL | Neural machine translation has recently achieved impressive results, while
using little in the way of external linguistic information. In this paper we
show that the strong learning capability of neural MT models does not make
linguistic features redundant; they can be easily incorporated to provide
further improvement... | computer science |
15,867 | Key-Value Memory Networks for Directly Reading Documents | cs.CL | Directly reading documents and being able to answer questions from them is an
unsolved challenge. To avoid its inherent difficulty, question answering (QA)
has been directed towards using Knowledge Bases (KBs) instead, which has proven
effective. Unfortunately KBs often suffer from being too restrictive, as the
schema ... | computer science |
15,868 | PerSum: Novel Systems for Document Summarization in Persian | cs.CL | In this paper we explore the problem of document summarization in Persian
language from two distinct angles. In our first approach, we modify a popular
and widely cited Persian document summarization framework to see how it works
on a realistic corpus of news articles. Human evaluation on generated summaries
shows that... | computer science |
15,869 | PSDVec: a Toolbox for Incremental and Scalable Word Embedding | cs.CL | PSDVec is a Python/Perl toolbox that learns word embeddings, i.e. the mapping
of words in a natural language to continuous vectors which encode the
semantic/syntactic regularities between the words. PSDVec implements a word
embedding learning method based on a weighted low-rank positive semidefinite
approximation. To s... | computer science |
15,870 | Simple Question Answering by Attentive Convolutional Neural Network | cs.CL | This work focuses on answering single-relation factoid questions over
Freebase. Each question can acquire the answer from a single fact of form
(subject, predicate, object) in Freebase. This task, simple question answering
(SimpleQA), can be addressed via a two-step pipeline: entity linking and fact
selection. In fact ... | computer science |
15,871 | Bootstrapping Distantly Supervised IE using Joint Learning and Small
Well-structured Corpora | cs.CL | We propose a framework to improve performance of distantly-supervised
relation extraction, by jointly learning to solve two related tasks:
concept-instance extraction and relation extraction. We combine this with a
novel use of document structure: in some small, well-structured corpora,
sections can be identified that ... | computer science |
15,872 | Data Recombination for Neural Semantic Parsing | cs.CL | Modeling crisp logical regularities is crucial in semantic parsing, making it
difficult for neural models with no task-specific prior knowledge to achieve
good results. In this paper, we introduce data recombination, a novel framework
for injecting such prior knowledge into a model. From the training data, we
induce a ... | computer science |
15,873 | Natural Language Generation in Dialogue using Lexicalized and
Delexicalized Data | cs.CL | Natural language generation plays a critical role in spoken dialogue systems.
We present a new approach to natural language generation for task-oriented
dialogue using recurrent neural networks in an encoder-decoder framework. In
contrast to previous work, our model uses both lexicalized and delexicalized
components i.... | computer science |
15,874 | External Lexical Information for Multilingual Part-of-Speech Tagging | cs.CL | Morphosyntactic lexicons and word vector representations have both proven
useful for improving the accuracy of statistical part-of-speech taggers. Here
we compare the performances of four systems on datasets covering 16 languages,
two of these systems being feature-based (MEMMs and CRFs) and two of them being
neural-ba... | computer science |
15,875 | Learning to Generate Compositional Color Descriptions | cs.CL | The production of color language is essential for grounded language
generation. Color descriptions have many challenging properties: they can be
vague, compositionally complex, and denotationally rich. We present an
effective approach to generating color descriptions using recurrent neural
networks and a Fourier-transf... | computer science |
15,876 | Zero-Resource Translation with Multi-Lingual Neural Machine Translation | cs.CL | In this paper, we propose a novel finetuning algorithm for the recently
introduced multi-way, mulitlingual neural machine translate that enables
zero-resource machine translation. When used together with novel many-to-one
translation strategies, we empirically show that this finetuning algorithm
allows the multi-way, m... | computer science |
15,877 | Active Discriminative Text Representation Learning | cs.CL | We propose a new active learning (AL) method for text classification with
convolutional neural networks (CNNs). In AL, one selects the instances to be
manually labeled with the aim of maximizing model performance with minimal
effort. Neural models capitalize on word embeddings as representations
(features), tuning thes... | computer science |
15,878 | Cross-Lingual Morphological Tagging for Low-Resource Languages | cs.CL | Morphologically rich languages often lack the annotated linguistic resources
required to develop accurate natural language processing tools. We propose
models suitable for training morphological taggers with rich tagsets for
low-resource languages without using direct supervision. Our approach extends
existing approach... | computer science |
15,879 | Neural Word Segmentation Learning for Chinese | cs.CL | Most previous approaches to Chinese word segmentation formalize this problem
as a character-based sequence labeling task where only contextual information
within fixed sized local windows and simple interactions between adjacent tags
can be captured. In this paper, we propose a novel neural framework which
thoroughly e... | computer science |
15,880 | Shallow Discourse Parsing Using Distributed Argument Representations and
Bayesian Optimization | cs.CL | This paper describes the Georgia Tech team's approach to the CoNLL-2016
supplementary evaluation on discourse relation sense classification. We use
long short-term memories (LSTM) to induce distributed representations of each
argument, and then combine these representations with surface features in a
neural network. Th... | computer science |
15,881 | Semi-Supervised Learning for Neural Machine Translation | cs.CL | While end-to-end neural machine translation (NMT) has made remarkable
progress recently, NMT systems only rely on parallel corpora for parameter
estimation. Since parallel corpora are usually limited in quantity, quality,
and coverage, especially for low-resource languages, it is appealing to exploit
monolingual corpor... | computer science |
15,882 | Agreement-based Learning of Parallel Lexicons and Phrases from
Non-Parallel Corpora | cs.CL | We introduce an agreement-based approach to learning parallel lexicons and
phrases from non-parallel corpora. The basic idea is to encourage two
asymmetric latent-variable translation models (i.e., source-to-target and
target-to-source) to agree on identifying latent phrase and word alignments.
The agreement is defined... | computer science |
15,883 | Siamese CBOW: Optimizing Word Embeddings for Sentence Representations | cs.CL | We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural
network for efficient estimation of high-quality sentence embeddings. Averaging
the embeddings of words in a sentence has proven to be a surprisingly
successful and efficient way of obtaining sentence embeddings. However, word
embeddings trai... | computer science |
15,884 | A Correlational Encoder Decoder Architecture for Pivot Based Sequence
Generation | cs.CL | Interlingua based Machine Translation (MT) aims to encode multiple languages
into a common linguistic representation and then decode sentences in multiple
target languages from this representation. In this work we explore this idea in
the context of neural encoder decoder architectures, albeit on a smaller scale
and wi... | computer science |
15,885 | Learning Word Sense Embeddings from Word Sense Definitions | cs.CL | Word embeddings play a significant role in many modern NLP systems. Since
learning one representation per word is problematic for polysemous words and
homonymous words, researchers propose to use one embedding per word sense.
Their approaches mainly train word sense embeddings on a corpus. In this paper,
we propose to ... | computer science |
15,886 | Smart Reply: Automated Response Suggestion for Email | cs.CL | In this paper we propose and investigate a novel end-to-end method for
automatically generating short email responses, called Smart Reply. It
generates semantically diverse suggestions that can be used as complete email
responses with just one tap on mobile. The system is currently used in Inbox by
Gmail and is respons... | computer science |
15,887 | The Edit Distance Transducer in Action: The University of Cambridge
English-German System at WMT16 | cs.CL | This paper presents the University of Cambridge submission to WMT16.
Motivated by the complementary nature of syntactical machine translation and
neural machine translation (NMT), we exploit the synergies of Hiero and NMT in
different combination schemes. Starting out with a simple neural lattice
rescoring approach, we... | computer science |
15,888 | No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for
Answering "Simple" Questions) | cs.CL | First-order factoid question answering assumes that the question can be
answered by a single fact in a knowledge base (KB). While this does not seem
like a challenging task, many recent attempts that apply either complex
linguistic reasoning or deep neural networks achieve 65%-76% accuracy on
benchmark sets. Our approa... | computer science |
15,889 | SQuAD: 100,000+ Questions for Machine Comprehension of Text | cs.CL | We present the Stanford Question Answering Dataset (SQuAD), a new reading
comprehension dataset consisting of 100,000+ questions posed by crowdworkers on
a set of Wikipedia articles, where the answer to each question is a segment of
text from the corresponding reading passage. We analyze the dataset to
understand the t... | computer science |
15,890 | Simpler Context-Dependent Logical Forms via Model Projections | cs.CL | We consider the task of learning a context-dependent mapping from utterances
to denotations. With only denotations at training time, we must search over a
combinatorially large space of logical forms, which is even larger with
context-dependent utterances. To cope with this challenge, we perform
successive projections ... | computer science |
15,891 | Sense Embedding Learning for Word Sense Induction | cs.CL | Conventional word sense induction (WSI) methods usually represent each
instance with discrete linguistic features or cooccurrence features, and train
a model for each polysemous word individually. In this work, we propose to
learn sense embeddings for the WSI task. In the training stage, our method
induces several sens... | computer science |
15,892 | Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax
Trees and Strings | cs.CL | We present a natural language generator based on the sequence-to-sequence
approach that can be trained to produce natural language strings as well as
deep syntax dependency trees from input dialogue acts, and we use it to
directly compare two-step generation with separate sentence planning and
surface realization stage... | computer science |
15,893 | Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis | cs.CL | We present a novel unsupervised approach for multilingual sentiment analysis
driven by compositional syntax-based rules. On the one hand, we exploit some of
the main advantages of unsupervised algorithms: (1) the interpretability of
their output, in contrast with most supervised models, which behave as a black
box and ... | computer science |
15,894 | Two Discourse Driven Language Models for Semantics | cs.CL | Natural language understanding often requires deep semantic knowledge.
Expanding on previous proposals, we suggest that some important aspects of
semantic knowledge can be modeled as a language model if done at an appropriate
level of abstraction. We develop two distinct models that capture semantic
frame chains and di... | computer science |
15,895 | Socially-Informed Timeline Generation for Complex Events | cs.CL | Existing timeline generation systems for complex events consider only
information from traditional media, ignoring the rich social context provided
by user-generated content that reveals representative public interests or
insightful opinions. We instead aim to generate socially-informed timelines
that contain both news... | computer science |
15,896 | Query-Focused Opinion Summarization for User-Generated Content | cs.CL | We present a submodular function-based framework for query-focused opinion
summarization. Within our framework, relevance ordering produced by a
statistical ranker, and information coverage with respect to topic distribution
and diverse viewpoints are both encoded as submodular functions. Dispersion
functions are utili... | computer science |
15,897 | A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute
Detection | cs.CL | We investigate the novel task of online dispute detection and propose a
sentiment analysis solution to the problem: we aim to identify the sequence of
sentence-level sentiments expressed during a discussion and to use them as
features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the
discussion as a w... | computer science |
15,898 | Improving Agreement and Disagreement Identification in Online
Discussions with A Socially-Tuned Sentiment Lexicon | cs.CL | We study the problem of agreement and disagreement detection in online
discussions. An isotonic Conditional Random Fields (isotonic CRF) based
sequential model is proposed to make predictions on sentence- or segment-level.
We automatically construct a socially-tuned lexicon that is bootstrapped from
existing general-pu... | computer science |
15,899 | Egyptian Arabic to English Statistical Machine Translation System for
NIST OpenMT'2015 | cs.CL | The paper describes the Egyptian Arabic-to-English statistical machine
translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to
the NIST OpenMT'2015 competition. The competition focused on informal dialectal
Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on
processing and stan... | computer science |
15,900 | Generalizing to Unseen Entities and Entity Pairs with Row-less Universal
Schema | cs.CL | Universal schema predicts the types of entities and relations in a knowledge
base (KB) by jointly embedding the union of all available schema types---not
only types from multiple structured databases (such as Freebase or Wikipedia
infoboxes), but also types expressed as textual patterns from raw text. This
prediction i... | computer science |
15,901 | Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test | cs.CL | Recent progress in neural learning demonstrated that machines can do well in
regularized tasks, e.g., the game of Go. However, artistic activities such as
poem generation are still widely regarded as human's special capability. In
this paper, we demonstrate that a simple neural model can imitate human in some
tasks of ... | computer science |
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