Titles
stringlengths 6
220
| Abstracts
stringlengths 37
3.26k
| Years
int64 1.99k
2.02k
| Categories
stringclasses 1
value |
|---|---|---|---|
Linguistic Matrix Theory | Recent research in computational linguistics has developed algorithms which
associate matrices with adjectives and verbs, based on the distribution of
words in a corpus of text. These matrices are linear operators on a vector
space of context words. They are used to construct the meaning of composite
expressions from that of the elementary constituents, forming part of a
compositional distributional approach to semantics. We propose a Matrix Theory
approach to this data, based on permutation symmetry along with Gaussian
weights and their perturbations. A simple Gaussian model is tested against word
matrices created from a large corpus of text. We characterize the cubic and
quartic departures from the model, which we propose, alongside the Gaussian
parameters, as signatures for comparison of linguistic corpora. We propose that
perturbed Gaussian models with permutation symmetry provide a promising
framework for characterizing the nature of universality in the statistical
properties of word matrices. The matrix theory framework developed here
exploits the view of statistics as zero dimensional perturbative quantum field
theory. It perceives language as a physical system realizing a universality
class of matrix statistics characterized by permutation symmetry.
| 2,017 | Computation and Language |
BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character
Dataset | Bangla handwriting recognition is becoming a very important issue nowadays.
It is potentially a very important task specially for Bangla speaking
population of Bangladesh and West Bengal. By keeping that in our mind we are
introducing a comprehensive Bangla handwritten character dataset named
BanglaLekha-Isolated. This dataset contains Bangla handwritten numerals, basic
characters and compound characters. This dataset was collected from multiple
geographical location within Bangladesh and includes sample collected from a
variety of aged groups. This dataset can also be used for other classification
problems i.e: gender, age, district. This is the largest dataset on Bangla
handwritten characters yet.
| 2,017 | Computation and Language |
Neutral evolution and turnover over centuries of English word popularity | Here we test Neutral models against the evolution of English word frequency
and vocabulary at the population scale, as recorded in annual word frequencies
from three centuries of English language books. Against these data, we test
both static and dynamic predictions of two neutral models, including the
relation between corpus size and vocabulary size, frequency distributions, and
turnover within those frequency distributions. Although a commonly used Neutral
model fails to replicate all these emergent properties at once, we find that
modified two-stage Neutral model does replicate the static and dynamic
properties of the corpus data. This two-stage model is meant to represent a
relatively small corpus (population) of English books, analogous to a `canon',
sampled by an exponentially increasing corpus of books in the wider population
of authors. More broadly, this mode -- a smaller neutral model within a larger
neutral model -- could represent more broadly those situations where mass
attention is focused on a small subset of the cultural variants.
| 2,017 | Computation and Language |
Factorization tricks for LSTM networks | We present two simple ways of reducing the number of parameters and
accelerating the training of large Long Short-Term Memory (LSTM) networks: the
first one is "matrix factorization by design" of LSTM matrix into the product
of two smaller matrices, and the second one is partitioning of LSTM matrix, its
inputs and states into the independent groups. Both approaches allow us to
train large LSTM networks significantly faster to the near state-of the art
perplexity while using significantly less RNN parameters.
| 2,018 | Computation and Language |
N-gram Language Modeling using Recurrent Neural Network Estimation | We investigate the effective memory depth of RNN models by using them for
$n$-gram language model (LM) smoothing.
Experiments on a small corpus (UPenn Treebank, one million words of training
data and 10k vocabulary) have found the LSTM cell with dropout to be the best
model for encoding the $n$-gram state when compared with feed-forward and
vanilla RNN models. When preserving the sentence independence assumption the
LSTM $n$-gram matches the LSTM LM performance for $n=9$ and slightly
outperforms it for $n=13$. When allowing dependencies across sentence
boundaries, the LSTM $13$-gram almost matches the perplexity of the unlimited
history LSTM LM.
LSTM $n$-gram smoothing also has the desirable property of improving with
increasing $n$-gram order, unlike the Katz or Kneser-Ney back-off estimators.
Using multinomial distributions as targets in training instead of the usual
one-hot target is only slightly beneficial for low $n$-gram orders.
Experiments on the One Billion Words benchmark show that the results hold at
larger scale: while LSTM smoothing for short $n$-gram contexts does not provide
significant advantages over classic N-gram models, it becomes effective with
long contexts ($n > 5$); depending on the task and amount of data it can match
fully recurrent LSTM models at about $n=13$. This may have implications when
modeling short-format text, e.g. voice search/query LMs.
Building LSTM $n$-gram LMs may be appealing for some practical situations:
the state in a $n$-gram LM can be succinctly represented with $(n-1)*4$ bytes
storing the identity of the words in the context and batches of $n$-gram
contexts can be processed in parallel. On the downside, the $n$-gram context
encoding computed by the LSTM is discarded, making the model more expensive
than a regular recurrent LSTM LM.
| 2,017 | Computation and Language |
Joining Hands: Exploiting Monolingual Treebanks for Parsing of
Code-mixing Data | In this paper, we propose efficient and less resource-intensive strategies
for parsing of code-mixed data. These strategies are not constrained by
in-domain annotations, rather they leverage pre-existing monolingual annotated
resources for training. We show that these methods can produce significantly
better results as compared to an informed baseline. Besides, we also present a
data set of 450 Hindi and English code-mixed tweets of Hindi multilingual
speakers for evaluation. The data set is manually annotated with Universal
Dependencies.
| 2,017 | Computation and Language |
Sentence Simplification with Deep Reinforcement Learning | Sentence simplification aims to make sentences easier to read and understand.
Most recent approaches draw on insights from machine translation to learn
simplification rewrites from monolingual corpora of complex and simple
sentences. We address the simplification problem with an encoder-decoder model
coupled with a deep reinforcement learning framework. Our model, which we call
{\sc Dress} (as shorthand for {\bf D}eep {\bf RE}inforcement {\bf S}entence
{\bf S}implification), explores the space of possible simplifications while
learning to optimize a reward function that encourages outputs which are
simple, fluent, and preserve the meaning of the input. Experiments on three
datasets demonstrate that our model outperforms competitive simplification
systems.
| 2,017 | Computation and Language |
Learning Discourse-level Diversity for Neural Dialog Models using
Conditional Variational Autoencoders | While recent neural encoder-decoder models have shown great promise in
modeling open-domain conversations, they often generate dull and generic
responses. Unlike past work that has focused on diversifying the output of the
decoder at word-level to alleviate this problem, we present a novel framework
based on conditional variational autoencoders that captures the discourse-level
diversity in the encoder. Our model uses latent variables to learn a
distribution over potential conversational intents and generates diverse
responses using only greedy decoders. We have further developed a novel variant
that is integrated with linguistic prior knowledge for better performance.
Finally, the training procedure is improved by introducing a bag-of-word loss.
Our proposed models have been validated to generate significantly more diverse
responses than baseline approaches and exhibit competence in discourse-level
decision-making.
| 2,017 | Computation and Language |
Opinion Mining on Non-English Short Text | As the type and the number of such venues increase, automated analysis of
sentiment on textual resources has become an essential data mining task. In
this paper, we investigate the problem of mining opinions on the collection of
informal short texts. Both positive and negative sentiment strength of texts
are detected. We focus on a non-English language that has few resources for
text mining. This approach would help enhance the sentiment analysis in
languages where a list of opinionated words does not exist. We propose a new
method projects the text into dense and low dimensional feature vectors
according to the sentiment strength of the words. We detect the mixture of
positive and negative sentiments on a multi-variant scale. Empirical evaluation
of the proposed framework on Turkish tweets shows that our approach gets good
results for opinion mining.
| 2,017 | Computation and Language |
Reading Wikipedia to Answer Open-Domain Questions | This paper proposes to tackle open- domain question answering using Wikipedia
as the unique knowledge source: the answer to any factoid question is a text
span in a Wikipedia article. This task of machine reading at scale combines the
challenges of document retrieval (finding the relevant articles) with that of
machine comprehension of text (identifying the answer spans from those
articles). Our approach combines a search component based on bigram hashing and
TF-IDF matching with a multi-layer recurrent neural network model trained to
detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA
datasets indicate that (1) both modules are highly competitive with respect to
existing counterparts and (2) multitask learning using distant supervision on
their combination is an effective complete system on this challenging task.
| 2,017 | Computation and Language |
One-Shot Neural Cross-Lingual Transfer for Paradigm Completion | We present a novel cross-lingual transfer method for paradigm completion, the
task of mapping a lemma to its inflected forms, using a neural encoder-decoder
model, the state of the art for the monolingual task. We use labeled data from
a high-resource language to increase performance on a low-resource language. In
experiments on 21 language pairs from four different language families, we
obtain up to 58% higher accuracy than without transfer and show that even
zero-shot and one-shot learning are possible. We further find that the degree
of language relatedness strongly influences the ability to transfer
morphological knowledge.
| 2,017 | Computation and Language |
Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems | This paper presents the Frames dataset (Frames is available at
http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues
with an average of 15 turns per dialogue. We developed this dataset to study
the role of memory in goal-oriented dialogue systems. Based on Frames, we
introduce a task called frame tracking, which extends state tracking to a
setting where several states are tracked simultaneously. We propose a baseline
model for this task. We show that Frames can also be used to study memory in
dialogue management and information presentation through natural language
generation.
| 2,017 | Computation and Language |
Psychological and Personality Profiles of Political Extremists | Global recruitment into radical Islamic movements has spurred renewed
interest in the appeal of political extremism. Is the appeal a rational
response to material conditions or is it the expression of psychological and
personality disorders associated with aggressive behavior, intolerance,
conspiratorial imagination, and paranoia? Empirical answers using surveys have
been limited by lack of access to extremist groups, while field studies have
lacked psychological measures and failed to compare extremists with contrast
groups. We revisit the debate over the appeal of extremism in the U.S. context
by comparing publicly available Twitter messages written by over 355,000
political extremist followers with messages written by non-extremist U.S.
users. Analysis of text-based psychological indicators supports the moral
foundation theory which identifies emotion as a critical factor in determining
political orientation of individuals. Extremist followers also differ from
others in four of the Big Five personality traits.
| 2,017 | Computation and Language |
Sentiment Analysis of Citations Using Word2vec | Citation sentiment analysis is an important task in scientific paper
analysis. Existing machine learning techniques for citation sentiment analysis
are focusing on labor-intensive feature engineering, which requires large
annotated corpus. As an automatic feature extraction tool, word2vec has been
successfully applied to sentiment analysis of short texts. In this work, I
conducted empirical research with the question: how well does word2vec work on
the sentiment analysis of citations? The proposed method constructed sentence
vectors (sent2vec) by averaging the word embeddings, which were learned from
Anthology Collections (ACL-Embeddings). I also investigated polarity-specific
word embeddings (PS-Embeddings) for classifying positive and negative
citations. The sentence vectors formed a feature space, to which the examined
citation sentence was mapped to. Those features were input into classifiers
(support vector machines) for supervised classification. Using
10-cross-validation scheme, evaluation was conducted on a set of annotated
citations. The results showed that word embeddings are effective on classifying
positive and negative citations. However, hand-crafted features performed
better for the overall classification.
| 2,017 | Computation and Language |
Towards Building Large Scale Multimodal Domain-Aware Conversation
Systems | While multimodal conversation agents are gaining importance in several
domains such as retail, travel etc., deep learning research in this area has
been limited primarily due to the lack of availability of large-scale, open
chatlogs. To overcome this bottleneck, in this paper we introduce the task of
multimodal, domain-aware conversations, and propose the MMD benchmark dataset.
This dataset was gathered by working in close coordination with large number of
domain experts in the retail domain. These experts suggested various
conversations flows and dialog states which are typically seen in multimodal
conversations in the fashion domain. Keeping these flows and states in mind, we
created a dataset consisting of over 150K conversation sessions between
shoppers and sales agents, with the help of in-house annotators using a
semi-automated manually intense iterative process. With this dataset, we
propose 5 new sub-tasks for multimodal conversations along with their
evaluation methodology. We also propose two multimodal neural models in the
encode-attend-decode paradigm and demonstrate their performance on two of the
sub-tasks, namely text response generation and best image response selection.
These experiments serve to establish baseline performance and open new research
directions for each of these sub-tasks. Further, for each of the sub-tasks, we
present a `per-state evaluation' of 9 most significant dialog states, which
would enable more focused research into understanding the challenges and
complexities involved in each of these states.
| 2,018 | Computation and Language |
Adversarial Connective-exploiting Networks for Implicit Discourse
Relation Classification | Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.
| 2,017 | Computation and Language |
Building a Neural Machine Translation System Using Only Synthetic
Parallel Data | Recent works have shown that synthetic parallel data automatically generated
by translation models can be effective for various neural machine translation
(NMT) issues. In this study, we build NMT systems using only synthetic parallel
data. As an efficient alternative to real parallel data, we also present a new
type of synthetic parallel corpus. The proposed pseudo parallel data are
distinct from previous works in that ground truth and synthetic examples are
mixed on both sides of sentence pairs. Experiments on Czech-German and
French-German translations demonstrate the efficacy of the proposed pseudo
parallel corpus, which shows not only enhanced results for bidirectional
translation tasks but also substantial improvement with the aid of a ground
truth real parallel corpus.
| 2,017 | Computation and Language |
Word-Alignment-Based Segment-Level Machine Translation Evaluation using
Word Embeddings | One of the most important problems in machine translation (MT) evaluation is
to evaluate the similarity between translation hypotheses with different
surface forms from the reference, especially at the segment level. We propose
to use word embeddings to perform word alignment for segment-level MT
evaluation. We performed experiments with three types of alignment methods
using word embeddings. We evaluated our proposed methods with various
translation datasets. Experimental results show that our proposed methods
outperform previous word embeddings-based methods.
| 2,017 | Computation and Language |
Syntax Aware LSTM Model for Chinese Semantic Role Labeling | As for semantic role labeling (SRL) task, when it comes to utilizing parsing
information, both traditional methods and recent recurrent neural network (RNN)
based methods use the feature engineering way. In this paper, we propose Syntax
Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies
according to dependency parsing information in order to model parsing
information directly in an architecture engineering way instead of feature
engineering way. We experimentally demonstrate that SA-LSTM gains more
improvement from the model architecture. Furthermore, SA-LSTM outperforms the
state-of-the-art on CPB 1.0 significantly according to Student t-test
($p<0.05$).
| 2,017 | Computation and Language |
Combining Lexical and Syntactic Features for Detecting Content-dense
Texts in News | Content-dense news report important factual information about an event in
direct, succinct manner. Information seeking applications such as information
extraction, question answering and summarization normally assume all text they
deal with is content-dense. Here we empirically test this assumption on news
articles from the business, U.S. international relations, sports and science
journalism domains. Our findings clearly indicate that about half of the news
texts in our study are in fact not content-dense and motivate the development
of a supervised content-density detector. We heuristically label a large
training corpus for the task and train a two-layer classifying model based on
lexical and unlexicalized syntactic features. On manually annotated data, we
compare the performance of domain-specific classifiers, trained on data only
from a given news domain and a general classifier in which data from all four
domains is pooled together. Our annotation and prediction experiments
demonstrate that the concept of content density varies depending on the domain
and that naive annotators provide judgement biased toward the stereotypical
domain label. Domain-specific classifiers are more accurate for domains in
which content-dense texts are typically fewer. Domain independent classifiers
reproduce better naive crowdsourced judgements. Classification prediction is
high across all conditions, around 80%.
| 2,017 | Computation and Language |
Multi-Task Learning of Keyphrase Boundary Classification | Keyphrase boundary classification (KBC) is the task of detecting keyphrases
in scientific articles and labelling them with respect to predefined types.
Although important in practice, this task is so far underexplored, partly due
to the lack of labelled data. To overcome this, we explore several auxiliary
tasks, including semantic super-sense tagging and identification of multi-word
expressions, and cast the task as a multi-task learning problem with deep
recurrent neural networks. Our multi-task models perform significantly better
than previous state of the art approaches on two scientific KBC datasets,
particularly for long keyphrases.
| 2,017 | Computation and Language |
A Transition-Based Directed Acyclic Graph Parser for UCCA | We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.
| 2,018 | Computation and Language |
Neural Lattice-to-Sequence Models for Uncertain Inputs | The input to a neural sequence-to-sequence model is often determined by an
up-stream system, e.g. a word segmenter, part of speech tagger, or speech
recognizer. These up-stream models are potentially error-prone. Representing
inputs through word lattices allows making this uncertainty explicit by
capturing alternative sequences and their posterior probabilities in a compact
form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a
LatticeLSTM that is able to consume word lattices, and can be used as encoder
in an attentional encoder-decoder model. We integrate lattice posterior scores
into this architecture by extending the TreeLSTM's child-sum and forget gates
and introducing a bias term into the attention mechanism. We experiment with
speech translation lattices and report consistent improvements over baselines
that translate either the 1-best hypothesis or the lattice without posterior
scores.
| 2,017 | Computation and Language |
Detection and Resolution of Rumours in Social Media: A Survey | Despite the increasing use of social media platforms for information and news
gathering, its unmoderated nature often leads to the emergence and spread of
rumours, i.e. pieces of information that are unverified at the time of posting.
At the same time, the openness of social media platforms provides opportunities
to study how users share and discuss rumours, and to explore how natural
language processing and data mining techniques may be used to find ways of
determining their veracity. In this survey we introduce and discuss two types
of rumours that circulate on social media; long-standing rumours that circulate
for long periods of time, and newly-emerging rumours spawned during fast-paced
events such as breaking news, where reports are released piecemeal and often
with an unverified status in their early stages. We provide an overview of
research into social media rumours with the ultimate goal of developing a
rumour classification system that consists of four components: rumour
detection, rumour tracking, rumour stance classification and rumour veracity
classification. We delve into the approaches presented in the scientific
literature for the development of each of these four components. We summarise
the efforts and achievements so far towards the development of rumour
classification systems and conclude with suggestions for avenues for future
research in social media mining for detection and resolution of rumours.
| 2,018 | Computation and Language |
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency
and Compositionality | Increasing the capacity of recurrent neural networks (RNN) usually involves
augmenting the size of the hidden layer, with significant increase of
computational cost. Recurrent neural tensor networks (RNTN) increase capacity
using distinct hidden layer weights for each word, but with greater costs in
memory usage. In this paper, we introduce restricted recurrent neural tensor
networks (r-RNTN) which reserve distinct hidden layer weights for frequent
vocabulary words while sharing a single set of weights for infrequent words.
Perplexity evaluations show that for fixed hidden layer sizes, r-RNTNs improve
language model performance over RNNs using only a small fraction of the
parameters of unrestricted RNTNs. These results hold for r-RNTNs using Gated
Recurrent Units and Long Short-Term Memory.
| 2,018 | Computation and Language |
Voice Conversion from Unaligned Corpora using Variational Autoencoding
Wasserstein Generative Adversarial Networks | Building a voice conversion (VC) system from non-parallel speech corpora is
challenging but highly valuable in real application scenarios. In most
situations, the source and the target speakers do not repeat the same texts or
they may even speak different languages. In this case, one possible, although
indirect, solution is to build a generative model for speech. Generative models
focus on explaining the observations with latent variables instead of learning
a pairwise transformation function, thereby bypassing the requirement of speech
frame alignment. In this paper, we propose a non-parallel VC framework with a
variational autoencoding Wasserstein generative adversarial network (VAW-GAN)
that explicitly considers a VC objective when building the speech model.
Experimental results corroborate the capability of our framework for building a
VC system from unaligned data, and demonstrate improved conversion quality.
| 2,017 | Computation and Language |
Interpretation of Semantic Tweet Representations | Research in analysis of microblogging platforms is experiencing a renewed
surge with a large number of works applying representation learning models for
applications like sentiment analysis, semantic textual similarity computation,
hashtag prediction, etc. Although the performance of the representation
learning models has been better than the traditional baselines for such tasks,
little is known about the elementary properties of a tweet encoded within these
representations, or why particular representations work better for certain
tasks. Our work presented here constitutes the first step in opening the
black-box of vector embeddings for tweets. Traditional feature engineering
methods for high-level applications have exploited various elementary
properties of tweets. We believe that a tweet representation is effective for
an application because it meticulously encodes the application-specific
elementary properties of tweets. To understand the elementary properties
encoded in a tweet representation, we evaluate the representations on the
accuracy to which they can model each of those properties such as tweet length,
presence of particular words, hashtags, mentions, capitalization, etc. Our
systematic extensive study of nine supervised and four unsupervised tweet
representations against most popular eight textual and five social elementary
properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors
(STV) best encode the textual and social properties of tweets respectively.
FastText is the best model for low resource settings, providing very little
degradation with reduction in embedding size. Finally, we draw interesting
insights by correlating the model performance obtained for elementary property
prediction tasks with the highlevel downstream applications.
| 2,017 | Computation and Language |
Japanese Sentiment Classification using a Tree-Structured Long
Short-Term Memory with Attention | Previous approaches to training syntax-based sentiment classification models
required phrase-level annotated corpora, which are not readily available in
many languages other than English. Thus, we propose the use of tree-structured
Long Short-Term Memory with an attention mechanism that pays attention to each
subtree of the parse tree. Experimental results indicate that our model
achieves the state-of-the-art performance in a Japanese sentiment
classification task.
| 2,018 | Computation and Language |
Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring
Sentiment towards Brands from Financial News Headlines | In this paper, we describe a methodology to infer Bullish or Bearish
sentiment towards companies/brands. More specifically, our approach leverages
affective lexica and word embeddings in combination with convolutional neural
networks to infer the sentiment of financial news headlines towards a target
company. Such architecture was used and evaluated in the context of the SemEval
2017 challenge (task 5, subtask 2), in which it obtained the best performance.
| 2,017 | Computation and Language |
Emotional Chatting Machine: Emotional Conversation Generation with
Internal and External Memory | Perception and expression of emotion are key factors to the success of
dialogue systems or conversational agents. However, this problem has not been
studied in large-scale conversation generation so far. In this paper, we
propose Emotional Chatting Machine (ECM) that can generate appropriate
responses not only in content (relevant and grammatical) but also in emotion
(emotionally consistent). To the best of our knowledge, this is the first work
that addresses the emotion factor in large-scale conversation generation. ECM
addresses the factor using three new mechanisms that respectively (1) models
the high-level abstraction of emotion expressions by embedding emotion
categories, (2) captures the change of implicit internal emotion states, and
(3) uses explicit emotion expressions with an external emotion vocabulary.
Experiments show that the proposed model can generate responses appropriate not
only in content but also in emotion.
| 2,018 | Computation and Language |
Character-based Joint Segmentation and POS Tagging for Chinese using
Bidirectional RNN-CRF | We present a character-based model for joint segmentation and POS tagging for
Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is
adapted and applied with novel vector representations of Chinese characters
that capture rich contextual information and lower-than-character level
features. The proposed model is extensively evaluated and compared with a
state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The
experimental results indicate that our model is accurate and robust across
datasets in different sizes, genres and annotation schemes. We obtain
state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint
segmentation and POS tagging.
| 2,017 | Computation and Language |
CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection
Methods for Semantic Textual Similarity | We present our submitted systems for Semantic Textual Similarity (STS) Track
4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must
estimate their semantic similarity by a score between 0 and 5. In our
submission, we use syntax-based, dictionary-based, context-based, and MT-based
methods. We also combine these methods in unsupervised and supervised way. Our
best run ranked 1st on track 4a with a correlation of 83.02% with human
annotations.
| 2,017 | Computation and Language |
Linear Ensembles of Word Embedding Models | This paper explores linear methods for combining several word embedding
models into an ensemble. We construct the combined models using an iterative
method based on either ordinary least squares regression or the solution to the
orthogonal Procrustes problem.
We evaluate the proposed approaches on Estonian---a morphologically complex
language, for which the available corpora for training word embeddings are
relatively small. We compare both combined models with each other and with the
input word embedding models using synonym and analogy tests. The results show
that while using the ordinary least squares regression performs poorly in our
experiments, using orthogonal Procrustes to combine several word embedding
models into an ensemble model leads to 7-10% relative improvements over the
mean result of the initial models in synonym tests and 19-47% in analogy tests.
| 2,017 | Computation and Language |
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional
Neural Networks | Over 50 million scholarly articles have been published: they constitute a
unique repository of knowledge. In particular, one may infer from them
relations between scientific concepts, such as synonyms and hyponyms.
Artificial neural networks have been recently explored for relation extraction.
In this work, we continue this line of work and present a system based on a
convolutional neural network to extract relations. Our model ranked first in
the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific
articles (subtask C).
| 2,017 | Computation and Language |
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder
Based Speech Recognition | End-to-end training of deep learning-based models allows for implicit
learning of intermediate representations based on the final task loss. However,
the end-to-end approach ignores the useful domain knowledge encoded in explicit
intermediate-level supervision. We hypothesize that using intermediate
representations as auxiliary supervision at lower levels of deep networks may
be a good way of combining the advantages of end-to-end training and more
traditional pipeline approaches. We present experiments on conversational
speech recognition where we use lower-level tasks, such as phoneme recognition,
in a multitask training approach with an encoder-decoder model for direct
character transcription. We compare multiple types of lower-level tasks and
analyze the effects of the auxiliary tasks. Our results on the Switchboard
corpus show that this approach improves recognition accuracy over a standard
encoder-decoder model on the Eval2000 test set.
| 2,017 | Computation and Language |
Automatic Measurement of Pre-aspiration | Pre-aspiration is defined as the period of glottal friction occurring in
sequences of vocalic/consonantal sonorants and phonetically voiceless
obstruents. We propose two machine learning methods for automatic measurement
of pre-aspiration duration: a feedforward neural network, which works at the
frame level; and a structured prediction model, which relies on manually
designed feature functions, and works at the segment level. The input for both
algorithms is a speech signal of an arbitrary length containing a single
obstruent, and the output is a pair of times which constitutes the
pre-aspiration boundaries. We train both models on a set of manually annotated
examples. Results suggest that the structured model is superior to the
frame-based model as it yields higher accuracy in predicting the boundaries and
generalizes to new speakers and new languages. Finally, we demonstrate the
applicability of our structured prediction algorithm by replicating linguistic
analysis of pre-aspiration in Aberystwyth English with high correlation.
| 2,017 | Computation and Language |
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled
Sequence Transduction | Labeled sequence transduction is a task of transforming one sequence into
another sequence that satisfies desiderata specified by a set of labels. In
this paper we propose multi-space variational encoder-decoders, a new model for
labeled sequence transduction with semi-supervised learning. The generative
model can use neural networks to handle both discrete and continuous latent
variables to exploit various features of data. Experiments show that our model
provides not only a powerful supervised framework but also can effectively take
advantage of the unlabeled data. On the SIGMORPHON morphological inflection
benchmark, our model outperforms single-model state-of-art results by a large
margin for the majority of languages.
| 2,017 | Computation and Language |
A Syntactic Neural Model for General-Purpose Code Generation | We consider the problem of parsing natural language descriptions into source
code written in a general-purpose programming language like Python. Existing
data-driven methods treat this problem as a language generation task without
considering the underlying syntax of the target programming language. Informed
by previous work in semantic parsing, in this paper we propose a novel neural
architecture powered by a grammar model to explicitly capture the target syntax
as prior knowledge. Experiments find this an effective way to scale up to
generation of complex programs from natural language descriptions, achieving
state-of-the-art results that well outperform previous code generation and
semantic parsing approaches.
| 2,017 | Computation and Language |
MRA - Proof of Concept of a Multilingual Report Annotator Web
Application | MRA (Multilingual Report Annotator) is a web application that translates
Radiology text and annotates it with RadLex terms. Its goal is to explore the
solution of translating non-English Radiology reports as a way to solve the
problem of most of the Text Mining tools being developed for English. In this
brief paper we explain the language barrier problem and shortly describe the
application. MRA can be found at https://github.com/lasigeBioTM/MRA .
| 2,017 | Computation and Language |
Neural Question Generation from Text: A Preliminary Study | Automatic question generation aims to generate questions from a text passage
where the generated questions can be answered by certain sub-spans of the given
passage. Traditional methods mainly use rigid heuristic rules to transform a
sentence into related questions. In this work, we propose to apply the neural
encoder-decoder model to generate meaningful and diverse questions from natural
language sentences. The encoder reads the input text and the answer position,
to produce an answer-aware input representation, which is fed to the decoder to
generate an answer focused question. We conduct a preliminary study on neural
question generation from text with the SQuAD dataset, and the experiment
results show that our method can produce fluent and diverse questions.
| 2,017 | Computation and Language |
The Interplay of Semantics and Morphology in Word Embeddings | We explore the ability of word embeddings to capture both semantic and
morphological similarity, as affected by the different types of linguistic
properties (surface form, lemma, morphological tag) used to compose the
representation of each word. We train several models, where each uses a
different subset of these properties to compose its representations. By
evaluating the models on semantic and morphological measures, we reveal some
useful insights on the relationship between semantics and morphology.
| 2,017 | Computation and Language |
An Automated Text Categorization Framework based on Hyperparameter
Optimization | A great variety of text tasks such as topic or spam identification, user
profiling, and sentiment analysis can be posed as a supervised learning problem
and tackle using a text classifier. A text classifier consists of several
subprocesses, some of them are general enough to be applied to any supervised
learning problem, whereas others are specifically designed to tackle a
particular task, using complex and computational expensive processes such as
lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we
propose a minimalistic and wide system able to tackle text classification tasks
independent of domain and language, namely microTC. It is composed by some easy
to implement text transformations, text representations, and a supervised
learning algorithm. These pieces produce a competitive classifier even in the
domain of informally written text. We provide a detailed description of microTC
along with an extensive experimental comparison with relevant state-of-the-art
methods. mircoTC was compared on 30 different datasets. Regarding accuracy,
microTC obtained the best performance in 20 datasets while achieves competitive
results in the remaining 10. The compared datasets include several problems
like topic and polarity classification, spam detection, user profiling and
authorship attribution. Furthermore, it is important to state that our approach
allows the usage of the technology even without knowledge of machine learning
and natural language processing.
| 2,017 | Computation and Language |
Conversation Modeling on Reddit using a Graph-Structured LSTM | This paper presents a novel approach for modeling threaded discussions on
social media using a graph-structured bidirectional LSTM which represents both
hierarchical and temporal conversation structure. In experiments with a task of
predicting popularity of comments in Reddit discussions, the proposed model
outperforms a node-independent architecture for different sets of input
features. Analyses show a benefit to the model over the full course of the
discussion, improving detection in both early and late stages. Further, the use
of language cues with the bidirectional tree state updates helps with
identifying controversial comments.
| 2,017 | Computation and Language |
Conceptualization Topic Modeling | Recently, topic modeling has been widely used to discover the abstract topics
in text corpora. Most of the existing topic models are based on the assumption
of three-layer hierarchical Bayesian structure, i.e. each document is modeled
as a probability distribution over topics, and each topic is a probability
distribution over words. However, the assumption is not optimal. Intuitively,
it's more reasonable to assume that each topic is a probability distribution
over concepts, and then each concept is a probability distribution over words,
i.e. adding a latent concept layer between topic layer and word layer in
traditional three-layer assumption. In this paper, we verify the proposed
assumption by incorporating the new assumption in two representative topic
models, and obtain two novel topic models. Extensive experiments were conducted
among the proposed models and corresponding baselines, and the results show
that the proposed models significantly outperform the baselines in terms of
case study and perplexity, which means the new assumption is more reasonable
than traditional one.
| 2,017 | Computation and Language |
Adposition and Case Supersenses v2.6: Guidelines for English | This document offers a detailed linguistic description of SNACS (Semantic
Network of Adposition and Case Supersenses; Schneider et al., 2018), an
inventory of 52 semantic labels ("supersenses") that characterize the use of
adpositions and case markers at a somewhat coarse level of granularity, as
demonstrated in the STREUSLE corpus (https://github.com/nert-nlp/streusle/ ;
version 4.5 tracks guidelines version 2.6). Though the SNACS inventory aspires
to be universal, this document is specific to English; documentation for other
languages will be published separately.
Version 2 is a revision of the supersense inventory proposed for English by
Schneider et al. (2015, 2016) (henceforth "v1"), which in turn was based on
previous schemes. The present inventory was developed after extensive review of
the v1 corpus annotations for English, plus previously unanalyzed genitive case
possessives (Blodgett and Schneider, 2018), as well as consideration of
adposition and case phenomena in Hebrew, Hindi, Korean, and German. Hwang et
al. (2017) present the theoretical underpinnings of the v2 scheme. Schneider et
al. (2018) summarize the scheme, its application to English corpus data, and an
automatic disambiguation task. Liu et al. (2021) offer an English Lexical
Semantic Recognition tagger that includes SNACS labels in its output.
This documentation can also be browsed alongside corpus data on the Xposition
website (Gessler et al., 2022): http://www.xposition.org/
| 2,022 | Computation and Language |
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural
Semantic Parsing | We evaluate a semantic parser based on a character-based sequence-to-sequence
model in the context of the SemEval-2017 shared task on semantic parsing for
AMRs. With data augmentation, super characters, and POS-tagging we gain major
improvements in performance compared to a baseline character-level model.
Although we improve on previous character-based neural semantic parsing models,
the overall accuracy is still lower than a state-of-the-art AMR parser. An
ensemble combining our neural semantic parser with an existing, traditional
parser, yields a small gain in performance.
| 2,017 | Computation and Language |
EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for
kEyphrase ClassificaTION | This paper describes our approach to the SemEval 2017 Task 10: "Extracting
Keyphrases and Relations from Scientific Publications", specifically to Subtask
(B): "Classification of identified keyphrases". We explored three different
deep learning approaches: a character-level convolutional neural network (CNN),
a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM.
From these approaches, we created an ensemble of differently
hyper-parameterized systems, achieving a micro-F1-score of 0.63 on the test
data. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four
according to this official score. However, we erroneously trained 2 out of 3
neural nets (the stacker and the CNN) on only roughly 15% of the full data,
namely, the original development set. When trained on the full data
(training+development), our ensemble has a micro-F1-score of 0.69. Our code is
available from https://github.com/UKPLab/semeval2017-scienceie.
| 2,017 | Computation and Language |
NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter
Sentiment Analysis | This paper describes our multi-view ensemble approach to SemEval-2017 Task 4
on Sentiment Analysis in Twitter, specifically, the Message Polarity
Classification subtask for English (subtask A). Our system is a voting
ensemble, where each base classifier is trained in a different feature space.
The first space is a bag-of-words model and has a Linear SVM as base
classifier. The second and third spaces are two different strategies of
combining word embeddings to represent sentences and use a Linear SVM and a
Logistic Regressor as base classifiers. The proposed system was ranked 18th out
of 38 systems considering F1 score and 20th considering recall.
| 2,017 | Computation and Language |
Comparison of Global Algorithms in Word Sense Disambiguation | This article compares four probabilistic algorithms (global algorithms) for
Word Sense Disambiguation (WSD) in terms of the number of scorer calls (local
algo- rithm) and the F1 score as determined by a gold-standard scorer. Two
algorithms come from the state of the art, a Simulated Annealing Algorithm
(SAA) and a Genetic Algorithm (GA) as well as two algorithms that we first
adapt from WSD that are state of the art probabilistic search algorithms,
namely a Cuckoo search algorithm (CSA) and a Bat Search algorithm (BS). As WSD
requires to evaluate exponentially many word sense combinations (with branching
factors of up to 6 or more), probabilistic algorithms allow to find approximate
solution in a tractable time by sampling the search space. We find that CSA, GA
and SA all eventually converge to similar results (0.98 F1 score), but CSA gets
there faster (in fewer scorer calls) and reaches up to 0.95 F1 before SA in
fewer scorer calls. In BA a strict convergence criterion prevents it from
reaching above 0.89 F1.
| 2,017 | Computation and Language |
A Constrained Sequence-to-Sequence Neural Model for Sentence
Simplification | Sentence simplification reduces semantic complexity to benefit people with
language impairments. Previous simplification studies on the sentence level and
word level have achieved promising results but also meet great challenges. For
sentence-level studies, sentences after simplification are fluent but sometimes
are not really simplified. For word-level studies, words are simplified but
also have potential grammar errors due to different usages of words before and
after simplification. In this paper, we propose a two-step simplification
framework by combining both the word-level and the sentence-level
simplifications, making use of their corresponding advantages. Based on the
two-step framework, we implement a novel constrained neural generation model to
simplify sentences given simplified words. The final results on Wikipedia and
Simple Wikipedia aligned datasets indicate that our method yields better
performance than various baselines.
| 2,017 | Computation and Language |
Fostering User Engagement: Rhetorical Devices for Applause Generation
Learnt from TED Talks | One problem that every presenter faces when delivering a public discourse is
how to hold the listeners' attentions or to keep them involved. Therefore, many
studies in conversation analysis work on this issue and suggest qualitatively
con-structions that can effectively lead to audience's applause. To investigate
these proposals quantitatively, in this study we an-alyze the transcripts of
2,135 TED Talks, with a particular fo-cus on the rhetorical devices that are
used by the presenters for applause elicitation. Through conducting regression
anal-ysis, we identify and interpret 24 rhetorical devices as triggers of
audience applauding. We further build models that can rec-ognize
applause-evoking sentences and conclude this work with potential implications.
| 2,017 | Computation and Language |
A Trolling Hierarchy in Social Media and A Conditional Random Field For
Trolling Detection | An-ever increasing number of social media websites, electronic newspapers and
Internet forums allow visitors to leave comments for others to read and
interact. This exchange is not free from participants with malicious
intentions, which do not contribute with the written conversation. Among
different communities users adopt strategies to handle such users. In this
paper we present a comprehensive categorization of the trolling phenomena
resource, inspired by politeness research and propose a model that jointly
predicts four crucial aspects of trolling: intention, interpretation, intention
disclosure and response strategy. Finally, we present a new annotated dataset
containing excerpts of conversations involving trolls and the interactions with
other users that we hope will be a useful resource for the research community.
| 2,017 | Computation and Language |
On the Linearity of Semantic Change: Investigating Meaning Variation via
Dynamic Graph Models | We consider two graph models of semantic change. The first is a time-series
model that relates embedding vectors from one time period to embedding vectors
of previous time periods. In the second, we construct one graph for each word:
nodes in this graph correspond to time points and edge weights to the
similarity of the word's meaning across two time points. We apply our two
models to corpora across three different languages. We find that semantic
change is linear in two senses. Firstly, today's embedding vectors (= meaning)
of words can be derived as linear combinations of embedding vectors of their
neighbors in previous time periods. Secondly, self-similarity of words decays
linearly in time. We consider both findings as new laws/hypotheses of semantic
change.
| 2,017 | Computation and Language |
Prosody: The Rhythms and Melodies of Speech | The present contribution is a tutorial on selected aspects of prosody, the
rhythms and melodies of speech, based on a course of the same name at the
Summer School on Contemporary Phonetics and Phonology at Tongji University,
Shanghai, China in July 2016. The tutorial is not intended as an introduction
to experimental methodology or as an overview of the literature on the topic,
but as an outline of observationally accessible aspects of fundamental
frequency and timing patterns with the aid of computational visualisation,
situated in a semiotic framework of sign ranks and interpretations. After an
informal introduction to the basic concepts of prosody in the introduction and
a discussion of the place of prosody in the architecture of language, a
selection of acoustic phonetic topics in phonemic tone and accent prosody, word
prosody, phrasal prosody and discourse prosody are discussed, and a stylisation
method for visualising aspects of prosody is introduced. Examples are taken
from a number of typologically different languages: Anyi/Agni (Niger-Congo>Kwa,
Ivory Coast), English, Kuki-Thadou (Sino-Tibetan, North-East India and
Myanmar), Mandarin Chinese, Tem (Niger-Congo>Gur, Togo) and Farsi. The main
focus is on fundamental frequency patterns, but issues of timing and rhythm are
also discussed. In the final section, further reading and possible future
research directions are outlined.
| 2,017 | Computation and Language |
Improving Implicit Semantic Role Labeling by Predicting Semantic Frame
Arguments | Implicit semantic role labeling (iSRL) is the task of predicting the semantic
roles of a predicate that do not appear as explicit arguments, but rather
regard common sense knowledge or are mentioned earlier in the discourse. We
introduce an approach to iSRL based on a predictive recurrent neural semantic
frame model (PRNSFM) that uses a large unannotated corpus to learn the
probability of a sequence of semantic arguments given a predicate. We leverage
the sequence probabilities predicted by the PRNSFM to estimate selectional
preferences for predicates and their arguments. On the NomBank iSRL test set,
our approach improves state-of-the-art performance on implicit semantic role
labeling with less reliance than prior work on manually constructed language
resources.
| 2,017 | Computation and Language |
Entity Linking for Queries by Searching Wikipedia Sentences | We present a simple yet effective approach for linking entities in queries.
The key idea is to search sentences similar to a query from Wikipedia articles
and directly use the human-annotated entities in the similar sentences as
candidate entities for the query. Then, we employ a rich set of features, such
as link-probability, context-matching, word embeddings, and relatedness among
candidate entities as well as their related entities, to rank the candidates
under a regression based framework. The advantages of our approach lie in two
aspects, which contribute to the ranking process and final linking result.
First, it can greatly reduce the number of candidate entities by filtering out
irrelevant entities with the words in the query. Second, we can obtain the
query sensitive prior probability in addition to the static link-probability
derived from all Wikipedia articles. We conduct experiments on two benchmark
datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ
dataset. Experimental results show that our method outperforms state-of-the-art
systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ
dataset.
| 2,017 | Computation and Language |
Character-Word LSTM Language Models | We present a Character-Word Long Short-Term Memory Language Model which both
reduces the perplexity with respect to a baseline word-level language model and
reduces the number of parameters of the model. Character information can reveal
structural (dis)similarities between words and can even be used when a word is
out-of-vocabulary, thus improving the modeling of infrequent and unknown words.
By concatenating word and character embeddings, we achieve up to 2.77% relative
improvement on English compared to a baseline model with a similar amount of
parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level
models with a larger number of parameters.
| 2,017 | Computation and Language |
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations
from Scientific Publications | We describe the SemEval task of extracting keyphrases and relations between
them from scientific documents, which is crucial for understanding which
publications describe which processes, tasks and materials. Although this was a
new task, we had a total of 26 submissions across 3 evaluation scenarios. We
expect the task and the findings reported in this paper to be relevant for
researchers working on understanding scientific content, as well as the broader
knowledge base population and information extraction communities.
| 2,017 | Computation and Language |
Pay Attention to Those Sets! Learning Quantification from Images | Major advances have recently been made in merging language and vision
representations. But most tasks considered so far have confined themselves to
the processing of objects and lexicalised relations amongst objects (content
words). We know, however, that humans (even pre-school children) can abstract
over raw data to perform certain types of higher-level reasoning, expressed in
natural language by function words. A case in point is given by their ability
to learn quantifiers, i.e. expressions like 'few', 'some' and 'all'. From
formal semantics and cognitive linguistics, we know that quantifiers are
relations over sets which, as a simplification, we can see as proportions. For
instance, in 'most fish are red', most encodes the proportion of fish which are
red fish. In this paper, we study how well current language and vision
strategies model such relations. We show that state-of-the-art attention
mechanisms coupled with a traditional linguistic formalisation of quantifiers
gives best performance on the task. Additionally, we provide insights on the
role of 'gist' representations in quantification. A 'logical' strategy to
tackle the task would be to first obtain a numerosity estimation for the two
involved sets and then compare their cardinalities. We however argue that
precisely identifying the composition of the sets is not only beyond current
state-of-the-art models but perhaps even detrimental to a task that is most
efficiently performed by refining the approximate numerosity estimator of the
system.
| 2,017 | Computation and Language |
Exploring Word Embeddings for Unsupervised Textual User-Generated
Content Normalization | Text normalization techniques based on rules, lexicons or supervised training
requiring large corpora are not scalable nor domain interchangeable, and this
makes them unsuitable for normalizing user-generated content (UGC). Current
tools available for Brazilian Portuguese make use of such techniques. In this
work we propose a technique based on distributed representation of words (or
word embeddings). It generates continuous numeric vectors of
high-dimensionality to represent words. The vectors explicitly encode many
linguistic regularities and patterns, as well as syntactic and semantic word
relationships. Words that share semantic similarity are represented by similar
vectors. Based on these features, we present a totally unsupervised, expandable
and language and domain independent method for learning normalization lexicons
from word embeddings. Our approach obtains high correction rate of orthographic
errors and internet slang in product reviews, outperforming the current
available tools for Brazilian Portuguese.
| 2,017 | Computation and Language |
Automatic Classification of the Complexity of Nonfiction Texts in
Portuguese for Early School Years | Recent research shows that most Brazilian students have serious problems
regarding their reading skills. The full development of this skill is key for
the academic and professional future of every citizen. Tools for classifying
the complexity of reading materials for children aim to improve the quality of
the model of teaching reading and text comprehension. For English, Fengs work
[11] is considered the state-of-art in grade level prediction and achieved 74%
of accuracy in automatically classifying 4 levels of textual complexity for
close school grades. There are no classifiers for nonfiction texts for close
grades in Portuguese. In this article, we propose a scheme for manual
annotation of texts in 5 grade levels, which will be used for customized
reading to avoid the lack of interest by students who are more advanced in
reading and the blocking of those that still need to make further progress. We
obtained 52% of accuracy in classifying texts into 5 levels and 74% in 3
levels. The results prove to be promising when compared to the state-of-art
work.9
| 2,016 | Computation and Language |
Automatic semantic role labeling on non-revised syntactic trees of
journalistic texts | Semantic Role Labeling (SRL) is a Natural Language Processing task that
enables the detection of events described in sentences and the participants of
these events. For Brazilian Portuguese (BP), there are two studies recently
concluded that perform SRL in journalistic texts. [1] obtained F1-measure
scores of 79.6, using the PropBank.Br corpus, which has syntactic trees
manually revised, [8], without using a treebank for training, obtained
F1-measure scores of 68.0 for the same corpus. However, the use of manually
revised syntactic trees for this task does not represent a real scenario of
application. The goal of this paper is to evaluate the performance of SRL on
revised and non-revised syntactic trees using a larger and balanced corpus of
BP journalistic texts. First, we have shown that [1]'s system also performs
better than [8]'s system on the larger corpus. Second, the SRL system trained
on non-revised syntactic trees performs better over non-revised trees than a
system trained on gold-standard data.
| 2,016 | Computation and Language |
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep
Reinforcement Learning | Building a dialogue agent to fulfill complex tasks, such as travel planning,
is challenging because the agent has to learn to collectively complete multiple
subtasks. For example, the agent needs to reserve a hotel and book a flight so
that there leaves enough time for commute between arrival and hotel check-in.
This paper addresses this challenge by formulating the task in the mathematical
framework of options over Markov Decision Processes (MDPs), and proposing a
hierarchical deep reinforcement learning approach to learning a dialogue
manager that operates at different temporal scales. The dialogue manager
consists of: (1) a top-level dialogue policy that selects among subtasks or
options, (2) a low-level dialogue policy that selects primitive actions to
complete the subtask given by the top-level policy, and (3) a global state
tracker that helps ensure all cross-subtask constraints be satisfied.
Experiments on a travel planning task with simulated and real users show that
our approach leads to significant improvements over three baselines, two based
on handcrafted rules and the other based on flat deep reinforcement learning.
| 2,017 | Computation and Language |
Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation | For extended periods of time, sequence generation models rely on beam search
algorithm to generate output sequence. However, the correctness of beam search
degrades when the a model is over-confident about a suboptimal prediction. In
this paper, we propose to perform minimum Bayes-risk (MBR) decoding for some
extra steps at a later stage. In order to speed up MBR decoding, we compute the
Bayes risks on GPU in batch mode. In our experiments, we found that MBR
reranking works with a large beam size. Later-stage MBR decoding is shown to
outperform simple MBR reranking in machine translation tasks.
| 2,017 | Computation and Language |
Persian Wordnet Construction using Supervised Learning | This paper presents an automated supervised method for Persian wordnet
construction. Using a Persian corpus and a bi-lingual dictionary, the initial
links between Persian words and Princeton WordNet synsets have been generated.
These links will be discriminated later as correct or incorrect by employing
seven features in a trained classification system. The whole method is just a
classification system, which has been trained on a train set containing FarsNet
as a set of correct instances. State of the art results on the automatically
derived Persian wordnet is achieved. The resulted wordnet with a precision of
91.18% includes more than 16,000 words and 22,000 synsets.
| 2,017 | Computation and Language |
Automatic Keyword Extraction for Text Summarization: A Survey | In recent times, data is growing rapidly in every domain such as news, social
media, banking, education, etc. Due to the excessiveness of data, there is a
need of automatic summarizer which will be capable to summarize the data
especially textual data in original document without losing any critical
purposes. Text summarization is emerged as an important research area in recent
past. In this regard, review of existing work on text summarization process is
useful for carrying out further research. In this paper, recent literature on
automatic keyword extraction and text summarization are presented since text
summarization process is highly depend on keyword extraction. This literature
includes the discussion about different methodology used for keyword extraction
and text summarization. It also discusses about different databases used for
text summarization in several domains along with evaluation matrices. Finally,
it discusses briefly about issues and research challenges faced by researchers
along with future direction.
| 2,017 | Computation and Language |
Unfolding and Shrinking Neural Machine Translation Ensembles | Ensembling is a well-known technique in neural machine translation (NMT) to
improve system performance. Instead of a single neural net, multiple neural
nets with the same topology are trained separately, and the decoder generates
predictions by averaging over the individual models. Ensembling often improves
the quality of the generated translations drastically. However, it is not
suitable for production systems because it is cumbersome and slow. This work
aims to reduce the runtime to be on par with a single system without
compromising the translation quality. First, we show that the ensemble can be
unfolded into a single large neural network which imitates the output of the
ensemble system. We show that unfolding can already improve the runtime in
practice since more work can be done on the GPU. We proceed by describing a set
of techniques to shrink the unfolded network by reducing the dimensionality of
layers. On Japanese-English we report that the resulting network has the size
and decoding speed of a single NMT network but performs on the level of a
3-ensemble system.
| 2,017 | Computation and Language |
What we really want to find by Sentiment Analysis: The Relationship
between Computational Models and Psychological State | As the first step to model emotional state of a person, we build sentiment
analysis models with existing deep neural network algorithms and compare the
models with psychological measurements to enlighten the relationship. In the
experiments, we first examined psychological state of 64 participants and asked
them to summarize the story of a book, Chronicle of a Death Foretold (Marquez,
1981). Secondly, we trained models using crawled 365,802 movie review data;
then we evaluated participants' summaries using the pretrained model as a
concept of transfer learning. With the background that emotion affects on
memories, we investigated the relationship between the evaluation score of the
summaries from computational models and the examined psychological
measurements. The result shows that although CNN performed the best among other
deep neural network algorithms (LSTM, GRU), its results are not related to the
psychological state. Rather, GRU shows more explainable results depending on
the psychological state. The contribution of this paper can be summarized as
follows: (1) we enlighten the relationship between computational models and
psychological measurements. (2) we suggest this framework as objective methods
to evaluate the emotion; the real sentiment analysis of a person.
| 2,018 | Computation and Language |
What do Neural Machine Translation Models Learn about Morphology? | Neural machine translation (MT) models obtain state-of-the-art performance
while maintaining a simple, end-to-end architecture. However, little is known
about what these models learn about source and target languages during the
training process. In this work, we analyze the representations learned by
neural MT models at various levels of granularity and empirically evaluate the
quality of the representations for learning morphology through extrinsic
part-of-speech and morphological tagging tasks. We conduct a thorough
investigation along several parameters: word-based vs. character-based
representations, depth of the encoding layer, the identity of the target
language, and encoder vs. decoder representations. Our data-driven,
quantitative evaluation sheds light on important aspects in the neural MT
system and its ability to capture word structure.
| 2,017 | Computation and Language |
ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with
Multilingual Relational Knowledge | This paper describes Luminoso's participation in SemEval 2017 Task 2,
"Multilingual and Cross-lingual Semantic Word Similarity", with a system based
on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses
on general knowledge that relates the meanings of words and phrases. Our
submission to SemEval was an update of previous work that builds high-quality,
multilingual word embeddings from a combination of ConceptNet and
distributional semantics. Our system took first place in both subtasks. It
ranked first in 4 out of 5 of the separate languages, and also ranked first in
all 10 of the cross-lingual language pairs.
| 2,017 | Computation and Language |
Representation Stability as a Regularizer for Improved Text Analytics
Transfer Learning | Although neural networks are well suited for sequential transfer learning
tasks, the catastrophic forgetting problem hinders proper integration of prior
knowledge. In this work, we propose a solution to this problem by using a
multi-task objective based on the idea of distillation and a mechanism that
directly penalizes forgetting at the shared representation layer during the
knowledge integration phase of training. We demonstrate our approach on a
Twitter domain sentiment analysis task with sequential knowledge transfer from
four related tasks. We show that our technique outperforms networks fine-tuned
to the target task. Additionally, we show both through empirical evidence and
examples that it does not forget useful knowledge from the source task that is
forgotten during standard fine-tuning. Surprisingly, we find that first
distilling a human made rule based sentiment engine into a recurrent neural
network and then integrating the knowledge with the target task data leads to a
substantial gain in generalization performance. Our experiments demonstrate the
power of multi-source transfer techniques in practical text analytics problems
when paired with distillation. In particular, for the SemEval 2016 Task 4
Subtask A (Nakov et al., 2016) dataset we surpass the state of the art
established during the competition with a comparatively simple model
architecture that is not even competitive when trained on only the labeled task
specific data.
| 2,017 | Computation and Language |
Trainable Referring Expression Generation using Overspecification
Preferences | Referring expression generation (REG) models that use speaker-dependent
information require a considerable amount of training data produced by every
individual speaker, or may otherwise perform poorly. In this work we present a
simple REG experiment that allows the use of larger training data sets by
grouping speakers according to their overspecification preferences. Intrinsic
evaluation shows that this method generally outperforms the personalised method
found in previous work.
| 2,017 | Computation and Language |
Incremental Skip-gram Model with Negative Sampling | This paper explores an incremental training strategy for the skip-gram model
with negative sampling (SGNS) from both empirical and theoretical perspectives.
Existing methods of neural word embeddings, including SGNS, are multi-pass
algorithms and thus cannot perform incremental model update. To address this
problem, we present a simple incremental extension of SGNS and provide a
thorough theoretical analysis to demonstrate its validity. Empirical
experiments demonstrated the correctness of the theoretical analysis as well as
the practical usefulness of the incremental algorithm.
| 2,017 | Computation and Language |
Mobile Keyboard Input Decoding with Finite-State Transducers | We propose a finite-state transducer (FST) representation for the models used
to decode keyboard inputs on mobile devices. Drawing from learnings from the
field of speech recognition, we describe a decoding framework that can satisfy
the strict memory and latency constraints of keyboard input. We extend this
framework to support functionalities typically not present in speech
recognition, such as literal decoding, autocorrections, word completions, and
next word predictions.
We describe the general framework of what we call for short the keyboard "FST
decoder" as well as the implementation details that are new compared to a
speech FST decoder. We demonstrate that the FST decoder enables new UX features
such as post-corrections. Finally, we sketch how this decoder can support
advanced features such as personalization and contextualization.
| 2,017 | Computation and Language |
A Neural Model for User Geolocation and Lexical Dialectology | We propose a simple yet effective text- based user geolocation model based on
a neural network with one hidden layer, which achieves state of the art
performance over three Twitter benchmark geolocation datasets, in addition to
producing word and phrase embeddings in the hidden layer that we show to be
useful for detecting dialectal terms. As part of our analysis of dialectal
terms, we release DAREDS, a dataset for evaluating dialect term detection
methods.
| 2,017 | Computation and Language |
Cross-lingual and cross-domain discourse segmentation of entire
documents | Discourse segmentation is a crucial step in building end-to-end discourse
parsers. However, discourse segmenters only exist for a few languages and
domains. Typically they only detect intra-sentential segment boundaries,
assuming gold standard sentence and token segmentation, and relying on
high-quality syntactic parses and rich heuristics that are not generally
available across languages and domains. In this paper, we propose statistical
discourse segmenters for five languages and three domains that do not rely on
gold pre-annotations. We also consider the problem of learning discourse
segmenters when no labeled data is available for a language. Our fully
supervised system obtains 89.5% F1 for English newswire, with slight drops in
performance on other domains, and we report supervised and unsupervised
(cross-lingual) results for five languages in total.
| 2,017 | Computation and Language |
Learning Joint Multilingual Sentence Representations with Neural Machine
Translation | In this paper, we use the framework of neural machine translation to learn
joint sentence representations across six very different languages. Our aim is
that a representation which is independent of the language, is likely to
capture the underlying semantics. We define a new cross-lingual similarity
measure, compare up to 1.4M sentence representations and study the
characteristics of close sentences. We provide experimental evidence that
sentences that are close in embedding space are indeed semantically highly
related, but often have quite different structure and syntax. These relations
also hold when comparing sentences in different languages.
| 2,017 | Computation and Language |
Room for improvement in automatic image description: an error analysis | In recent years we have seen rapid and significant progress in automatic
image description but what are the open problems in this area? Most work has
been evaluated using text-based similarity metrics, which only indicate that
there have been improvements, without explaining what has improved. In this
paper, we present a detailed error analysis of the descriptions generated by a
state-of-the-art attention-based model. Our analysis operates on two levels:
first we check the descriptions for accuracy, and then we categorize the types
of errors we observe in the inaccurate descriptions. We find only 20% of the
descriptions are free from errors, and surprisingly that 26% are unrelated to
the image. Finally, we manually correct the most frequently occurring error
types (e.g. gender identification) to estimate the performance reward for
addressing these errors, observing gains of 0.2--1 BLEU point per type.
| 2,017 | Computation and Language |
Learning Latent Representations for Speech Generation and Transformation | An ability to model a generative process and learn a latent representation
for speech in an unsupervised fashion will be crucial to process vast
quantities of unlabelled speech data. Recently, deep probabilistic generative
models such as Variational Autoencoders (VAEs) have achieved tremendous success
in modeling natural images. In this paper, we apply a convolutional VAE to
model the generative process of natural speech. We derive latent space
arithmetic operations to disentangle learned latent representations. We
demonstrate the capability of our model to modify the phonetic content or the
speaker identity for speech segments using the derived operations, without the
need for parallel supervisory data.
| 2,017 | Computation and Language |
Identity and Granularity of Events in Text | In this paper we describe a method to detect event descrip- tions in
different news articles and to model the semantics of events and their
components using RDF representations. We compare these descriptions to solve a
cross-document event coreference task. Our com- ponent approach to event
semantics defines identity and granularity of events at different levels. It
performs close to state-of-the-art approaches on the cross-document event
coreference task, while outperforming other works when assuming similar quality
of event detection. We demonstrate how granularity and identity are
interconnected and we discuss how se- mantic anomaly could be used to define
differences between coreference, subevent and topical relations.
| 2,017 | Computation and Language |
An entity-driven recursive neural network model for chinese discourse
coherence modeling | Chinese discourse coherence modeling remains a challenge taskin Natural
Language Processing field.Existing approaches mostlyfocus on the need for
feature engineering, whichadoptthe sophisticated features to capture the logic
or syntactic or semantic relationships acrosssentences within a text.In this
paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse
coherence evaluation based on current English discourse coherenceneural network
model. Specifically, to overcome the shortage of identifying the entity(nouns)
overlap across sentences in the currentmodel, Our combined modelsuccessfully
investigatesthe entities information into the recursive neural network
freamework.Evaluation results on both sentence ordering and machine translation
coherence rating task show the effectiveness of the proposed model, which
significantly outperforms the existing strong baseline.
| 2,017 | Computation and Language |
Exploiting Cross-Sentence Context for Neural Machine Translation | In translation, considering the document as a whole can help to resolve
ambiguities and inconsistencies. In this paper, we propose a cross-sentence
context-aware approach and investigate the influence of historical contextual
information on the performance of neural machine translation (NMT). First, this
history is summarized in a hierarchical way. We then integrate the historical
representation into NMT in two strategies: 1) a warm-start of encoder and
decoder states, and 2) an auxiliary context source for updating decoder states.
Experimental results on a large Chinese-English translation task show that our
approach significantly improves upon a strong attention-based NMT system by up
to +2.1 BLEU points.
| 2,017 | Computation and Language |
Get To The Point: Summarization with Pointer-Generator Networks | Neural sequence-to-sequence models have provided a viable new approach for
abstractive text summarization (meaning they are not restricted to simply
selecting and rearranging passages from the original text). However, these
models have two shortcomings: they are liable to reproduce factual details
inaccurately, and they tend to repeat themselves. In this work we propose a
novel architecture that augments the standard sequence-to-sequence attentional
model in two orthogonal ways. First, we use a hybrid pointer-generator network
that can copy words from the source text via pointing, which aids accurate
reproduction of information, while retaining the ability to produce novel words
through the generator. Second, we use coverage to keep track of what has been
summarized, which discourages repetition. We apply our model to the CNN / Daily
Mail summarization task, outperforming the current abstractive state-of-the-art
by at least 2 ROUGE points.
| 2,017 | Computation and Language |
How Robust Are Character-Based Word Embeddings in Tagging and MT Against
Wrod Scramlbing or Randdm Nouse? | This paper investigates the robustness of NLP against perturbed word forms.
While neural approaches can achieve (almost) human-like accuracy for certain
tasks and conditions, they often are sensitive to small changes in the input
such as non-canonical input (e.g., typos). Yet both stability and robustness
are desired properties in applications involving user-generated content, and
the more as humans easily cope with such noisy or adversary conditions. In this
paper, we study the impact of noisy input. We consider different noise
distributions (one type of noise, combination of noise types) and mismatched
noise distributions for training and testing. Moreover, we empirically evaluate
the robustness of different models (convolutional neural networks, recurrent
neural networks, non-neural models), different basic units (characters, byte
pair encoding units), and different NLP tasks (morphological tagging, machine
translation).
| 2,017 | Computation and Language |
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics | Coreference evaluation metrics are hard to optimize directly as they are
non-differentiable functions, not easily decomposable into elementary
decisions. Consequently, most approaches optimize objectives only indirectly
related to the end goal, resulting in suboptimal performance. Instead, we
propose a differentiable relaxation that lends itself to gradient-based
optimisation, thus bypassing the need for reinforcement learning or heuristic
modification of cross-entropy. We show that by modifying the training objective
of a competitive neural coreference system, we obtain a substantial gain in
performance. This suggests that our approach can be regarded as a viable
alternative to using reinforcement learning or more computationally expensive
imitation learning.
| 2,017 | Computation and Language |
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of
Concept Maps | Concept maps can be used to concisely represent important information and
bring structure into large document collections. Therefore, we study a variant
of multi-document summarization that produces summaries in the form of concept
maps. However, suitable evaluation datasets for this task are currently
missing. To close this gap, we present a newly created corpus of concept maps
that summarize heterogeneous collections of web documents on educational
topics. It was created using a novel crowdsourcing approach that allows us to
efficiently determine important elements in large document collections. We
release the corpus along with a baseline system and proposed evaluation
protocol to enable further research on this variant of summarization.
| 2,017 | Computation and Language |
Cardinal Virtues: Extracting Relation Cardinalities from Text | Information extraction (IE) from text has largely focused on relations
between individual entities, such as who has won which award. However, some
facts are never fully mentioned, and no IE method has perfect recall. Thus, it
is beneficial to also tap contents about the cardinalities of these relations,
for example, how many awards someone has won. We introduce this novel problem
of extracting cardinalities and discusses the specific challenges that set it
apart from standard IE. We present a distant supervision method using
conditional random fields. A preliminary evaluation results in precision
between 3% and 55%, depending on the difficulty of relations.
| 2,017 | Computation and Language |
ShapeWorld - A new test methodology for multimodal language
understanding | We introduce a novel framework for evaluating multimodal deep learning models
with respect to their language understanding and generalization abilities. In
this approach, artificial data is automatically generated according to the
experimenter's specifications. The content of the data, both during training
and evaluation, can be controlled in detail, which enables tasks to be created
that require true generalization abilities, in particular the combination of
previously introduced concepts in novel ways. We demonstrate the potential of
our methodology by evaluating various visual question answering models on four
different tasks, and show how our framework gives us detailed insights into
their capabilities and limitations. By open-sourcing our framework, we hope to
stimulate progress in the field of multimodal language understanding.
| 2,017 | Computation and Language |
Neural Machine Translation Model with a Large Vocabulary Selected by
Branching Entropy | Neural machine translation (NMT), a new approach to machine translation, has
achieved promising results comparable to those of traditional approaches such
as statistical machine translation (SMT). Despite its recent success, NMT
cannot handle a larger vocabulary because the training complexity and decoding
complexity proportionally increase with the number of target words. This
problem becomes even more serious when translating patent documents, which
contain many technical terms that are observed infrequently. In this paper, we
propose to select phrases that contain out-of-vocabulary words using the
statistical approach of branching entropy. This allows the proposed NMT system
to be applied to a translation task of any language pair without any
language-specific knowledge about technical term identification. The selected
phrases are then replaced with tokens during training and post-translated by
the phrase translation table of SMT. Evaluation on Japanese-to-Chinese,
Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent
sentence translation proved the effectiveness of phrases selected with
branching entropy, where the proposed NMT model achieves a substantial
improvement over a baseline NMT model without our proposed technique. Moreover,
the number of translation errors of under-translation by the baseline NMT model
without our proposed technique reduces to around half by the proposed NMT
model.
| 2,017 | Computation and Language |
Translation of Patent Sentences with a Large Vocabulary of Technical
Terms Using Neural Machine Translation | Neural machine translation (NMT), a new approach to machine translation, has
achieved promising results comparable to those of traditional approaches such
as statistical machine translation (SMT). Despite its recent success, NMT
cannot handle a larger vocabulary because training complexity and decoding
complexity proportionally increase with the number of target words. This
problem becomes even more serious when translating patent documents, which
contain many technical terms that are observed infrequently. In NMTs, words
that are out of vocabulary are represented by a single unknown token. In this
paper, we propose a method that enables NMT to translate patent sentences
comprising a large vocabulary of technical terms. We train an NMT system on
bilingual data wherein technical terms are replaced with technical term tokens;
this allows it to translate most of the source sentences except technical
terms. Further, we use it as a decoder to translate source sentences with
technical term tokens and replace the tokens with technical term translations
using SMT. We also use it to rerank the 1,000-best SMT translations on the
basis of the average of the SMT score and that of the NMT rescoring of the
translated sentences with technical term tokens. Our experiments on
Japanese-Chinese patent sentences show that the proposed NMT system achieves a
substantial improvement of up to 3.1 BLEU points and 2.3 RIBES points over
traditional SMT systems and an improvement of approximately 0.6 BLEU points and
0.8 RIBES points over an equivalent NMT system without our proposed technique.
| 2,017 | Computation and Language |
Neural Extractive Summarization with Side Information | Most extractive summarization methods focus on the main body of the document
from which sentences need to be extracted. However, the gist of the document
may lie in side information, such as the title and image captions which are
often available for newswire articles. We propose to explore side information
in the context of single-document extractive summarization. We develop a
framework for single-document summarization composed of a hierarchical document
encoder and an attention-based extractor with attention over side information.
We evaluate our model on a large scale news dataset. We show that extractive
summarization with side information consistently outperforms its counterpart
that does not use any side information, in terms of both informativeness and
fluency.
| 2,017 | Computation and Language |
Cross-lingual Abstract Meaning Representation Parsing | Abstract Meaning Representation (AMR) annotation efforts have mostly focused
on English. In order to train parsers on other languages, we propose a method
based on annotation projection, which involves exploiting annotations in a
source language and a parallel corpus of the source language and a target
language. Using English as the source language, we show promising results for
Italian, Spanish, German and Chinese as target languages. Besides evaluating
the target parsers on non-gold datasets, we further propose an evaluation
method that exploits the English gold annotations and does not require access
to gold annotations for the target languages. This is achieved by inverting the
projection process: a new English parser is learned from the target language
parser and evaluated on the existing English gold standard.
| 2,018 | Computation and Language |
Distributional Modeling on a Diet: One-shot Word Learning from Text Only | We test whether distributional models can do one-shot learning of
definitional properties from text only. Using Bayesian models, we find that
first learning overarching structure in the known data, regularities in textual
contexts and in properties, helps one-shot learning, and that individual
context items can be highly informative. Our experiments show that our model
can learn properties from a single exposure when given an informative
utterance.
| 2,017 | Computation and Language |
Neural Paraphrase Identification of Questions with Noisy Pretraining | We present a solution to the problem of paraphrase identification of
questions. We focus on a recent dataset of question pairs annotated with binary
paraphrase labels and show that a variant of the decomposable attention model
(Parikh et al., 2016) results in accurate performance on this task, while being
far simpler than many competing neural architectures. Furthermore, when the
model is pretrained on a noisy dataset of automatically collected question
paraphrases, it obtains the best reported performance on the dataset.
| 2,017 | Computation and Language |
MUSE: Modularizing Unsupervised Sense Embeddings | This paper proposes to address the word sense ambiguity issue in an
unsupervised manner, where word sense representations are learned along a word
sense selection mechanism given contexts. Prior work focused on designing a
single model to deliver both mechanisms, and thus suffered from either
coarse-grained representation learning or inefficient sense selection. The
proposed modular approach, MUSE, implements flexible modules to optimize
distinct mechanisms, achieving the first purely sense-level representation
learning system with linear-time sense selection. We leverage reinforcement
learning to enable joint training on the proposed modules, and introduce
various exploration techniques on sense selection for better robustness. The
experiments on benchmark data show that the proposed approach achieves the
state-of-the-art performance on synonym selection as well as on contextual word
similarities in terms of MaxSimC.
| 2,017 | Computation and Language |
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation | We present a simple and effective approach to incorporating syntactic
structure into neural attention-based encoder-decoder models for machine
translation. We rely on graph-convolutional networks (GCNs), a recent class of
neural networks developed for modeling graph-structured data. Our GCNs use
predicted syntactic dependency trees of source sentences to produce
representations of words (i.e. hidden states of the encoder) that are sensitive
to their syntactic neighborhoods. GCNs take word representations as input and
produce word representations as output, so they can easily be incorporated as
layers into standard encoders (e.g., on top of bidirectional RNNs or
convolutional neural networks). We evaluate their effectiveness with
English-German and English-Czech translation experiments for different types of
encoders and observe substantial improvements over their syntax-agnostic
versions in all the considered setups.
| 2,020 | Computation and Language |
RACE: Large-scale ReAding Comprehension Dataset From Examinations | We present RACE, a new dataset for benchmark evaluation of methods in the
reading comprehension task. Collected from the English exams for middle and
high school Chinese students in the age range between 12 to 18, RACE consists
of near 28,000 passages and near 100,000 questions generated by human experts
(English instructors), and covers a variety of topics which are carefully
designed for evaluating the students' ability in understanding and reasoning.
In particular, the proportion of questions that requires reasoning is much
larger in RACE than that in other benchmark datasets for reading comprehension,
and there is a significant gap between the performance of the state-of-the-art
models (43%) and the ceiling human performance (95%). We hope this new dataset
can serve as a valuable resource for research and evaluation in machine
comprehension. The dataset is freely available at
http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at
https://github.com/qizhex/RACE_AR_baselines.
| 2,017 | Computation and Language |
Towards String-to-Tree Neural Machine Translation | We present a simple method to incorporate syntactic information about the
target language in a neural machine translation system by translating into
linearized, lexicalized constituency trees. An experiment on the WMT16
German-English news translation task resulted in an improved BLEU score when
compared to a syntax-agnostic NMT baseline trained on the same dataset. An
analysis of the translations from the syntax-aware system shows that it
performs more reordering during translation in comparison to the baseline. A
small-scale human evaluation also showed an advantage to the syntax-aware
system.
| 2,017 | Computation and Language |
A Neural Architecture for Generating Natural Language Descriptions from
Source Code Changes | We propose a model to automatically describe changes introduced in the source
code of a program using natural language. Our method receives as input a set of
code commits, which contains both the modifications and message introduced by
an user. These two modalities are used to train an encoder-decoder
architecture. We evaluated our approach on twelve real world open source
projects from four different programming languages. Quantitative and
qualitative results showed that the proposed approach can generate feasible and
semantically sound descriptions not only in standard in-project settings, but
also in a cross-project setting.
| 2,017 | Computation and Language |
Learning Character-level Compositionality with Visual Features | Previous work has modeled the compositionality of words by creating
character-level models of meaning, reducing problems of sparsity for rare
words. However, in many writing systems compositionality has an effect even on
the character-level: the meaning of a character is derived by the sum of its
parts. In this paper, we model this effect by creating embeddings for
characters based on their visual characteristics, creating an image for the
character and running it through a convolutional neural network to produce a
visual character embedding. Experiments on a text classification task
demonstrate that such model allows for better processing of instances with rare
characters in languages such as Chinese, Japanese, and Korean. Additionally,
qualitative analyses demonstrate that our proposed model learns to focus on the
parts of characters that carry semantic content, resulting in embeddings that
are coherent in visual space.
| 2,017 | Computation and Language |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.