Titles
stringlengths 6
220
| Abstracts
stringlengths 37
3.26k
| Years
int64 1.99k
2.02k
| Categories
stringclasses 1
value |
|---|---|---|---|
Decoding Sentiment from Distributed Representations of Sentences | Distributed representations of sentences have been developed recently to
represent their meaning as real-valued vectors. However, it is not clear how
much information such representations retain about the polarity of sentences.
To study this question, we decode sentiment from unsupervised sentence
representations learned with different architectures (sensitive to the order of
words, the order of sentences, or none) in 9 typologically diverse languages.
Sentiment results from the (recursive) composition of lexical items and
grammatical strategies such as negation and concession. The results are
manifold: we show that there is no `one-size-fits-all' representation
architecture outperforming the others across the board. Rather, the top-ranking
architectures depend on the language and data at hand. Moreover, we find that
in several cases the additive composition model based on skip-gram word vectors
may surpass supervised state-of-art architectures such as bidirectional LSTMs.
Finally, we provide a possible explanation of the observed variation based on
the type of negative constructions in each language.
| 2,017 | Computation and Language |
Information Density as a Factor for Variation in the Embedding of
Relative Clauses | In German, relative clauses can be positioned in-situ or extraposed. A
potential factor for the variation might be information density. In this study,
this hypothesis is tested with a corpus of 17th century German funeral sermons.
For each referent in the relative clauses and their matrix clauses, the
attention state was determined (first calculation). In a second calculation,
for each word the surprisal values were determined, using a bi-gram language
model. In a third calculation, the surprisal values were accommodated as to
whether it is the first occurrence of the word in question or not. All three
calculations pointed in the same direction: With in-situ relative clauses, the
rate of new referents was lower and the average surprisal values were lower,
especially the accommodated surprisal values, than with extraposed relative
clauses. This indicated that in-formation density is a factor governing the
choice between in-situ and extraposed relative clauses. The study also sheds
light on the intrinsic relation-ship between the information theoretic concept
of information density and in-formation structural concepts such as givenness
which are used under a more linguistic perspective.
| 2,017 | Computation and Language |
Universal Dependencies Parsing for Colloquial Singaporean English | Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies scheme, and then training a neural network model by integrating
English syntactic knowledge into a state-of-the-art parser trained on the
Singlish treebank. Results show that English knowledge can lead to 25% relative
error reduction, resulting in a parser of 84.47% accuracies. To the best of our
knowledge, we are the first to use neural stacking to improve cross-lingual
dependency parsing on low-resource languages. We make both our annotation and
parser available for further research.
| 2,017 | Computation and Language |
ParlAI: A Dialog Research Software Platform | We introduce ParlAI (pronounced "par-lay"), an open-source software platform
for dialog research implemented in Python, available at http://parl.ai. Its
goal is to provide a unified framework for sharing, training and testing of
dialog models, integration of Amazon Mechanical Turk for data collection, human
evaluation, and online/reinforcement learning; and a repository of machine
learning models for comparing with others' models, and improving upon existing
architectures. Over 20 tasks are supported in the first release, including
popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail,
CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated,
including neural models such as memory networks, seq2seq and attentive LSTMs.
| 2,018 | Computation and Language |
A Lightweight Regression Method to Infer Psycholinguistic Properties for
Brazilian Portuguese | Psycholinguistic properties of words have been used in various approaches to
Natural Language Processing tasks, such as text simplification and readability
assessment. Most of these properties are subjective, involving costly and
time-consuming surveys to be gathered. Recent approaches use the limited
datasets of psycholinguistic properties to extend them automatically to large
lexicons. However, some of the resources used by such approaches are not
available to most languages. This study presents a method to infer
psycholinguistic properties for Brazilian Portuguese (BP) using regressors
built with a light set of features usually available for less resourced
languages: word length, frequency lists, lexical databases composed of school
dictionaries and word embedding models. The correlations between the properties
inferred are close to those obtained by related works. The resulting resource
contains 26,874 words in BP annotated with concreteness, age of acquisition,
imageability and subjective frequency.
| 2,017 | Computation and Language |
Search Engine Guided Non-Parametric Neural Machine Translation | In this paper, we extend an attention-based neural machine translation (NMT)
model by allowing it to access an entire training set of parallel sentence
pairs even after training. The proposed approach consists of two stages. In the
first stage--retrieval stage--, an off-the-shelf, black-box search engine is
used to retrieve a small subset of sentence pairs from a training set given a
source sentence. These pairs are further filtered based on a fuzzy matching
score based on edit distance. In the second stage--translation stage--, a novel
translation model, called translation memory enhanced NMT (TM-NMT), seamlessly
uses both the source sentence and a set of retrieved sentence pairs to perform
the translation. Empirical evaluation on three language pairs (En-Fr, En-De,
and En-Es) shows that the proposed approach significantly outperforms the
baseline approach and the improvement is more significant when more relevant
sentence pairs were retrieved.
| 2,018 | Computation and Language |
Formalized Lambek Calculus in Higher Order Logic (HOL4) | In this project, a rather complete proof-theoretical formalization of Lambek
Calculus (non-associative with arbitrary extensions) has been ported from Coq
proof assistent to HOL4 theorem prover, with some improvements and new
theorems.
Three deduction systems (Syntactic Calculus, Natural Deduction and Sequent
Calculus) of Lambek Calculus are defined with many related theorems proved. The
equivalance between these systems are formally proved. Finally, a formalization
of Sequent Calculus proofs (where Coq has built-in supports) has been designed
and implemented in HOL4. Some basic results including the sub-formula
properties of the so-called "cut-free" proofs are formally proved.
This work can be considered as the preliminary work towards a language parser
based on category grammars which is not multimodal but still has ability to
support context-sensitive languages through customized extensions.
| 2,017 | Computation and Language |
Mixed Membership Word Embeddings for Computational Social Science | Word embeddings improve the performance of NLP systems by revealing the
hidden structural relationships between words. Despite their success in many
applications, word embeddings have seen very little use in computational social
science NLP tasks, presumably due to their reliance on big data, and to a lack
of interpretability. I propose a probabilistic model-based word embedding
method which can recover interpretable embeddings, without big data. The key
insight is to leverage mixed membership modeling, in which global
representations are shared, but individual entities (i.e. dictionary words) are
free to use these representations to uniquely differing degrees. I show how to
train the model using a combination of state-of-the-art training techniques for
word embeddings and topic models. The experimental results show an improvement
in predictive language modeling of up to 63% in MRR over the skip-gram, and
demonstrate that the representations are beneficial for supervised learning. I
illustrate the interpretability of the models with computational social science
case studies on State of the Union addresses and NIPS articles.
| 2,018 | Computation and Language |
Spelling Correction as a Foreign Language | In this paper, we reformulated the spell correction problem as a machine
translation task under the encoder-decoder framework. This reformulation
enabled us to use a single model for solving the problem that is traditionally
formulated as learning a language model and an error model. This model employs
multi-layer recurrent neural networks as an encoder and a decoder. We
demonstrate the effectiveness of this model using an internal dataset, where
the training data is automatically obtained from user logs. The model offers
competitive performance as compared to the state of the art methods but does
not require any feature engineering nor hand tuning between models.
| 2,019 | Computation and Language |
Recurrent Additive Networks | We introduce recurrent additive networks (RANs), a new gated RNN which is
distinguished by the use of purely additive latent state updates. At every time
step, the new state is computed as a gated component-wise sum of the input and
the previous state, without any of the non-linearities commonly used in RNN
transition dynamics. We formally show that RAN states are weighted sums of the
input vectors, and that the gates only contribute to computing the weights of
these sums. Despite this relatively simple functional form, experiments
demonstrate that RANs perform on par with LSTMs on benchmark language modeling
problems. This result shows that many of the non-linear computations in LSTMs
and related networks are not essential, at least for the problems we consider,
and suggests that the gates are doing more of the computational work than
previously understood.
| 2,017 | Computation and Language |
Learning Semantic Relatedness From Human Feedback Using Metric Learning | Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.
| 2,017 | Computation and Language |
W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis | With the increase of online customer opinions in specialised websites and
social networks, the necessity of automatic systems to help to organise and
classify customer reviews by domain-specific aspect/categories and sentiment
polarity is more important than ever. Supervised approaches to Aspect Based
Sentiment Analysis obtain good results for the domain/language their are
trained on, but having manually labelled data for training supervised systems
for all domains and languages are usually very costly and time consuming. In
this work we describe W2VLDA, an almost unsupervised system based on topic
modelling, that combined with some other unsupervised methods and a minimal
configuration, performs aspect/category classifiation,
aspect-terms/opinion-words separation and sentiment polarity classification for
any given domain and language. We evaluate the performance of the aspect and
sentiment classification in the multilingual SemEval 2016 task 5 (ABSA)
dataset. We show competitive results for several languages (English, Spanish,
French and Dutch) and domains (hotels, restaurants, electronic-devices).
| 2,017 | Computation and Language |
Ask the Right Questions: Active Question Reformulation with
Reinforcement Learning | We frame Question Answering (QA) as a Reinforcement Learning task, an
approach that we call Active Question Answering. We propose an agent that sits
between the user and a black box QA system and learns to reformulate questions
to elicit the best possible answers. The agent probes the system with,
potentially many, natural language reformulations of an initial question and
aggregates the returned evidence to yield the best answer. The reformulation
system is trained end-to-end to maximize answer quality using policy gradient.
We evaluate on SearchQA, a dataset of complex questions extracted from
Jeopardy!. The agent outperforms a state-of-the-art base model, playing the
role of the environment, and other benchmarks. We also analyze the language
that the agent has learned while interacting with the question answering
system. We find that successful question reformulations look quite different
from natural language paraphrases. The agent is able to discover non-trivial
reformulation strategies that resemble classic information retrieval techniques
such as term re-weighting (tf-idf) and stemming.
| 2,018 | Computation and Language |
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech
Recognition | With the evolution of neural network based methods, automatic speech
recognition (ASR) field has been advanced to a level where building an
application with speech interface is a reality. In spite of these advances,
building a real-time speech recogniser faces several problems such as low
recognition accuracy, domain constraint, and out-of-vocabulary words. The low
recognition accuracy problem is addressed by improving the acoustic model,
language model, decoder and by rescoring the N-best list at the output of the
decoder. We are considering the N-best list rescoring approach to improve the
recognition accuracy. Most of the methods in the literature use the
grammatical, lexical, syntactic and semantic connection between the words in a
recognised sentence as a feature to rescore. In this paper, we have tried to
see the semantic relatedness between the words in a sentence to rescore the
N-best list. Semantic relatedness is computed using
TransE~\cite{bordes2013translating}, a method for low dimensional embedding of
a triple in a knowledge graph. The novelty of the paper is the application of
semantic web to automatic speech recognition.
| 2,017 | Computation and Language |
Latent Human Traits in the Language of Social Media: An Open-Vocabulary
Approach | Over the past century, personality theory and research has successfully
identified core sets of characteristics that consistently describe and explain
fundamental differences in the way people think, feel and behave. Such
characteristics were derived through theory, dictionary analyses, and survey
research using explicit self-reports. The availability of social media data
spanning millions of users now makes it possible to automatically derive
characteristics from language use -- at large scale. Taking advantage of
linguistic information available through Facebook, we study the process of
inferring a new set of potential human traits based on unprompted language use.
We subject these new traits to a comprehensive set of evaluations and compare
them with a popular five factor model of personality. We find that our
language-based trait construct is often more generalizable in that it often
predicts non-questionnaire-based outcomes better than questionnaire-based
traits (e.g. entities someone likes, income and intelligence quotient), while
the factors remain nearly as stable as traditional factors. Our approach
suggests a value in new constructs of personality derived from everyday human
language use.
| 2,019 | Computation and Language |
Contextualizing Citations for Scientific Summarization using Word
Embeddings and Domain Knowledge | Citation texts are sometimes not very informative or in some cases inaccurate
by themselves; they need the appropriate context from the referenced paper to
reflect its exact contributions. To address this problem, we propose an
unsupervised model that uses distributed representation of words as well as
domain knowledge to extract the appropriate context from the reference paper.
Evaluation results show the effectiveness of our model by significantly
outperforming the state-of-the-art. We furthermore demonstrate how an effective
contextualization method results in improving citation-based summarization of
the scientific articles.
| 2,017 | Computation and Language |
Local Monotonic Attention Mechanism for End-to-End Speech and Language
Processing | Recently, encoder-decoder neural networks have shown impressive performance
on many sequence-related tasks. The architecture commonly uses an attentional
mechanism which allows the model to learn alignments between the source and the
target sequence. Most attentional mechanisms used today is based on a global
attention property which requires a computation of a weighted summarization of
the whole input sequence generated by encoder states. However, it is
computationally expensive and often produces misalignment on the longer input
sequence. Furthermore, it does not fit with monotonous or left-to-right nature
in several tasks, such as automatic speech recognition (ASR),
grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention
mechanism that has local and monotonic properties. Various ways to control
those properties are also explored. Experimental results on ASR, G2P and
machine translation between two languages with similar sentence structures,
demonstrate that the proposed encoder-decoder model with local monotonic
attention could achieve significant performance improvements and reduce the
computational complexity in comparison with the one that used the standard
global attention architecture.
| 2,017 | Computation and Language |
Better Text Understanding Through Image-To-Text Transfer | Generic text embeddings are successfully used in a variety of tasks. However,
they are often learnt by capturing the co-occurrence structure from pure text
corpora, resulting in limitations of their ability to generalize. In this
paper, we explore models that incorporate visual information into the text
representation. Based on comprehensive ablation studies, we propose a
conceptually simple, yet well performing architecture. It outperforms previous
multimodal approaches on a set of well established benchmarks. We also improve
the state-of-the-art results for image-related text datasets, using orders of
magnitude less data.
| 2,017 | Computation and Language |
Question-Answering with Grammatically-Interpretable Representations | We introduce an architecture, the Tensor Product Recurrent Network (TPRN). In
our application of TPRN, internal representations learned by end-to-end
optimization in a deep neural network performing a textual question-answering
(QA) task can be interpreted using basic concepts from linguistic theory. No
performance penalty need be paid for this increased interpretability: the
proposed model performs comparably to a state-of-the-art system on the SQuAD QA
task. The internal representation which is interpreted is a Tensor Product
Representation: for each input word, the model selects a symbol to encode the
word, and a role in which to place the symbol, and binds the two together. The
selection is via soft attention. The overall interpretation is built from
interpretations of the symbols, as recruited by the trained model, and
interpretations of the roles as used by the model. We find support for our
initial hypothesis that symbols can be interpreted as lexical-semantic word
meanings, while roles can be interpreted as approximations of grammatical roles
(or categories) such as subject, wh-word, determiner, etc. Fine-grained
analysis reveals specific correspondences between the learned roles and parts
of speech as assigned by a standard tagger (Toutanova et al. 2003), and finds
several discrepancies in the model's favor. In this sense, the model learns
significant aspects of grammar, after having been exposed solely to
linguistically unannotated text, questions, and answers: no prior linguistic
knowledge is given to the model. What is given is the means to build
representations using symbols and roles, with an inductive bias favoring use of
these in an approximately discrete manner.
| 2,017 | Computation and Language |
Second-Order Word Embeddings from Nearest Neighbor Topological Features | We introduce second-order vector representations of words, induced from
nearest neighborhood topological features in pre-trained contextual word
embeddings. We then analyze the effects of using second-order embeddings as
input features in two deep natural language processing models, for named entity
recognition and recognizing textual entailment, as well as a linear model for
paraphrase recognition. Surprisingly, we find that nearest neighbor information
alone is sufficient to capture most of the performance benefits derived from
using pre-trained word embeddings. Furthermore, second-order embeddings are
able to handle highly heterogeneous data better than first-order
representations, though at the cost of some specificity. Additionally,
augmenting contextual embeddings with second-order information further improves
model performance in some cases. Due to variance in the random initializations
of word embeddings, utilizing nearest neighbor features from multiple
first-order embedding samples can also contribute to downstream performance
gains. Finally, we identify intriguing characteristics of second-order
embedding spaces for further research, including much higher density and
different semantic interpretations of cosine similarity.
| 2,017 | Computation and Language |
Deep Investigation of Cross-Language Plagiarism Detection Methods | This paper is a deep investigation of cross-language plagiarism detection
methods on a new recently introduced open dataset, which contains parallel and
comparable collections of documents with multiple characteristics (different
genres, languages and sizes of texts). We investigate cross-language plagiarism
detection methods for 6 language pairs on 2 granularities of text units in
order to draw robust conclusions on the best methods while deeply analyzing
correlations across document styles and languages.
| 2,017 | Computation and Language |
Parsing with CYK over Distributed Representations | Syntactic parsing is a key task in natural language processing. This task has
been dominated by symbolic, grammar-based parsers. Neural networks, with their
distributed representations, are challenging these methods. In this article we
show that existing symbolic parsing algorithms can cross the border and be
entirely formulated over distributed representations. To this end we introduce
a version of the traditional Cocke-Younger-Kasami (CYK) algorithm, called
D-CYK, which is entirely defined over distributed representations. Our D-CYK
uses matrix multiplication on real number matrices of size independent of the
length of the input string. These operations are compatible with traditional
neural networks. Experiments show that our D-CYK approximates the original CYK
algorithm. By showing that CYK can be entirely performed on distributed
representations, we open the way to the definition of recurrent layers of
CYK-informed neural networks.
| 2,020 | Computation and Language |
Joint PoS Tagging and Stemming for Agglutinative Languages | The number of word forms in agglutinative languages is theoretically infinite
and this variety in word forms introduces sparsity in many natural language
processing tasks. Part-of-speech tagging (PoS tagging) is one of these tasks
that often suffers from sparsity. In this paper, we present an unsupervised
Bayesian model using Hidden Markov Models (HMMs) for joint PoS tagging and
stemming for agglutinative languages. We use stemming to reduce sparsity in PoS
tagging. Two tasks are jointly performed to provide a mutual benefit in both
tasks. Our results show that joint POS tagging and stemming improves PoS
tagging scores. We present results for Turkish and Finnish as agglutinative
languages and English as a morphologically poor language.
| 2,017 | Computation and Language |
Deep Voice 2: Multi-Speaker Neural Text-to-Speech | We introduce a technique for augmenting neural text-to-speech (TTS) with
lowdimensional trainable speaker embeddings to generate different voices from a
single model. As a starting point, we show improvements over the two
state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and
Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with
Deep Voice 1, but constructed with higher performance building blocks and
demonstrates a significant audio quality improvement over Deep Voice 1. We
improve Tacotron by introducing a post-processing neural vocoder, and
demonstrate a significant audio quality improvement. We then demonstrate our
technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron
on two multi-speaker TTS datasets. We show that a single neural TTS system can
learn hundreds of unique voices from less than half an hour of data per
speaker, while achieving high audio quality synthesis and preserving the
speaker identities almost perfectly.
| 2,017 | Computation and Language |
Max-Cosine Matching Based Neural Models for Recognizing Textual
Entailment | Recognizing textual entailment is a fundamental task in a variety of text
mining or natural language processing applications. This paper proposes a
simple neural model for RTE problem. It first matches each word in the
hypothesis with its most-similar word in the premise, producing an augmented
representation of the hypothesis conditioned on the premise as a sequence of
word pairs. The LSTM model is then used to model this augmented sequence, and
the final output from the LSTM is fed into a softmax layer to make the
prediction. Besides the base model, in order to enhance its performance, we
also proposed three techniques: the integration of multiple word-embedding
library, bi-way integration, and ensemble based on model averaging.
Experimental results on the SNLI dataset have shown that the three techniques
are effective in boosting the predicative accuracy and that our method
outperforms several state-of-the-state ones.
| 2,017 | Computation and Language |
Jointly Learning Sentence Embeddings and Syntax with Unsupervised
Tree-LSTMs | We introduce a neural network that represents sentences by composing their
words according to induced binary parse trees. We use Tree-LSTM as our
composition function, applied along a tree structure found by a fully
differentiable natural language chart parser. Our model simultaneously
optimises both the composition function and the parser, thus eliminating the
need for externally-provided parse trees which are normally required for
Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised
with respect to the parse trees. As it is fully differentiable, our model is
easily trained with an off-the-shelf gradient descent method and
backpropagation. We demonstrate that it achieves better performance compared to
various supervised Tree-LSTM architectures on a textual entailment task and a
reverse dictionary task.
| 2,019 | Computation and Language |
Learning Structured Text Representations | In this paper, we focus on learning structure-aware document representations
from data without recourse to a discourse parser or additional annotations.
Drawing inspiration from recent efforts to empower neural networks with a
structural bias, we propose a model that can encode a document while
automatically inducing rich structural dependencies. Specifically, we embed a
differentiable non-projective parsing algorithm into a neural model and use
attention mechanisms to incorporate the structural biases. Experimental
evaluation across different tasks and datasets shows that the proposed model
achieves state-of-the-art results on document modeling tasks while inducing
intermediate structures which are both interpretable and meaningful.
| 2,018 | Computation and Language |
ASR error management for improving spoken language understanding | This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .
| 2,017 | Computation and Language |
Biomedical Event Trigger Identification Using Bidirectional Recurrent
Neural Network Based Models | Biomedical events describe complex interactions between various biomedical
entities. Event trigger is a word or a phrase which typically signifies the
occurrence of an event. Event trigger identification is an important first step
in all event extraction methods. However many of the current approaches either
rely on complex hand-crafted features or consider features only within a
window. In this paper we propose a method that takes the advantage of recurrent
neural network (RNN) to extract higher level features present across the
sentence. Thus hidden state representation of RNN along with word and entity
type embedding as features avoid relying on the complex hand-crafted features
generated using various NLP toolkits. Our experiments have shown to achieve
state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have
also performed category-wise analysis of the result and discussed the
importance of various features in trigger identification task.
| 2,017 | Computation and Language |
Detecting and Explaining Crisis | Individuals on social media may reveal themselves to be in various states of
crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
from social media text automatically and accurately can have profound
consequences. However, detecting a general state of crisis without explaining
why has limited applications. An explanation in this context is a coherent,
concise subset of the text that rationalizes the crisis detection. We explore
several methods to detect and explain crisis using a combination of neural and
non-neural techniques. We evaluate these techniques on a unique data set
obtained from Koko, an anonymous emotional support network available through
various messaging applications. We annotate a small subset of the samples
labeled with crisis with corresponding explanations. Our best technique
significantly outperforms the baseline for detection and explanation.
| 2,017 | Computation and Language |
Style Transfer from Non-Parallel Text by Cross-Alignment | This paper focuses on style transfer on the basis of non-parallel text. This
is an instance of a broad family of problems including machine translation,
decipherment, and sentiment modification. The key challenge is to separate the
content from other aspects such as style. We assume a shared latent content
distribution across different text corpora, and propose a method that leverages
refined alignment of latent representations to perform style transfer. The
transferred sentences from one style should match example sentences from the
other style as a population. We demonstrate the effectiveness of this
cross-alignment method on three tasks: sentiment modification, decipherment of
word substitution ciphers, and recovery of word order.
| 2,017 | Computation and Language |
Helping News Editors Write Better Headlines: A Recommender to Improve
the Keyword Contents & Shareability of News Headlines | We present a software tool that employs state-of-the-art natural language
processing (NLP) and machine learning techniques to help newspaper editors
compose effective headlines for online publication. The system identifies the
most salient keywords in a news article and ranks them based on both their
overall popularity and their direct relevance to the article. The system also
uses a supervised regression model to identify headlines that are likely to be
widely shared on social media. The user interface is designed to simplify and
speed the editor's decision process on the composition of the headline. As
such, the tool provides an efficient way to combine the benefits of automated
predictors of engagement and search-engine optimization (SEO) with human
judgments of overall headline quality.
| 2,016 | Computation and Language |
Semi-Supervised Model Training for Unbounded Conversational Speech
Recognition | For conversational large-vocabulary continuous speech recognition (LVCSR)
tasks, up to about two thousand hours of audio is commonly used to train state
of the art models. Collection of labeled conversational audio however, is
prohibitively expensive, laborious and error-prone. Furthermore, academic
corpora like Fisher English (2004) or Switchboard (1992) are inadequate to
train models with sufficient accuracy in the unbounded space of conversational
speech. These corpora are also timeworn due to dated acoustic telephony
features and the rapid advancement of colloquial vocabulary and idiomatic
speech over the last decades. Utilizing the colossal scale of our unlabeled
telephony dataset, we propose a technique to construct a modern, high quality
conversational speech training corpus on the order of hundreds of millions of
utterances (or tens of thousands of hours) for both acoustic and language model
training. We describe the data collection, selection and training, evaluating
the results of our updated speech recognition system on a test corpus of 7K
manually transcribed utterances. We show relative word error rate (WER)
reductions of {35%, 19%} on {agent, caller} utterances over our seed model and
5% absolute WER improvements over IBM Watson STT on this conversational speech
task.
| 2,017 | Computation and Language |
word2vec Skip-Gram with Negative Sampling is a Weighted Logistic PCA | We show that the skip-gram formulation of word2vec trained with negative
sampling is equivalent to a weighted logistic PCA. This connection allows us to
better understand the objective, compare it to other word embedding methods,
and extend it to higher dimensional models.
| 2,017 | Computation and Language |
On the relation between dependency distance, crossing dependencies, and
parsing. Comment on "Dependency distance: a new perspective on syntactic
patterns in natural languages" by Haitao Liu et al | Liu et al. (2017) provide a comprehensive account of research on dependency
distance in human languages. While the article is a very rich and useful report
on this complex subject, here I will expand on a few specific issues where
research in computational linguistics (specifically natural language
processing) can inform DDM research, and vice versa. These aspects have not
been explored much in the article by Liu et al. or elsewhere, probably due to
the little overlap between both research communities, but they may provide
interesting insights for improving our understanding of the evolution of human
languages, the mechanisms by which the brain processes and understands
language, and the construction of effective computer systems to achieve this
goal.
| 2,017 | Computation and Language |
Understanding Abuse: A Typology of Abusive Language Detection Subtasks | As the body of research on abusive language detection and analysis grows,
there is a need for critical consideration of the relationships between
different subtasks that have been grouped under this label. Based on work on
hate speech, cyberbullying, and online abuse we propose a typology that
captures central similarities and differences between subtasks and we discuss
its implications for data annotation and feature construction. We emphasize the
practical actions that can be taken by researchers to best approach their
abusive language detection subtask of interest.
| 2,017 | Computation and Language |
Listen, Interact and Talk: Learning to Speak via Interaction | One of the long-term goals of artificial intelligence is to build an agent
that can communicate intelligently with human in natural language. Most
existing work on natural language learning relies heavily on training over a
pre-collected dataset with annotated labels, leading to an agent that
essentially captures the statistics of the fixed external training data. As the
training data is essentially a static snapshot representation of the knowledge
from the annotator, the agent trained this way is limited in adaptiveness and
generalization of its behavior. Moreover, this is very different from the
language learning process of humans, where language is acquired during
communication by taking speaking action and learning from the consequences of
speaking action in an interactive manner. This paper presents an interactive
setting for grounded natural language learning, where an agent learns natural
language by interacting with a teacher and learning from feedback, thus
learning and improving language skills while taking part in the conversation.
To achieve this goal, we propose a model which incorporates both imitation and
reinforcement by leveraging jointly sentence and reward feedbacks from the
teacher. Experiments are conducted to validate the effectiveness of the
proposed approach.
| 2,017 | Computation and Language |
Neural Semantic Parsing by Character-based Translation: Experiments with
Abstract Meaning Representations | We evaluate the character-level translation method for neural semantic
parsing on a large corpus of sentences annotated with Abstract Meaning
Representations (AMRs). Using a sequence-to-sequence model, and some trivial
preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1
(F-score on AMR-triples). We examine five different approaches to improve this
baseline result: (i) reordering AMR branches to match the word order of the
input sentence increases performance to 58.3; (ii) adding part-of-speech tags
(automatically produced) to the input shows improvement as well (57.2); (iii)
So does the introduction of super characters (conflating frequent sequences of
characters to a single character), reaching 57.4; (iv) optimizing the training
process by using pre-training and averaging a set of models increases
performance to 58.7; (v) adding silver-standard training data obtained by an
off-the-shelf parser yields the biggest improvement, resulting in an F-score of
64.0. Combining all five techniques leads to an F-score of 71.0 on holdout
data, which is state-of-the-art in AMR parsing. This is remarkable because of
the relative simplicity of the approach.
| 2,017 | Computation and Language |
Deep Learning for User Comment Moderation | Experimenting with a new dataset of 1.6M user comments from a Greek news
portal and existing datasets of English Wikipedia comments, we show that an RNN
outperforms the previous state of the art in moderation. A deep,
classification-specific attention mechanism improves further the overall
performance of the RNN. We also compare against a CNN and a word-list baseline,
considering both fully automatic and semi-automatic moderation.
| 2,017 | Computation and Language |
Subject Specific Stream Classification Preprocessing Algorithm for
Twitter Data Stream | Micro-blogging service Twitter is a lucrative source for data mining
applications on global sentiment. But due to the omnifariousness of the
subjects mentioned in each data item; it is inefficient to run a data mining
algorithm on the raw data. This paper discusses an algorithm to accurately
classify the entire stream in to a given number of mutually exclusive
collectively exhaustive streams upon each of which the data mining algorithm
can be run separately yielding more relevant results with a high efficiency.
| 2,017 | Computation and Language |
Supervised Complementary Entity Recognition with Augmented Key-value
Pairs of Knowledge | Extracting opinion targets is an important task in sentiment analysis on
product reviews and complementary entities (products) are one important type of
opinion targets that may work together with the reviewed product. In this
paper, we address the problem of Complementary Entity Recognition (CER) as a
supervised sequence labeling with the capability of expanding domain knowledge
as key-value pairs from unlabeled reviews, by automatically learning and
enhancing knowledge-based features. We use Conditional Random Field (CRF) as
the base learner and augment CRF with knowledge-based features (called the
Knowledge-based CRF or KCRF for short). We conduct experiments to show that
KCRF effectively improves the performance of supervised CER task.
| 2,017 | Computation and Language |
Dynamics of core of language vocabulary | Studies of the overall structure of vocabulary and its dynamics became
possible due to creation of diachronic text corpora, especially Google Books
Ngram. This article discusses the question of core change rate and the degree
to which the core words cover the texts. Different periods of the last three
centuries and six main European languages presented in Google Books Ngram are
compared. The main result is high stability of core change rate, which is
analogous to stability of the Swadesh list.
| 2,016 | Computation and Language |
An Automatic Contextual Analysis and Clustering Classifiers Ensemble
approach to Sentiment Analysis | Products reviews are one of the major resources to determine the public
sentiment. The existing literature on reviews sentiment analysis mainly
utilizes supervised paradigm, which needs labeled data to be trained on and
suffers from domain-dependency. This article addresses these issues by
describes a completely automatic approach for sentiment analysis based on
unsupervised ensemble learning. The method consists of two phases. The first
phase is contextual analysis, which has five processes, namely (1) data
preparation; (2) spelling correction; (3) intensifier handling; (4) negation
handling and (5) contrast handling. The second phase comprises the unsupervised
learning approach, which is an ensemble of clustering classifiers using a
majority voting mechanism with different weight schemes. The base classifier of
the ensemble method is a modified k-means algorithm. The base classifier is
modified by extracting initial centroids from the feature set via using
SentWordNet (SWN). We also introduce new sentiment analysis problems of
Australian airlines and home builders which offer potential benchmark problems
in the sentiment analysis field. Our experiments on datasets from different
domains show that contextual analysis and the ensemble phases improve the
clustering performance in term of accuracy, stability and generalization
ability.
| 2,017 | Computation and Language |
On Multilingual Training of Neural Dependency Parsers | We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistically relevant concepts can be inferred from word
spellings. We analyze the representations of characters and words that are
learned by the network to establish which properties of languages were
accounted for. In particular we show that the parser has approximately learned
to associate Latin characters with their Cyrillic counterparts and that it can
group Polish and Russian words that have a similar grammatical function.
Finally, we evaluate the parser on selected languages from the Universal
Dependencies dataset and show that it is competitive with other recently
proposed state-of-the art methods, while having a simple structure.
| 2,017 | Computation and Language |
Latent Intention Dialogue Models | Developing a dialogue agent that is capable of making autonomous decisions
and communicating by natural language is one of the long-term goals of machine
learning research. Traditional approaches either rely on hand-crafting a small
state-action set for applying reinforcement learning that is not scalable or
constructing deterministic models for learning dialogue sentences that fail to
capture natural conversational variability. In this paper, we propose a Latent
Intention Dialogue Model (LIDM) that employs a discrete latent variable to
learn underlying dialogue intentions in the framework of neural variational
inference. In a goal-oriented dialogue scenario, these latent intentions can be
interpreted as actions guiding the generation of machine responses, which can
be further refined autonomously by reinforcement learning. The experimental
evaluation of LIDM shows that the model out-performs published benchmarks for
both corpus-based and human evaluation, demonstrating the effectiveness of
discrete latent variable models for learning goal-oriented dialogues.
| 2,017 | Computation and Language |
Who's to say what's funny? A computer using Language Models and Deep
Learning, That's Who! | Humor is a defining characteristic of human beings. Our goal is to develop
methods that automatically detect humorous statements and rank them on a
continuous scale. In this paper we report on results using a Language Model
approach, and outline our plans for using methods from Deep Learning.
| 2,017 | Computation and Language |
On the "Calligraphy" of Books | Authorship attribution is a natural language processing task that has been
widely studied, often by considering small order statistics. In this paper, we
explore a complex network approach to assign the authorship of texts based on
their mesoscopic representation, in an attempt to capture the flow of the
narrative. Indeed, as reported in this work, such an approach allowed the
identification of the dominant narrative structure of the studied authors. This
has been achieved due to the ability of the mesoscopic approach to take into
account relationships between different, not necessarily adjacent, parts of the
text, which is able to capture the story flow. The potential of the proposed
approach has been illustrated through principal component analysis, a
comparison with the chance baseline method, and network visualization. Such
visualizations reveal individual characteristics of the authors, which can be
understood as a kind of calligraphy.
| 2,017 | Computation and Language |
Character-Based Text Classification using Top Down Semantic Model for
Sentence Representation | Despite the success of deep learning on many fronts especially image and
speech, its application in text classification often is still not as good as a
simple linear SVM on n-gram TF-IDF representation especially for smaller
datasets. Deep learning tends to emphasize on sentence level semantics when
learning a representation with models like recurrent neural network or
recursive neural network, however from the success of TF-IDF representation, it
seems a bag-of-words type of representation has its strength. Taking advantage
of both representions, we present a model known as TDSM (Top Down Semantic
Model) for extracting a sentence representation that considers both the
word-level semantics by linearly combining the words with attention weights and
the sentence-level semantics with BiLSTM and use it on text classification. We
apply the model on characters and our results show that our model is better
than all the other character-based and word-based convolutional neural network
models by \cite{zhang15} across seven different datasets with only 1\% of their
parameters. We also demonstrate that this model beats traditional linear models
on TF-IDF vectors on small and polished datasets like news article in which
typically deep learning models surrender.
| 2,017 | Computation and Language |
The Importance of Automatic Syntactic Features in Vietnamese Named
Entity Recognition | This paper presents a state-of-the-art system for Vietnamese Named Entity
Recognition (NER). By incorporating automatic syntactic features with word
embeddings as input for bidirectional Long Short-Term Memory (Bi-LSTM), our
system, although simpler than some deep learning architectures, achieves a much
better result for Vietnamese NER. The proposed method achieves an overall F1
score of 92.05% on the test set of an evaluation campaign, organized in late
2016 by the Vietnamese Language and Speech Processing (VLSP) community. Our
named entity recognition system outperforms the best previous systems for
Vietnamese NER by a large margin.
| 2,017 | Computation and Language |
A Low Dimensionality Representation for Language Variety Identification | Language variety identification aims at labelling texts in a native language
(e.g. Spanish, Portuguese, English) with its specific variation (e.g.
Argentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work
we propose a low dimensionality representation (LDR) to address this task with
five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain.
We compare our LDR method with common state-of-the-art representations and show
an increase in accuracy of ~35%. Furthermore, we compare LDR with two reference
distributed representation models. Experimental results show competitive
performance while dramatically reducing the dimensionality --and increasing the
big data suitability-- to only 6 features per variety. Additionally, we analyse
the behaviour of the employed machine learning algorithms and the most
discriminating features. Finally, we employ an alternative dataset to test the
robustness of our low dimensionality representation with another set of similar
languages.
| 2,016 | Computation and Language |
Character Composition Model with Convolutional Neural Networks for
Dependency Parsing on Morphologically Rich Languages | We present a transition-based dependency parser that uses a convolutional
neural network to compose word representations from characters. The character
composition model shows great improvement over the word-lookup model,
especially for parsing agglutinative languages. These improvements are even
better than using pre-trained word embeddings from extra data. On the SPMRL
data sets, our system outperforms the previous best greedy parser (Ballesteros
et al., 2015) by a margin of 3% on average.
| 2,017 | Computation and Language |
Does the Geometry of Word Embeddings Help Document Classification? A
Case Study on Persistent Homology Based Representations | We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space. In this work, we evaluate the utility of
these topology-based document representations in traditional NLP tasks,
specifically document clustering and sentiment classification. We find that the
embeddings do not benefit text analysis. In fact, performance is worse than
simple techniques like $\textit{tf-idf}$, indicating that the geometry of the
document does not provide enough variability for classification on the basis of
topic or sentiment in the chosen datasets.
| 2,017 | Computation and Language |
Adversarial Generation of Natural Language | Generative Adversarial Networks (GANs) have gathered a lot of attention from
the computer vision community, yielding impressive results for image
generation. Advances in the adversarial generation of natural language from
noise however are not commensurate with the progress made in generating images,
and still lag far behind likelihood based methods. In this paper, we take a
step towards generating natural language with a GAN objective alone. We
introduce a simple baseline that addresses the discrete output space problem
without relying on gradient estimators and show that it is able to achieve
state-of-the-art results on a Chinese poem generation dataset. We present
quantitative results on generating sentences from context-free and
probabilistic context-free grammars, and qualitative language modeling results.
A conditional version is also described that can generate sequences conditioned
on sentence characteristics.
| 2,017 | Computation and Language |
Analysis of the Effect of Dependency Information on Predicate-Argument
Structure Analysis and Zero Anaphora Resolution | This paper investigates and analyzes the effect of dependency information on
predicate-argument structure analysis (PASA) and zero anaphora resolution (ZAR)
for Japanese, and shows that a straightforward approach of PASA and ZAR works
effectively even if dependency information was not available. We constructed an
analyzer that directly predicts relationships of predicates and arguments with
their semantic roles from a POS-tagged corpus. The features of the system are
designed to compensate for the absence of syntactic information by using
features used in dependency parsing as a reference. We also constructed
analyzers that use the oracle dependency and the real dependency parsing
results, and compared with the system that does not use any syntactic
information to verify that the improvement provided by dependencies is not
crucial.
| 2,017 | Computation and Language |
Adversarial Ranking for Language Generation | Generative adversarial networks (GANs) have great successes on synthesizing
data. However, the existing GANs restrict the discriminator to be a binary
classifier, and thus limit their learning capacity for tasks that need to
synthesize output with rich structures such as natural language descriptions.
In this paper, we propose a novel generative adversarial network, RankGAN, for
generating high-quality language descriptions. Rather than training the
discriminator to learn and assign absolute binary predicate for individual data
sample, the proposed RankGAN is able to analyze and rank a collection of
human-written and machine-written sentences by giving a reference group. By
viewing a set of data samples collectively and evaluating their quality through
relative ranking scores, the discriminator is able to make better assessment
which in turn helps to learn a better generator. The proposed RankGAN is
optimized through the policy gradient technique. Experimental results on
multiple public datasets clearly demonstrate the effectiveness of the proposed
approach.
| 2,018 | Computation and Language |
Learning When to Attend for Neural Machine Translation | In the past few years, attention mechanisms have become an indispensable
component of end-to-end neural machine translation models. However, previous
attention models always refer to some source words when predicting a target
word, which contradicts with the fact that some target words have no
corresponding source words. Motivated by this observation, we propose a novel
attention model that has the capability of determining when a decoder should
attend to source words and when it should not. Experimental results on NIST
Chinese-English translation tasks show that the new model achieves an
improvement of 0.8 BLEU score over a state-of-the-art baseline.
| 2,017 | Computation and Language |
Are distributional representations ready for the real world? Evaluating
word vectors for grounded perceptual meaning | Distributional word representation methods exploit word co-occurrences to
build compact vector encodings of words. While these representations enjoy
widespread use in modern natural language processing, it is unclear whether
they accurately encode all necessary facets of conceptual meaning. In this
paper, we evaluate how well these representations can predict perceptual and
conceptual features of concrete concepts, drawing on two semantic norm datasets
sourced from human participants. We find that several standard word
representations fail to encode many salient perceptual features of concepts,
and show that these deficits correlate with word-word similarity prediction
errors. Our analyses provide motivation for grounded and embodied language
learning approaches, which may help to remedy these deficits.
| 2,017 | Computation and Language |
Teaching Machines to Describe Images via Natural Language Feedback | Robots will eventually be part of every household. It is thus critical to
enable algorithms to learn from and be guided by non-expert users. In this
paper, we bring a human in the loop, and enable a human teacher to give
feedback to a learning agent in the form of natural language. We argue that a
descriptive sentence can provide a much stronger learning signal than a numeric
reward in that it can easily point to where the mistakes are and how to correct
them. We focus on the problem of image captioning in which the quality of the
output can easily be judged by non-experts. We propose a hierarchical
phrase-based captioning model trained with policy gradients, and design a
feedback network that provides reward to the learner by conditioning on the
human-provided feedback. We show that by exploiting descriptive feedback our
model learns to perform better than when given independently written human
captions.
| 2,017 | Computation and Language |
Semantic Refinement GRU-based Neural Language Generation for Spoken
Dialogue Systems | Natural language generation (NLG) plays a critical role in spoken dialogue
systems. This paper presents a new approach to NLG by using recurrent neural
networks (RNN), in which a gating mechanism is applied before RNN computation.
This allows the proposed model to generate appropriate sentences. The RNN-based
generator can be learned from unaligned data by jointly training sentence
planning and surface realization to produce natural language responses. The
model was extensively evaluated on four different NLG domains. The results show
that the proposed generator achieved better performance on all the NLG domains
compared to previous generators.
| 2,017 | Computation and Language |
Natural Language Generation for Spoken Dialogue System using RNN
Encoder-Decoder Networks | Natural language generation (NLG) is a critical component in a spoken
dialogue system. This paper presents a Recurrent Neural Network based
Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to
select, aggregate semantic elements produced by an attention mechanism over the
input elements, and to produce the required utterances. The proposed generator
can be jointly trained both sentence planning and surface realization to
produce natural language sentences. The proposed model was extensively
evaluated on four different NLG datasets. The experimental results showed that
the proposed generators not only consistently outperform the previous methods
across all the NLG domains but also show an ability to generalize from a new,
unseen domain and learn from multi-domain datasets.
| 2,017 | Computation and Language |
Deep Learning for Hate Speech Detection in Tweets | Hate speech detection on Twitter is critical for applications like
controversial event extraction, building AI chatterbots, content
recommendation, and sentiment analysis. We define this task as being able to
classify a tweet as racist, sexist or neither. The complexity of the natural
language constructs makes this task very challenging. We perform extensive
experiments with multiple deep learning architectures to learn semantic word
embeddings to handle this complexity. Our experiments on a benchmark dataset of
16K annotated tweets show that such deep learning methods outperform
state-of-the-art char/word n-gram methods by ~18 F1 points.
| 2,017 | Computation and Language |
Polish Read Speech Corpus for Speech Tools and Services | This paper describes the speech processing activities conducted at the Polish
consortium of the CLARIN project. The purpose of this segment of the project
was to develop specific tools that would allow for automatic and semi-automatic
processing of large quantities of acoustic speech data. The tools include the
following: grapheme-to-phoneme conversion, speech-to-text alignment, voice
activity detection, speaker diarization, keyword spotting and automatic speech
transcription. Furthermore, in order to develop these tools, a large
high-quality studio speech corpus was recorded and released under an open
license, to encourage development in the area of Polish speech research.
Another purpose of the corpus was to serve as a reference for studies in
phonetics and pronunciation. All the tools and resources were released on the
the Polish CLARIN website. This paper discusses the current status and future
plans for the project.
| 2,017 | Computation and Language |
Using of heterogeneous corpora for training of an ASR system | The paper summarizes the development of the LVCSR system built as a part of
the Pashto speech-translation system at the SCALE (Summer Camp for Applied
Language Exploration) 2015 workshop on "Speech-to-text-translation for
low-resource languages". The Pashto language was chosen as a good "proxy"
low-resource language, exhibiting multiple phenomena which make the
speech-recognition and and speech-to-text-translation systems development hard.
Even when the amount of data is seemingly sufficient, given the fact that the
data originates from multiple sources, the preliminary experiments reveal that
there is little to no benefit in merging (concatenating) the corpora and more
elaborate ways of making use of all of the data must be worked out.
This paper concentrates only on the LVCSR part and presents a range of
different techniques that were found to be useful in order to benefit from
multiple different corpora
| 2,017 | Computation and Language |
Discovering Discrete Latent Topics with Neural Variational Inference | Topic models have been widely explored as probabilistic generative models of
documents. Traditional inference methods have sought closed-form derivations
for updating the models, however as the expressiveness of these models grows,
so does the difficulty of performing fast and accurate inference over their
parameters. This paper presents alternative neural approaches to topic
modelling by providing parameterisable distributions over topics which permit
training by backpropagation in the framework of neural variational inference.
In addition, with the help of a stick-breaking construction, we propose a
recurrent network that is able to discover a notionally unbounded number of
topics, analogous to Bayesian non-parametric topic models. Experimental results
on the MXM Song Lyrics, 20NewsGroups and Reuters News datasets demonstrate the
effectiveness and efficiency of these neural topic models.
| 2,018 | Computation and Language |
Semantic Specialisation of Distributional Word Vector Spaces using
Monolingual and Cross-Lingual Constraints | We present Attract-Repel, an algorithm for improving the semantic quality of
word vectors by injecting constraints extracted from lexical resources.
Attract-Repel facilitates the use of constraints from mono- and cross-lingual
resources, yielding semantically specialised cross-lingual vector spaces. Our
evaluation shows that the method can make use of existing cross-lingual
lexicons to construct high-quality vector spaces for a plethora of different
languages, facilitating semantic transfer from high- to lower-resource ones.
The effectiveness of our approach is demonstrated with state-of-the-art results
on semantic similarity datasets in six languages. We next show that
Attract-Repel-specialised vectors boost performance in the downstream task of
dialogue state tracking (DST) across multiple languages. Finally, we show that
cross-lingual vector spaces produced by our algorithm facilitate the training
of multilingual DST models, which brings further performance improvements.
| 2,017 | Computation and Language |
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple
Language-Specific Rules | Morphologically rich languages accentuate two properties of distributional
vector space models: 1) the difficulty of inducing accurate representations for
low-frequency word forms; and 2) insensitivity to distinct lexical relations
that have similar distributional signatures. These effects are detrimental for
language understanding systems, which may infer that 'inexpensive' is a
rephrasing for 'expensive' or may not associate 'acquire' with 'acquires'. In
this work, we propose a novel morph-fitting procedure which moves past the use
of curated semantic lexicons for improving distributional vector spaces.
Instead, our method injects morphological constraints generated using simple
language-specific rules, pulling inflectional forms of the same word close
together and pushing derivational antonyms far apart. In intrinsic evaluation
over four languages, we show that our approach: 1) improves low-frequency word
estimates; and 2) boosts the semantic quality of the entire word vector
collection. Finally, we show that morph-fitted vectors yield large gains in the
downstream task of dialogue state tracking, highlighting the importance of
morphology for tackling long-tail phenomena in language understanding tasks.
| 2,017 | Computation and Language |
NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation
Systems | In this paper, we present nmtpy, a flexible Python toolkit based on Theano
for training Neural Machine Translation and other neural sequence-to-sequence
architectures. nmtpy decouples the specification of a network from the training
and inference utilities to simplify the addition of a new architecture and
reduce the amount of boilerplate code to be written. nmtpy has been used for
LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News
Translation tasks in 2016 and 2017.
| 2,018 | Computation and Language |
Machine Assisted Analysis of Vowel Length Contrasts in Wolof | Growing digital archives and improving algorithms for automatic analysis of
text and speech create new research opportunities for fundamental research in
phonetics. Such empirical approaches allow statistical evaluation of a much
larger set of hypothesis about phonetic variation and its conditioning factors
(among them geographical / dialectal variants). This paper illustrates this
vision and proposes to challenge automatic methods for the analysis of a not
easily observable phenomenon: vowel length contrast. We focus on Wolof, an
under-resourced language from Sub-Saharan Africa. In particular, we propose
multiple features to make a fine evaluation of the degree of length contrast
under different factors such as: read vs semi spontaneous speech ; standard vs
dialectal Wolof. Our measures made fully automatically on more than 20k vowel
tokens show that our proposed features can highlight different degrees of
contrast for each vowel considered. We notably show that contrast is weaker in
semi-spontaneous speech and in a non standard semi-spontaneous dialect.
| 2,017 | Computation and Language |
Function Assistant: A Tool for NL Querying of APIs | In this paper, we describe Function Assistant, a lightweight Python-based
toolkit for querying and exploring source code repositories using natural
language. The toolkit is designed to help end-users of a target API quickly
find information about functions through high-level natural language queries
and descriptions. For a given text query and background API, the tool finds
candidate functions by performing a translation from the text to known
representations in the API using the semantic parsing approach of Richardson
and Kuhn (2017). Translations are automatically learned from example text-code
pairs in example APIs. The toolkit includes features for building translation
pipelines and query engines for arbitrary source code projects. To explore this
last feature, we perform new experiments on 27 well-known Python projects
hosted on Github.
| 2,018 | Computation and Language |
Morphological Embeddings for Named Entity Recognition in Morphologically
Rich Languages | In this work, we present new state-of-the-art results of 93.59,% and 79.59,%
for Turkish and Czech named entity recognition based on the model of (Lample et
al., 2016). We contribute by proposing several schemes for representing the
morphological analysis of a word in the context of named entity recognition. We
show that a concatenation of this representation with the word and character
embeddings improves the performance. The effect of these representation schemes
on the tagging performance is also investigated.
| 2,017 | Computation and Language |
Joint Modeling of Topics, Citations, and Topical Authority in Academic
Corpora | Much of scientific progress stems from previously published findings, but
searching through the vast sea of scientific publications is difficult. We
often rely on metrics of scholarly authority to find the prominent authors but
these authority indices do not differentiate authority based on research
topics. We present Latent Topical-Authority Indexing (LTAI) for jointly
modeling the topics, citations, and topical authority in a corpus of academic
papers. Compared to previous models, LTAI differs in two main aspects. First,
it explicitly models the generative process of the citations, rather than
treating the citations as given. Second, it models each author's influence on
citations of a paper based on the topics of the cited papers, as well as the
citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS,
and Citeseer. We compare the performance of LTAI against various baselines,
starting with the latent Dirichlet allocation, to the more advanced models
including author-link topic model and dynamic author citation topic model. The
results show that LTAI achieves improved accuracy over other similar models
when predicting words, citations and authors of publications.
| 2,017 | Computation and Language |
Attentive Convolutional Neural Network based Speech Emotion Recognition:
A Study on the Impact of Input Features, Signal Length, and Acted Speech | Speech emotion recognition is an important and challenging task in the realm
of human-computer interaction. Prior work proposed a variety of models and
feature sets for training a system. In this work, we conduct extensive
experiments using an attentive convolutional neural network with multi-view
learning objective function. We compare system performance using different
lengths of the input signal, different types of acoustic features and different
types of emotion speech (improvised/scripted). Our experimental results on the
Interactive Emotional Motion Capture (IEMOCAP) database reveal that the
recognition performance strongly depends on the type of speech data independent
of the choice of input features. Furthermore, we achieved state-of-the-art
results on the improvised speech data of IEMOCAP.
| 2,017 | Computation and Language |
Prosodic Event Recognition using Convolutional Neural Networks with
Context Information | This paper demonstrates the potential of convolutional neural networks (CNN)
for detecting and classifying prosodic events on words, specifically pitch
accents and phrase boundary tones, from frame-based acoustic features. Typical
approaches use not only feature representations of the word in question but
also its surrounding context. We show that adding position features indicating
the current word benefits the CNN. In addition, this paper discusses the
generalization from a speaker-dependent modelling approach to a
speaker-independent setup. The proposed method is simple and efficient and
yields strong results not only in speaker-dependent but also
speaker-independent cases.
| 2,017 | Computation and Language |
Task-specific Word Identification from Short Texts Using a Convolutional
Neural Network | Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
However, we often have a set of labeled short texts where each short text has a
task-related class label, e.g., discriminatory or non-discriminatory, specified
by users or learned by classification algorithms. In this paper, we focus on
identifying task-specific words and phrases from short texts by exploiting
their class labels rather than using seed words or lexical dictionaries. We
consider the task-specific word and phrase identification as feature learning.
We train a convolutional neural network over a set of labeled texts and use
score vectors to localize the task-specific words and phrases. Experimental
results on sentiment word identification show that our approach significantly
outperforms existing methods. We further conduct two case studies to show the
effectiveness of our approach. One case study on a crawled tweets dataset
demonstrates that our approach can successfully capture the
discrimination-related words/phrases. The other case study on fake review
detection shows that our approach can identify the fake-review words/phrases.
| 2,017 | Computation and Language |
Concept Transfer Learning for Adaptive Language Understanding | Concept definition is important in language understanding (LU) adaptation
since literal definition difference can easily lead to data sparsity even if
different data sets are actually semantically correlated. To address this
issue, in this paper, a novel concept transfer learning approach is proposed.
Here, substructures within literal concept definition are investigated to
reveal the relationship between concepts. A hierarchical semantic
representation for concepts is proposed, where a semantic slot is represented
as a composition of {\em atomic concepts}. Based on this new hierarchical
representation, transfer learning approaches are developed for adaptive LU. The
approaches are applied to two tasks: value set mismatch and domain adaptation,
and evaluated on two LU benchmarks: ATIS and DSTC 2\&3. Thorough empirical
studies validate both the efficiency and effectiveness of the proposed method.
In particular, we achieve state-of-the-art performance ($F_1$-score 96.08\%) on
ATIS by only using lexicon features.
| 2,019 | Computation and Language |
CRNN: A Joint Neural Network for Redundancy Detection | This paper proposes a novel framework for detecting redundancy in supervised
sentence categorisation. Unlike traditional singleton neural network, our model
incorporates character-aware convolutional neural network (Char-CNN) with
character-aware recurrent neural network (Char-RNN) to form a convolutional
recurrent neural network (CRNN). Our model benefits from Char-CNN in that only
salient features are selected and fed into the integrated Char-RNN. Char-RNN
effectively learns long sequence semantics via sophisticated update mechanism.
We compare our framework against the state-of-the-art text classification
algorithms on four popular benchmarking corpus. For instance, our model
achieves competing precision rate, recall ratio, and F1 score on the
Google-news data-set. For twenty-news-groups data stream, our algorithm obtains
the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus,
our framework obtains the best F1 score and almost equivalent precision rate
and recall ratio over the top competitor. For the question classification
collection, CRNN produces the optimal recall rate and F1 score and comparable
precision rate. We also analyse three different RNN hidden recurrent cells'
impact on performance and their runtime efficiency. We observe that MGU
achieves the optimal runtime and comparable performance against GRU and LSTM.
For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec
embeddings and report their performance differences.
| 2,017 | Computation and Language |
One-step and Two-step Classification for Abusive Language Detection on
Twitter | Automatic abusive language detection is a difficult but important task for
online social media. Our research explores a two-step approach of performing
classification on abusive language and then classifying into specific types and
compares it with one-step approach of doing one multi-class classification for
detecting sexist and racist languages. With a public English Twitter corpus of
20 thousand tweets in the type of sexism and racism, our approach shows a
promising performance of 0.827 F-measure by using HybridCNN in one-step and
0.824 F-measure by using logistic regression in two-steps.
| 2,017 | Computation and Language |
Deep learning evaluation using deep linguistic processing | We discuss problems with the standard approaches to evaluation for tasks like
visual question answering, and argue that artificial data can be used to
address these as a complement to current practice. We demonstrate that with the
help of existing 'deep' linguistic processing technology we are able to create
challenging abstract datasets, which enable us to investigate the language
understanding abilities of multimodal deep learning models in detail, as
compared to a single performance value on a static and monolithic dataset.
| 2,018 | Computation and Language |
Event Representations for Automated Story Generation with Deep Neural
Nets | Automated story generation is the problem of automatically selecting a
sequence of events, actions, or words that can be told as a story. We seek to
develop a system that can generate stories by learning everything it needs to
know from textual story corpora. To date, recurrent neural networks that learn
language models at character, word, or sentence levels have had little success
generating coherent stories. We explore the question of event representations
that provide a mid-level of abstraction between words and sentences in order to
retain the semantic information of the original data while minimizing event
sparsity. We present a technique for preprocessing textual story data into
event sequences. We then present a technique for automated story generation
whereby we decompose the problem into the generation of successive events
(event2event) and the generation of natural language sentences from events
(event2sentence). We give empirical results comparing different event
representations and their effects on event successor generation and the
translation of events to natural language.
| 2,018 | Computation and Language |
Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor | Using supporting backchannel (BC) cues can make human-computer interaction
more social. BCs provide a feedback from the listener to the speaker indicating
to the speaker that he is still listened to. BCs can be expressed in different
ways, depending on the modality of the interaction, for example as gestures or
acoustic cues. In this work, we only considered acoustic cues. We are proposing
an approach towards detecting BC opportunities based on acoustic input features
like power and pitch. While other works in the field rely on the use of a
hand-written rule set or specialized features, we made use of artificial neural
networks. They are capable of deriving higher order features from input
features themselves. In our setup, we first used a fully connected feed-forward
network to establish an updated baseline in comparison to our previously
proposed setup. We also extended this setup by the use of Long Short-Term
Memory (LSTM) networks which have shown to outperform feed-forward based setups
on various tasks. Our best system achieved an F1-Score of 0.37 using power and
pitch features. Adding linguistic information using word2vec, the score
increased to 0.39.
| 2,017 | Computation and Language |
Language Generation with Recurrent Generative Adversarial Networks
without Pre-training | Generative Adversarial Networks (GANs) have shown great promise recently in
image generation. Training GANs for language generation has proven to be more
difficult, because of the non-differentiable nature of generating text with
recurrent neural networks. Consequently, past work has either resorted to
pre-training with maximum-likelihood or used convolutional networks for
generation. In this work, we show that recurrent neural networks can be trained
to generate text with GANs from scratch using curriculum learning, by slowly
teaching the model to generate sequences of increasing and variable length. We
empirically show that our approach vastly improves the quality of generated
sequences compared to a convolutional baseline.
| 2,017 | Computation and Language |
A simple neural network module for relational reasoning | Relational reasoning is a central component of generally intelligent
behavior, but has proven difficult for neural networks to learn. In this paper
we describe how to use Relation Networks (RNs) as a simple plug-and-play module
to solve problems that fundamentally hinge on relational reasoning. We tested
RN-augmented networks on three tasks: visual question answering using a
challenging dataset called CLEVR, on which we achieve state-of-the-art,
super-human performance; text-based question answering using the bAbI suite of
tasks; and complex reasoning about dynamic physical systems. Then, using a
curated dataset called Sort-of-CLEVR we show that powerful convolutional
networks do not have a general capacity to solve relational questions, but can
gain this capacity when augmented with RNs. Our work shows how a deep learning
architecture equipped with an RN module can implicitly discover and learn to
reason about entities and their relations.
| 2,017 | Computation and Language |
A Joint Model for Question Answering and Question Generation | We propose a generative machine comprehension model that learns jointly to
ask and answer questions based on documents. The proposed model uses a
sequence-to-sequence framework that encodes the document and generates a
question (answer) given an answer (question). Significant improvement in model
performance is observed empirically on the SQuAD corpus, confirming our
hypothesis that the model benefits from jointly learning to perform both tasks.
We believe the joint model's novelty offers a new perspective on machine
comprehension beyond architectural engineering, and serves as a first step
towards autonomous information seeking.
| 2,017 | Computation and Language |
Deep learning for extracting protein-protein interactions from
biomedical literature | State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-based convolutional neural network model (McDepCNN). It applies one
channel to the embedding vector of each word in the sentence, and another
channel to the embedding vector of the head of the corresponding word.
Therefore, the model can use richer information obtained from different
channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
demonstrate that McDepCNN compares favorably to the state-of-the-art
rich-feature and single-kernel based methods. In addition, McDepCNN achieves
24.4% relative improvement in F1-score over the state-of-the-art methods on
cross-corpus evaluation and 12% improvement in F1-score over kernel-based
methods on "difficult" instances. These results suggest that McDepCNN
generalizes more easily over different corpora, and is capable of capturing
long distance features in the sentences.
| 2,017 | Computation and Language |
Acquisition of Translation Lexicons for Historically Unwritten Languages
via Bridging Loanwords | With the advent of informal electronic communications such as social media,
colloquial languages that were historically unwritten are being written for the
first time in heavily code-switched environments. We present a method for
inducing portions of translation lexicons through the use of expert knowledge
in these settings where there are approximately zero resources available other
than a language informant, potentially not even large amounts of monolingual
data. We investigate inducing a Moroccan Darija-English translation lexicon via
French loanwords bridging into English and find that a useful lexicon is
induced for human-assisted translation and statistical machine translation.
| 2,017 | Computation and Language |
Text Summarization using Abstract Meaning Representation | With an ever increasing size of text present on the Internet, automatic
summary generation remains an important problem for natural language
understanding. In this work we explore a novel full-fledged pipeline for text
summarization with an intermediate step of Abstract Meaning Representation
(AMR). The pipeline proposed by us first generates an AMR graph of an input
story, through which it extracts a summary graph and finally, generate summary
sentences from this summary graph. Our proposed method achieves
state-of-the-art results compared to the other text summarization routines
based on AMR. We also point out some significant problems in the existing
evaluation methods, which make them unsuitable for evaluating summary quality.
| 2,017 | Computation and Language |
A Frame Tracking Model for Memory-Enhanced Dialogue Systems | Recently, resources and tasks were proposed to go beyond state tracking in
dialogue systems. An example is the frame tracking task, which requires
recording multiple frames, one for each user goal set during the dialogue. This
allows a user, for instance, to compare items corresponding to different goals.
This paper proposes a model which takes as input the list of frames created so
far during the dialogue, the current user utterance as well as the dialogue
acts, slot types, and slot values associated with this utterance. The model
then outputs the frame being referenced by each triple of dialogue act, slot
type, and slot value. We show that on the recently published Frames dataset,
this model significantly outperforms a previously proposed rule-based baseline.
In addition, we propose an extensive analysis of the frame tracking task by
dividing it into sub-tasks and assessing their difficulty with respect to our
model.
| 2,017 | Computation and Language |
A General-Purpose Tagger with Convolutional Neural Networks | We present a general-purpose tagger based on convolutional neural networks
(CNN), used for both composing word vectors and encoding context information.
The CNN tagger is robust across different tagging tasks: without task-specific
tuning of hyper-parameters, it achieves state-of-the-art results in
part-of-speech tagging, morphological tagging and supertagging. The CNN tagger
is also robust against the out-of-vocabulary problem, it performs well on
artificially unnormalized texts.
| 2,017 | Computation and Language |
Label-Dependencies Aware Recurrent Neural Networks | In the last few years, Recurrent Neural Networks (RNNs) have proved effective
on several NLP tasks. Despite such great success, their ability to model
\emph{sequence labeling} is still limited. This lead research toward solutions
where RNNs are combined with models which already proved effective in this
domain, such as CRFs. In this work we propose a solution far simpler but very
effective: an evolution of the simple Jordan RNN, where labels are re-injected
as input into the network, and converted into embeddings, in the same way as
words. We compare this RNN variant to all the other RNN models, Elman and
Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language
Understanding (SLU). Thanks to label embeddings and their combination at the
hidden layer, the proposed variant, which uses more parameters than Elman and
Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other
RNNs, but also outperforms sophisticated CRF models.
| 2,017 | Computation and Language |
A WL-SPPIM Semantic Model for Document Classification | In this paper, we explore SPPIM-based text classification method, and the
experiment reveals that the SPPIM method is equal to or even superior than SGNS
method in text classification task on three international and standard text
datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although
SPPMI provides a better solution, it is not necessarily better than SGNS in
text classification tasks. Based on our analysis, SGNS takes into the
consideration of weight calculation during decomposition process, so it has
better performance than SPPIM in some standard datasets. Inspired by this, we
propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows
that WL-SPPIM approach has better classification and higher scalability in the
text classification task compared with LDA, SGNS and SPPIM approaches.
| 2,017 | Computation and Language |
Assessing the Linguistic Productivity of Unsupervised Deep Neural
Networks | Increasingly, cognitive scientists have demonstrated interest in applying
tools from deep learning. One use for deep learning is in language acquisition
where it is useful to know if a linguistic phenomenon can be learned through
domain-general means. To assess whether unsupervised deep learning is
appropriate, we first pose a smaller question: Can unsupervised neural networks
apply linguistic rules productively, using them in novel situations? We draw
from the literature on determiner/noun productivity by training an
unsupervised, autoencoder network measuring its ability to combine nouns with
determiners. Our simple autoencoder creates combinations it has not previously
encountered and produces a degree of overlap matching adults. While this
preliminary work does not provide conclusive evidence for productivity, it
warrants further investigation with more complex models. Further, this work
helps lay the foundations for future collaboration between the deep learning
and cognitive science communities.
| 2,017 | Computation and Language |
Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext | We consider the problem of learning general-purpose, paraphrastic sentence
embeddings in the setting of Wieting et al. (2016b). We use neural machine
translation to generate sentential paraphrases via back-translation of
bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to
serve as training data for learning paraphrastic sentence embeddings. We find
that the data quality is stronger than prior work based on bitext and on par
with manually-written English paraphrase pairs, with the advantage that our
approach can scale up to generate large training sets for many languages and
domains. We experiment with several language pairs and data sources, and
develop a variety of data filtering techniques. In the process, we explore how
neural machine translation output differs from human-written sentences, finding
clear differences in length, the amount of repetition, and the use of rare
words.
| 2,017 | Computation and Language |
Marmara Turkish Coreference Corpus and Coreference Resolution Baseline | We describe the Marmara Turkish Coreference Corpus, which is an annotation of
the whole METU-Sabanci Turkish Treebank with mentions and coreference chains.
Collecting eight or more independent annotations for each document allowed for
fully automatic adjudication. We provide a baseline system for Turkish mention
detection and coreference resolution and evaluate it on the corpus.
| 2,018 | Computation and Language |
Measuring Offensive Speech in Online Political Discourse | The Internet and online forums such as Reddit have become an increasingly
popular medium for citizens to engage in political conversations. However, the
online disinhibition effect resulting from the ability to use pseudonymous
identities may manifest in the form of offensive speech, consequently making
political discussions more aggressive and polarizing than they already are.
Such environments may result in harassment and self-censorship from its
targets. In this paper, we present preliminary results from a large-scale
temporal measurement aimed at quantifying offensiveness in online political
discussions.
To enable our measurements, we develop and evaluate an offensive speech
classifier. We then use this classifier to quantify and compare offensiveness
in the political and general contexts. We perform our study using a database of
over 168M Reddit comments made by over 7M pseudonyms between January 2015 and
January 2017 -- a period covering several divisive political events including
the 2016 US presidential elections.
| 2,017 | Computation and Language |
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic
Similarity | Semantic similarity measures are an important part in Natural Language
Processing tasks. However Semantic similarity measures built for general use do
not perform well within specific domains. Therefore in this study we introduce
a domain specific semantic similarity measure that was created by the
synergistic union of word2vec, a word embedding method that is used for
semantic similarity calculation and lexicon based (lexical) semantic similarity
methods. We prove that this proposed methodology out performs word embedding
methods trained on generic corpus and methods trained on domain specific corpus
but do not use lexical semantic similarity methods to augment the results.
Further, we prove that text lemmatization can improve the performance of word
embedding methods.
| 2,019 | Computation and Language |
Question Answering and Question Generation as Dual Tasks | We study the problem of joint question answering (QA) and question generation
(QG) in this paper.
Our intuition is that QA and QG have intrinsic connections and these two
tasks could improve each other.
On one side, the QA model judges whether the generated question of a QG model
is relevant to the answer.
On the other side, the QG model provides the probability of generating a
question given the answer, which is a useful evidence that in turn facilitates
QA.
In this paper we regard QA and QG as dual tasks.
We propose a training framework that trains the models of QA and QG
simultaneously, and explicitly leverages their probabilistic correlation to
guide the training process of both models.
We implement a QG model based on sequence-to-sequence learning, and a QA
model based on recurrent neural network.
As all the components of the QA and QG models are differentiable, all the
parameters involved in these two models could be conventionally learned with
back propagation.
We conduct experiments on three datasets. Empirical results show that our
training framework improves both QA and QG tasks.
The improved QA model performs comparably with strong baseline approaches on
all three datasets.
| 2,017 | Computation and Language |
Macquarie University at BioASQ 5b -- Query-based Summarisation
Techniques for Selecting the Ideal Answers | Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B)
focused on the use of query-based extractive summarisation techniques for the
generation of the ideal answers. Four runs were submitted, with approaches
ranging from a trivial system that selected the first $n$ snippets, to the use
of deep learning approaches under a regression framework. Our experiments and
the ROUGE results of the five test batches of BioASQ indicate surprisingly good
results for the trivial approach. Overall, most of our runs on the first three
test batches achieved the best ROUGE-SU4 results in the challenge.
| 2,017 | Computation and Language |
Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks | Ladder networks are a notable new concept in the field of semi-supervised
learning by showing state-of-the-art results in image recognition tasks while
being compatible with many existing neural architectures. We present the
recurrent ladder network, a novel modification of the ladder network, for
semi-supervised learning of recurrent neural networks which we evaluate with a
phoneme recognition task on the TIMIT corpus. Our results show that the model
is able to consistently outperform the baseline and achieve fully-supervised
baseline performance with only 75% of all labels which demonstrates that the
model is capable of using unsupervised data as an effective regulariser.
| 2,017 | Computation and Language |
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on
Rule-Based Sentiment Analysis | Syntactic parsing, the process of obtaining the internal structure of
sentences in natural languages, is a crucial task for artificial intelligence
applications that need to extract meaning from natural language text or speech.
Sentiment analysis is one example of application for which parsing has recently
proven useful.
In recent years, there have been significant advances in the accuracy of
parsing algorithms. In this article, we perform an empirical, task-oriented
evaluation to determine how parsing accuracy influences the performance of a
state-of-the-art rule-based sentiment analysis system that determines the
polarity of sentences from their parse trees. In particular, we evaluate the
system using four well-known dependency parsers, including both current models
with state-of-the-art accuracy and more innacurate models which, however,
require less computational resources.
The experiments show that all of the parsers produce similarly good results
in the sentiment analysis task, without their accuracy having any relevant
influence on the results. Since parsing is currently a task with a relatively
high computational cost that varies strongly between algorithms, this suggests
that sentiment analysis researchers and users should prioritize speed over
accuracy when choosing a parser; and parsing researchers should investigate
models that improve speed further, even at some cost to accuracy.
| 2,017 | Computation and Language |
Insights into Analogy Completion from the Biomedical Domain | Analogy completion has been a popular task in recent years for evaluating the
semantic properties of word embeddings, but the standard methodology makes a
number of assumptions about analogies that do not always hold, either in recent
benchmark datasets or when expanding into other domains. Through an analysis of
analogies in the biomedical domain, we identify three assumptions: that of a
Single Answer for any given analogy, that the pairs involved describe the Same
Relationship, and that each pair is Informative with respect to the other. We
propose modifying the standard methodology to relax these assumptions by
allowing for multiple correct answers, reporting MAP and MRR in addition to
accuracy, and using multiple example pairs. We further present BMASS, a novel
dataset for evaluating linguistic regularities in biomedical embeddings, and
demonstrate that the relationships described in the dataset pose significant
semantic challenges to current word embedding methods.
| 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.