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Not All Dialogues are Created Equal: Instance Weighting for Neural
Conversational Models | Neural conversational models require substantial amounts of dialogue data for
their parameter estimation and are therefore usually learned on large corpora
such as chat forums or movie subtitles. These corpora are, however, often
challenging to work with, notably due to their frequent lack of turn
segmentation and the presence of multiple references external to the dialogue
itself. This paper shows that these challenges can be mitigated by adding a
weighting model into the architecture. The weighting model, which is itself
estimated from dialogue data, associates each training example to a numerical
weight that reflects its intrinsic quality for dialogue modelling. At training
time, these sample weights are included into the empirical loss to be
minimised. Evaluation results on retrieval-based models trained on movie and TV
subtitles demonstrate that the inclusion of such a weighting model improves the
model performance on unsupervised metrics.
| 2,017 | Computation and Language |
Understanding and Detecting Supporting Arguments of Diverse Types | We investigate the problem of sentence-level supporting argument detection
from relevant documents for user-specified claims. A dataset containing claims
and associated citation articles is collected from online debate website
idebate.org. We then manually label sentence-level supporting arguments from
the documents along with their types as study, factual, opinion, or reasoning.
We further characterize arguments of different types, and explore whether
leveraging type information can facilitate the supporting arguments detection
task. Experimental results show that LambdaMART (Burges, 2010) ranker that uses
features informed by argument types yields better performance than the same
ranker trained without type information.
| 2,017 | Computation and Language |
Learning to Ask: Neural Question Generation for Reading Comprehension | We study automatic question generation for sentences from text passages in
reading comprehension. We introduce an attention-based sequence learning model
for the task and investigate the effect of encoding sentence- vs.
paragraph-level information. In contrast to all previous work, our model does
not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead
trainable end-to-end via sequence-to-sequence learning. Automatic evaluation
results show that our system significantly outperforms the state-of-the-art
rule-based system. In human evaluations, questions generated by our system are
also rated as being more natural (i.e., grammaticality, fluency) and as more
difficult to answer (in terms of syntactic and lexical divergence from the
original text and reasoning needed to answer).
| 2,017 | Computation and Language |
Semi-supervised sequence tagging with bidirectional language models | Pre-trained word embeddings learned from unlabeled text have become a
standard component of neural network architectures for NLP tasks. However, in
most cases, the recurrent network that operates on word-level representations
to produce context sensitive representations is trained on relatively little
labeled data. In this paper, we demonstrate a general semi-supervised approach
for adding pre- trained context embeddings from bidirectional language models
to NLP systems and apply it to sequence labeling tasks. We evaluate our model
on two standard datasets for named entity recognition (NER) and chunking, and
in both cases achieve state of the art results, surpassing previous systems
that use other forms of transfer or joint learning with additional labeled data
and task specific gazetteers.
| 2,017 | Computation and Language |
Extending and Improving Wordnet via Unsupervised Word Embeddings | This work presents an unsupervised approach for improving WordNet that builds
upon recent advances in document and sense representation via distributional
semantics. We apply our methods to construct Wordnets in French and Russian,
languages which both lack good manual constructions.1 These are evaluated on
two new 600-word test sets for word-to-synset matching and found to improve
greatly upon synset recall, outperforming the best automated Wordnets in
F-score. Our methods require very few linguistic resources, thus being
applicable for Wordnet construction in low-resources languages, and may further
be applied to sense clustering and other Wordnet improvements.
| 2,017 | Computation and Language |
Lifelong Learning CRF for Supervised Aspect Extraction | This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.
| 2,017 | Computation and Language |
A Conditional Variational Framework for Dialog Generation | Deep latent variable models have been shown to facilitate the response
generation for open-domain dialog systems. However, these latent variables are
highly randomized, leading to uncontrollable generated responses. In this
paper, we propose a framework allowing conditional response generation based on
specific attributes. These attributes can be either manually assigned or
automatically detected. Moreover, the dialog states for both speakers are
modeled separately in order to reflect personal features. We validate this
framework on two different scenarios, where the attribute refers to genericness
and sentiment states respectively. The experiment result testified the
potential of our model, where meaningful responses can be generated in
accordance with the specified attributes.
| 2,017 | Computation and Language |
Quantifying Mental Health from Social Media with Neural User Embeddings | Mental illnesses adversely affect a significant proportion of the population
worldwide. However, the methods traditionally used for estimating and
characterizing the prevalence of mental health conditions are time-consuming
and expensive. Consequently, best-available estimates concerning the prevalence
of mental health conditions are often years out of date. Automated approaches
to supplement these survey methods with broad, aggregated information derived
from social media content provides a potential means for near real-time
estimates at scale. These may, in turn, provide grist for supporting,
evaluating and iteratively improving upon public health programs and
interventions.
We propose a novel model for automated mental health status quantification
that incorporates user embeddings. This builds upon recent work exploring
representation learning methods that induce embeddings by leveraging social
media post histories. Such embeddings capture latent characteristics of
individuals (e.g., political leanings) and encode a soft notion of homophily.
In this paper, we investigate whether user embeddings learned from twitter post
histories encode information that correlates with mental health statuses. To
this end, we estimated user embeddings for a set of users known to be affected
by depression and post-traumatic stress disorder (PTSD), and for a set of
demographically matched `control' users. We then evaluated these embeddings
with respect to: (i) their ability to capture homophilic relations with respect
to mental health status; and (ii) the performance of downstream mental health
prediction models based on these features. Our experimental results demonstrate
that the user embeddings capture similarities between users with respect to
mental conditions, and are predictive of mental health.
| 2,017 | Computation and Language |
Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings | We consider the problem of learning general-purpose, paraphrastic sentence
embeddings, revisiting the setting of Wieting et al. (2016b). While they found
LSTM recurrent networks to underperform word averaging, we present several
developments that together produce the opposite conclusion. These include
training on sentence pairs rather than phrase pairs, averaging states to
represent sequences, and regularizing aggressively. These improve LSTMs in both
transfer learning and supervised settings. We also introduce a new recurrent
architecture, the Gated Recurrent Averaging Network, that is inspired by
averaging and LSTMs while outperforming them both. We analyze our learned
models, finding evidence of preferences for particular parts of speech and
dependency relations.
| 2,017 | Computation and Language |
Duluth at SemEval--2016 Task 14 : Extending Gloss Overlaps to Enrich
Semantic Taxonomies | This paper describes the Duluth systems that participated in Task 14 of
SemEval 2016, Semantic Taxonomy Enrichment. There were three related systems in
the formal evaluation which are discussed here, along with numerous
post--evaluation runs. All of these systems identified synonyms between WordNet
and other dictionaries by measuring the gloss overlaps between them. These
systems perform better than the random baseline and one post--evaluation
variation was within a respectable margin of the median result attained by all
participating systems.
| 2,017 | Computation and Language |
Dependency Parsing with Dilated Iterated Graph CNNs | Dependency parses are an effective way to inject linguistic knowledge into
many downstream tasks, and many practitioners wish to efficiently parse
sentences at scale. Recent advances in GPU hardware have enabled neural
networks to achieve significant gains over the previous best models, these
models still fail to leverage GPUs' capability for massive parallelism due to
their requirement of sequential processing of the sentence. In response, we
propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for
graph-based dependency parsing, a graph convolutional architecture that allows
for efficient end-to-end GPU parsing. In experiments on the English Penn
TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best
neural network parsers.
| 2,017 | Computation and Language |
Model Transfer for Tagging Low-resource Languages using a Bilingual
Dictionary | Cross-lingual model transfer is a compelling and popular method for
predicting annotations in a low-resource language, whereby parallel corpora
provide a bridge to a high-resource language and its associated annotated
corpora. However, parallel data is not readily available for many languages,
limiting the applicability of these approaches. We address these drawbacks in
our framework which takes advantage of cross-lingual word embeddings trained
solely on a high coverage bilingual dictionary. We propose a novel neural
network model for joint training from both sources of data based on
cross-lingual word embeddings, and show substantial empirical improvements over
baseline techniques. We also propose several active learning heuristics, which
result in improvements over competitive benchmark methods.
| 2,017 | Computation and Language |
Data Augmentation for Low-Resource Neural Machine Translation | The quality of a Neural Machine Translation system depends substantially on
the availability of sizable parallel corpora. For low-resource language pairs
this is not the case, resulting in poor translation quality. Inspired by work
in computer vision, we propose a novel data augmentation approach that targets
low-frequency words by generating new sentence pairs containing rare words in
new, synthetically created contexts. Experimental results on simulated
low-resource settings show that our method improves translation quality by up
to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.
| 2,018 | Computation and Language |
Learning Topic-Sensitive Word Representations | Distributed word representations are widely used for modeling words in NLP
tasks. Most of the existing models generate one representation per word and do
not consider different meanings of a word. We present two approaches to learn
multiple topic-sensitive representations per word by using Hierarchical
Dirichlet Process. We observe that by modeling topics and integrating topic
distributions for each document we obtain representations that are able to
distinguish between different meanings of a given word. Our models yield
statistically significant improvements for the lexical substitution task
indicating that commonly used single word representations, even when combined
with contextual information, are insufficient for this task.
| 2,018 | Computation and Language |
Speech-Based Visual Question Answering | This paper introduces speech-based visual question answering (VQA), the task
of generating an answer given an image and a spoken question. Two methods are
studied: an end-to-end, deep neural network that directly uses audio waveforms
as input versus a pipelined approach that performs ASR (Automatic Speech
Recognition) on the question, followed by text-based visual question answering.
Furthermore, we investigate the robustness of both methods by injecting various
levels of noise into the spoken question and find both methods to be tolerate
noise at similar levels.
| 2,017 | Computation and Language |
Labelled network subgraphs reveal stylistic subtleties in written texts | The vast amount of data and increase of computational capacity have allowed
the analysis of texts from several perspectives, including the representation
of texts as complex networks. Nodes of the network represent the words, and
edges represent some relationship, usually word co-occurrence. Even though
networked representations have been applied to study some tasks, such
approaches are not usually combined with traditional models relying upon
statistical paradigms. Because networked models are able to grasp textual
patterns, we devised a hybrid classifier, called labelled subgraphs, that
combines the frequency of common words with small structures found in the
topology of the network, known as motifs. Our approach is illustrated in two
contexts, authorship attribution and translationese identification. In the
former, a set of novels written by different authors is analyzed. To identify
translationese, texts from the Canadian Hansard and the European parliament
were classified as to original and translated instances. Our results suggest
that labelled subgraphs are able to represent texts and it should be further
explored in other tasks, such as the analysis of text complexity, language
proficiency, and machine translation.
| 2,017 | Computation and Language |
Discourse-Based Objectives for Fast Unsupervised Sentence Representation
Learning | This work presents a novel objective function for the unsupervised training
of neural network sentence encoders. It exploits signals from paragraph-level
discourse coherence to train these models to understand text. Our objective is
purely discriminative, allowing us to train models many times faster than was
possible under prior methods, and it yields models which perform well in
extrinsic evaluations.
| 2,017 | Computation and Language |
Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter:
predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to
predict the sentiment of financial news headlines for a specific company on a
continuous scale between -1 and 1. We tackled the problem using a number of
approaches, utilising a Support Vector Regression (SVR) and a Bidirectional
Long Short-Term Memory (BLSTM). We found an improvement of 4-6% using the LSTM
model over the SVR and came fourth in the track. We report a number of
different evaluations using a finance specific word embedding model and reflect
on the effects of using different evaluation metrics.
| 2,018 | Computation and Language |
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News
Detection | Automatic fake news detection is a challenging problem in deception
detection, and it has tremendous real-world political and social impacts.
However, statistical approaches to combating fake news has been dramatically
limited by the lack of labeled benchmark datasets. In this paper, we present
liar: a new, publicly available dataset for fake news detection. We collected a
decade-long, 12.8K manually labeled short statements in various contexts from
PolitiFact.com, which provides detailed analysis report and links to source
documents for each case. This dataset can be used for fact-checking research as
well. Notably, this new dataset is an order of magnitude larger than previously
largest public fake news datasets of similar type. Empirically, we investigate
automatic fake news detection based on surface-level linguistic patterns. We
have designed a novel, hybrid convolutional neural network to integrate
meta-data with text. We show that this hybrid approach can improve a text-only
deep learning model.
| 2,017 | Computation and Language |
Efficient Natural Language Response Suggestion for Smart Reply | This paper presents a computationally efficient machine-learned method for
natural language response suggestion. Feed-forward neural networks using n-gram
embedding features encode messages into vectors which are optimized to give
message-response pairs a high dot-product value. An optimized search finds
response suggestions. The method is evaluated in a large-scale commercial
e-mail application, Inbox by Gmail. Compared to a sequence-to-sequence
approach, the new system achieves the same quality at a small fraction of the
computational requirements and latency.
| 2,017 | Computation and Language |
From Imitation to Prediction, Data Compression vs Recurrent Neural
Networks for Natural Language Processing | In recent studies [1][13][12] Recurrent Neural Networks were used for
generative processes and their surprising performance can be explained by their
ability to create good predictions. In addition, data compression is also based
on predictions. What the problem comes down to is whether a data compressor
could be used to perform as well as recurrent neural networks in natural
language processing tasks. If this is possible,then the problem comes down to
determining if a compression algorithm is even more intelligent than a neural
network in specific tasks related to human language. In our journey we
discovered what we think is the fundamental difference between a Data
Compression Algorithm and a Recurrent Neural Network.
| 2,017 | Computation and Language |
Chat Detection in an Intelligent Assistant: Combining Task-oriented and
Non-task-oriented Spoken Dialogue Systems | Recently emerged intelligent assistants on smartphones and home electronics
(e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific
task-oriented spoken dialogue systems and open-domain non-task-oriented ones.
To realize such hybrid dialogue systems, this paper investigates determining
whether or not a user is going to have a chat with the system. To address the
lack of benchmark datasets for this task, we construct a new dataset consisting
of 15; 160 utterances collected from the real log data of a commercial
intelligent assistant (and will release the dataset to facilitate future
research activity). In addition, we investigate using tweets and Web search
queries for handling open-domain user utterances, which characterize the task
of chat detection. Experiments demonstrated that, while simple supervised
methods are effective, the use of the tweets and search queries further
improves the F1-score from 86.21 to 87.53.
| 2,018 | Computation and Language |
A Teacher-Student Framework for Zero-Resource Neural Machine Translation | While end-to-end neural machine translation (NMT) has made remarkable
progress recently, it still suffers from the data scarcity problem for
low-resource language pairs and domains. In this paper, we propose a method for
zero-resource NMT by assuming that parallel sentences have close probabilities
of generating a sentence in a third language. Based on this assumption, our
method is able to train a source-to-target NMT model ("student") without
parallel corpora available, guided by an existing pivot-to-target NMT model
("teacher") on a source-pivot parallel corpus. Experimental results show that
the proposed method significantly improves over a baseline pivot-based model by
+3.0 BLEU points across various language pairs.
| 2,017 | Computation and Language |
STAIR Captions: Constructing a Large-Scale Japanese Image Caption
Dataset | In recent years, automatic generation of image descriptions (captions), that
is, image captioning, has attracted a great deal of attention. In this paper,
we particularly consider generating Japanese captions for images. Since most
available caption datasets have been constructed for English language, there
are few datasets for Japanese. To tackle this problem, we construct a
large-scale Japanese image caption dataset based on images from MS-COCO, which
is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions
for 164,062 images. In the experiment, we show that a neural network trained
using STAIR Captions can generate more natural and better Japanese captions,
compared to those generated using English-Japanese machine translation after
generating English captions.
| 2,017 | Computation and Language |
Deep Neural Machine Translation with Linear Associative Unit | Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art
Neural Machine Translation (NMT) with their capability in modeling complex
functions and capturing complex linguistic structures. However NMT systems with
deep architecture in their encoder or decoder RNNs often suffer from severe
gradient diffusion due to the non-linear recurrent activations, which often
make the optimization much more difficult. To address this problem we propose
novel linear associative units (LAU) to reduce the gradient propagation length
inside the recurrent unit. Different from conventional approaches (LSTM unit
and GRU), LAUs utilizes linear associative connections between input and output
of the recurrent unit, which allows unimpeded information flow through both
space and time direction. The model is quite simple, but it is surprisingly
effective. Our empirical study on Chinese-English translation shows that our
model with proper configuration can improve by 11.7 BLEU upon Groundhog and the
best reported results in the same setting. On WMT14 English-German task and a
larger WMT14 English-French task, our model achieves comparable results with
the state-of-the-art.
| 2,017 | Computation and Language |
Modeling Source Syntax for Neural Machine Translation | Even though a linguistics-free sequence to sequence model in neural machine
translation (NMT) has certain capability of implicitly learning syntactic
information of source sentences, this paper shows that source syntax can be
explicitly incorporated into NMT effectively to provide further improvements.
Specifically, we linearize parse trees of source sentences to obtain structural
label sequences. On the basis, we propose three different sorts of encoders to
incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word
and label annotation vectors parallelly; 2) Hierarchical RNN encoder that
learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed
RNN encoder that stitchingly learns word and label annotation vectors over
sequences where words and labels are mixed. Experimentation on
Chinese-to-English translation demonstrates that all the three proposed
syntactic encoders are able to improve translation accuracy. It is interesting
to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best
performance with an significant improvement of 1.4 BLEU points. Moreover, an
in-depth analysis from several perspectives is provided to reveal how source
syntax benefits NMT.
| 2,017 | Computation and Language |
Entity Linking with people entity on Wikipedia | This paper introduces a new model that uses named entity recognition,
coreference resolution, and entity linking techniques, to approach the task of
linking people entities on Wikipedia people pages to their corresponding
Wikipedia pages if applicable. Our task is different from general and
traditional entity linking because we are working in a limited domain, namely,
people entities, and we are including pronouns as entities, whereas in the
past, pronouns were never considered as entities in entity linking. We have
built 2 models, both outperforms our baseline model significantly. The purpose
of our project is to build a model that could be use to generate cleaner data
for future entity linking tasks. Our contribution include a clean data set
consisting of 50Wikipedia people pages, and 2 entity linking models,
specifically tuned for this domain.
| 2,017 | Computation and Language |
A Hybrid Architecture for Multi-Party Conversational Systems | Multi-party Conversational Systems are systems with natural language
interaction between one or more people or systems. From the moment that an
utterance is sent to a group, to the moment that it is replied in the group by
a member, several activities must be done by the system: utterance
understanding, information search, reasoning, among others. In this paper we
present the challenges of designing and building multi-party conversational
systems, the state of the art, our proposed hybrid architecture using both
rules and machine learning and some insights after implementing and evaluating
one on the finance domain.
| 2,017 | Computation and Language |
On the effectiveness of feature set augmentation using clusters of word
embeddings | Word clusters have been empirically shown to offer important performance
improvements on various tasks. Despite their importance, their incorporation in
the standard pipeline of feature engineering relies more on a trial-and-error
procedure where one evaluates several hyper-parameters, like the number of
clusters to be used. In order to better understand the role of such features we
systematically evaluate their effect on four tasks, those of named entity
segmentation and classification as well as, those of five-point sentiment
classification and quantification. Our results strongly suggest that cluster
membership features improve the performance.
| 2,018 | Computation and Language |
Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment
Detection on Twitter | This paper describes the Amobee sentiment analysis system, adapted to compete
in SemEval 2017 task 4. The system consists of two parts: a supervised training
of RNN models based on a Twitter sentiment treebank, and the use of feedforward
NN, Naive Bayes and logistic regression classifiers to produce predictions for
the different sub-tasks. The algorithm reached the 3rd place on the 5-label
classification task (sub-task C).
| 2,018 | Computation and Language |
Going Wider: Recurrent Neural Network With Parallel Cells | Recurrent Neural Network (RNN) has been widely applied for sequence modeling.
In RNN, the hidden states at current step are full connected to those at
previous step, thus the influence from less related features at previous step
may potentially decrease model's learning ability. We propose a simple
technique called parallel cells (PCs) to enhance the learning ability of
Recurrent Neural Network (RNN). In each layer, we run multiple small RNN cells
rather than one single large cell. In this paper, we evaluate PCs on 2 tasks.
On language modeling task on PTB (Penn Tree Bank), our model outperforms state
of art models by decreasing perplexity from 78.6 to 75.3. On Chinese-English
translation task, our model increases BLEU score for 0.39 points than baseline
model.
| 2,017 | Computation and Language |
Chunk-Based Bi-Scale Decoder for Neural Machine Translation | In typical neural machine translation~(NMT), the decoder generates a sentence
word by word, packing all linguistic granularities in the same time-scale of
RNN. In this paper, we propose a new type of decoder for NMT, which splits the
decode state into two parts and updates them in two different time-scales.
Specifically, we first predict a chunk time-scale state for phrasal modeling,
on top of which multiple word time-scale states are generated. In this way, the
target sentence is translated hierarchically from chunks to words, with
information in different granularities being leveraged. Experiments show that
our proposed model significantly improves the translation performance over the
state-of-the-art NMT model.
| 2,017 | Computation and Language |
Probabilistic Typology: Deep Generative Models of Vowel Inventories | Linguistic typology studies the range of structures present in human
language. The main goal of the field is to discover which sets of possible
phenomena are universal, and which are merely frequent. For example, all
languages have vowels, while most---but not all---languages have an /u/ sound.
In this paper we present the first probabilistic treatment of a basic question
in phonological typology: What makes a natural vowel inventory? We introduce a
series of deep stochastic point processes, and contrast them with previous
computational, simulation-based approaches. We provide a comprehensive suite of
experiments on over 200 distinct languages.
| 2,017 | Computation and Language |
A Finite State and Rule-based Akshara to Prosodeme (A2P) Converter in
Hindi | This article describes a software module called Akshara to Prosodeme (A2P)
converter in Hindi. It converts an input grapheme into prosedeme (sequence of
phonemes with the specification of syllable boundaries and prosodic labels).
The software is based on two proposed finite state machines\textemdash one for
the syllabification and another for the syllable labeling. In addition to that,
it also uses a set of nonlinear phonological rules proposed for foot formation
in Hindi, which encompass solutions to schwa-deletion in simple, compound,
derived and inflected words. The nonlinear phonological rules are based on
metrical phonology with the provision of recursive foot structure. A software
module is implemented in Python. The testing of the software for
syllabification, syllable labeling, schwa deletion and prosodic labeling yield
an accuracy of more than 99% on a lexicon of size 28664 words.
| 2,017 | Computation and Language |
Sharp Models on Dull Hardware: Fast and Accurate Neural Machine
Translation Decoding on the CPU | Attentional sequence-to-sequence models have become the new standard for
machine translation, but one challenge of such models is a significant increase
in training and decoding cost compared to phrase-based systems. Here, we focus
on efficient decoding, with a goal of achieving accuracy close the
state-of-the-art in neural machine translation (NMT), while achieving CPU
decoding speed/throughput close to that of a phrasal decoder.
We approach this problem from two angles: First, we describe several
techniques for speeding up an NMT beam search decoder, which obtain a 4.4x
speedup over a very efficient baseline decoder without changing the decoder
output. Second, we propose a simple but powerful network architecture which
uses an RNN (GRU/LSTM) layer at bottom, followed by a series of stacked
fully-connected layers applied at every timestep. This architecture achieves
similar accuracy to a deep recurrent model, at a small fraction of the training
and decoding cost. By combining these techniques, our best system achieves a
very competitive accuracy of 38.3 BLEU on WMT English-French NewsTest2014,
while decoding at 100 words/sec on single-threaded CPU. We believe this is the
best published accuracy/speed trade-off of an NMT system.
| 2,017 | Computation and Language |
Machine Comprehension by Text-to-Text Neural Question Generation | We propose a recurrent neural model that generates natural-language questions
from documents, conditioned on answers. We show how to train the model using a
combination of supervised and reinforcement learning. After teacher forcing for
standard maximum likelihood training, we fine-tune the model using policy
gradient techniques to maximize several rewards that measure question quality.
Most notably, one of these rewards is the performance of a question-answering
system. We motivate question generation as a means to improve the performance
of question answering systems. Our model is trained and evaluated on the recent
question-answering dataset SQuAD.
| 2,017 | Computation and Language |
Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters
for Tweet Polarity Classification | This paper presents Senti17 system which uses ten convolutional neural
networks (ConvNet) to assign a sentiment label to a tweet. The network consists
of a convolutional layer followed by a fully-connected layer and a Softmax on
top. Ten instances of this network are initialized with the same word
embeddings as inputs but with different initializations for the network
weights. We combine the results of all instances by selecting the sentiment
label given by the majority of the ten voters. This system is ranked fourth in
SemEval-2017 Task4 over 38 systems with 67.4%
| 2,017 | Computation and Language |
Cross-lingual Distillation for Text Classification | Cross-lingual text classification(CLTC) is the task of classifying documents
written in different languages into the same taxonomy of categories. This paper
presents a novel approach to CLTC that builds on model distillation, which
adapts and extends a framework originally proposed for model compression. Using
soft probabilistic predictions for the documents in a label-rich language as
the (induced) supervisory labels in a parallel corpus of documents, we train
classifiers successfully for new languages in which labeled training data are
not available. An adversarial feature adaptation technique is also applied
during the model training to reduce distribution mismatch. We conducted
experiments on two benchmark CLTC datasets, treating English as the source
language and German, French, Japan and Chinese as the unlabeled target
languages. The proposed approach had the advantageous or comparable performance
of the other state-of-art methods.
| 2,018 | Computation and Language |
Crowdsourcing Argumentation Structures in Chinese Hotel Reviews | Argumentation mining aims at automatically extracting the premises-claim
discourse structures in natural language texts. There is a great demand for
argumentation corpora for customer reviews. However, due to the controversial
nature of the argumentation annotation task, there exist very few large-scale
argumentation corpora for customer reviews. In this work, we novelly use the
crowdsourcing technique to collect argumentation annotations in Chinese hotel
reviews. As the first Chinese argumentation dataset, our corpus includes 4814
argument component annotations and 411 argument relation annotations, and its
annotations qualities are comparable to some widely used argumentation corpora
in other languages.
| 2,017 | Computation and Language |
Joint RNN Model for Argument Component Boundary Detection | Argument Component Boundary Detection (ACBD) is an important sub-task in
argumentation mining; it aims at identifying the word sequences that constitute
argument components, and is usually considered as the first sub-task in the
argumentation mining pipeline. Existing ACBD methods heavily depend on
task-specific knowledge, and require considerable human efforts on
feature-engineering. To tackle these problems, in this work, we formulate ACBD
as a sequence labeling problem and propose a variety of Recurrent Neural
Network (RNN) based methods, which do not use domain specific or handcrafted
features beyond the relative position of the sentence in the document. In
particular, we propose a novel joint RNN model that can predict whether
sentences are argumentative or not, and use the predicted results to more
precisely detect the argument component boundaries. We evaluate our techniques
on two corpora from two different genres; results suggest that our joint RNN
model obtain the state-of-the-art performance on both datasets.
| 2,017 | Computation and Language |
Sequential Attention: A Context-Aware Alignment Function for Machine
Reading | In this paper we propose a neural network model with a novel Sequential
Attention layer that extends soft attention by assigning weights to words in an
input sequence in a way that takes into account not just how well that word
matches a query, but how well surrounding words match. We evaluate this
approach on the task of reading comprehension (on the Who did What and CNN
datasets) and show that it dramatically improves a strong baseline--the
Stanford Reader--and is competitive with the state of the art.
| 2,017 | Computation and Language |
Deep Speaker: an End-to-End Neural Speaker Embedding System | We present Deep Speaker, a neural speaker embedding system that maps
utterances to a hypersphere where speaker similarity is measured by cosine
similarity. The embeddings generated by Deep Speaker can be used for many
tasks, including speaker identification, verification, and clustering. We
experiment with ResCNN and GRU architectures to extract the acoustic features,
then mean pool to produce utterance-level speaker embeddings, and train using
triplet loss based on cosine similarity. Experiments on three distinct datasets
suggest that Deep Speaker outperforms a DNN-based i-vector baseline. For
example, Deep Speaker reduces the verification equal error rate by 50%
(relatively) and improves the identification accuracy by 60% (relatively) on a
text-independent dataset. We also present results that suggest adapting from a
model trained with Mandarin can improve accuracy for English speaker
recognition.
| 2,017 | Computation and Language |
Building Morphological Chains for Agglutinative Languages | In this paper, we build morphological chains for agglutinative languages by
using a log-linear model for the morphological segmentation task. The model is
based on the unsupervised morphological segmentation system called
MorphoChains. We extend MorphoChains log linear model by expanding the
candidate space recursively to cover more split points for agglutinative
languages such as Turkish, whereas in the original model candidates are
generated by considering only binary segmentation of each word. The results
show that we improve the state-of-art Turkish scores by 12% having a F-measure
of 72% and we improve the English scores by 3% having a F-measure of 74%.
Eventually, the system outperforms both MorphoChains and other well-known
unsupervised morphological segmentation systems. The results indicate that
candidate generation plays an important role in such an unsupervised log-linear
model that is learned using contrastive estimation with negative samples.
| 2,017 | Computation and Language |
Supervised Learning of Universal Sentence Representations from Natural
Language Inference Data | Many modern NLP systems rely on word embeddings, previously trained in an
unsupervised manner on large corpora, as base features. Efforts to obtain
embeddings for larger chunks of text, such as sentences, have however not been
so successful. Several attempts at learning unsupervised representations of
sentences have not reached satisfactory enough performance to be widely
adopted. In this paper, we show how universal sentence representations trained
using the supervised data of the Stanford Natural Language Inference datasets
can consistently outperform unsupervised methods like SkipThought vectors on a
wide range of transfer tasks. Much like how computer vision uses ImageNet to
obtain features, which can then be transferred to other tasks, our work tends
to indicate the suitability of natural language inference for transfer learning
to other NLP tasks. Our encoder is publicly available.
| 2,018 | Computation and Language |
Learning Representations of Emotional Speech with Deep Convolutional
Generative Adversarial Networks | Automatically assessing emotional valence in human speech has historically
been a difficult task for machine learning algorithms. The subtle changes in
the voice of the speaker that are indicative of positive or negative emotional
states are often "overshadowed" by voice characteristics relating to emotional
intensity or emotional activation. In this work we explore a representation
learning approach that automatically derives discriminative representations of
emotional speech. In particular, we investigate two machine learning strategies
to improve classifier performance: (1) utilization of unlabeled data using a
deep convolutional generative adversarial network (DCGAN), and (2) multitask
learning. Within our extensive experiments we leverage a multitask annotated
emotional corpus as well as a large unlabeled meeting corpus (around 100
hours). Our speaker-independent classification experiments show that in
particular the use of unlabeled data in our investigations improves performance
of the classifiers and both fully supervised baseline approaches are
outperformed considerably. We improve the classification of emotional valence
on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which
is competitive to state-of-the-art performance.
| 2,017 | Computation and Language |
On Using Active Learning and Self-Training when Mining Performance
Discussions on Stack Overflow | Abundant data is the key to successful machine learning. However, supervised
learning requires annotated data that are often hard to obtain. In a
classification task with limited resources, Active Learning (AL) promises to
guide annotators to examples that bring the most value for a classifier. AL can
be successfully combined with self-training, i.e., extending a training set
with the unlabelled examples for which a classifier is the most certain. We
report our experiences on using AL in a systematic manner to train an SVM
classifier for Stack Overflow posts discussing performance of software
components. We show that the training examples deemed as the most valuable to
the classifier are also the most difficult for humans to annotate. Despite
carefully evolved annotation criteria, we report low inter-rater agreement, but
we also propose mitigation strategies. Finally, based on one annotator's work,
we show that self-training can improve the classification accuracy. We conclude
the paper by discussing implication for future text miners aspiring to use AL
and self-training.
| 2,017 | Computation and Language |
Max-Pooling Loss Training of Long Short-Term Memory Networks for
Small-Footprint Keyword Spotting | We propose a max-pooling based loss function for training Long Short-Term
Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
CPU, memory, and latency requirements. The max-pooling loss training can be
further guided by initializing with a cross-entropy loss trained network. A
posterior smoothing based evaluation approach is employed to measure keyword
spotting performance. Our experimental results show that LSTM models trained
using cross-entropy loss or max-pooling loss outperform a cross-entropy loss
trained baseline feed-forward Deep Neural Network (DNN). In addition,
max-pooling loss trained LSTM with randomly initialized network performs better
compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss
trained LSTM initialized with a cross-entropy pre-trained network shows the
best performance, which yields $67.6\%$ relative reduction compared to baseline
feed-forward DNN in Area Under the Curve (AUC) measure.
| 2,016 | Computation and Language |
A Generative Model of a Pronunciation Lexicon for Hindi | Voice browser applications in Text-to- Speech (TTS) and Automatic Speech
Recognition (ASR) systems crucially depend on a pronunciation lexicon. The
present paper describes the model of pronunciation lexicon of Hindi developed
to automatically generate the output forms of Hindi at two levels, the
<phoneme> and the <PS> (PS, in short for Prosodic Structure). The latter level
involves both syllable-division and stress placement. The paper describes the
tool developed for generating the two-level outputs of lexica in Hindi.
| 2,017 | Computation and Language |
Learning Distributed Representations of Texts and Entities from
Knowledge Base | We describe a neural network model that jointly learns distributed
representations of texts and knowledge base (KB) entities. Given a text in the
KB, we train our proposed model to predict entities that are relevant to the
text. Our model is designed to be generic with the ability to address various
NLP tasks with ease. We train the model using a large corpus of texts and their
entity annotations extracted from Wikipedia. We evaluated the model on three
important NLP tasks (i.e., sentence textual similarity, entity linking, and
factoid question answering) involving both unsupervised and supervised
settings. As a result, we achieved state-of-the-art results on all three of
these tasks. Our code and trained models are publicly available for further
academic research.
| 2,017 | Computation and Language |
Generating Memorable Mnemonic Encodings of Numbers | The major system is a mnemonic system that can be used to memorize sequences
of numbers. In this work, we present a method to automatically generate
sentences that encode a given number. We propose several encoding models and
compare the most promising ones in a password memorability study. The results
of the study show that a model combining part-of-speech sentence templates with
an $n$-gram language model produces the most memorable password
representations.
| 2,017 | Computation and Language |
Combating Human Trafficking with Deep Multimodal Models | Human trafficking is a global epidemic affecting millions of people across
the planet. Sex trafficking, the dominant form of human trafficking, has seen a
significant rise mostly due to the abundance of escort websites, where human
traffickers can openly advertise among at-will escort advertisements. In this
paper, we take a major step in the automatic detection of advertisements
suspected to pertain to human trafficking. We present a novel dataset called
Trafficking-10k, with more than 10,000 advertisements annotated for this task.
The dataset contains two sources of information per advertisement: text and
images. For the accurate detection of trafficking advertisements, we designed
and trained a deep multimodal model called the Human Trafficking Deep Network
(HTDN).
| 2,017 | Computation and Language |
Density Estimation for Geolocation via Convolutional Mixture Density
Network | Nowadays, geographic information related to Twitter is crucially important
for fine-grained applications. However, the amount of geographic information
avail- able on Twitter is low, which makes the pursuit of many applications
challenging. Under such circumstances, estimating the location of a tweet is an
important goal of the study. Unlike most previous studies that estimate the
pre-defined district as the classification task, this study employs a
probability distribution to represent richer information of the tweet, not only
the location but also its ambiguity. To realize this modeling, we propose the
convolutional mixture density network (CMDN), which uses text data to estimate
the mixture model parameters. Experimentally obtained results reveal that CMDN
achieved the highest prediction performance among the method for predicting the
exact coordinates. It also provides a quantitative representation of the
location ambiguity for each tweet that properly works for extracting the
reliable location estimations.
| 2,017 | Computation and Language |
Reinforced Mnemonic Reader for Machine Reading Comprehension | In this paper, we introduce the Reinforced Mnemonic Reader for machine
reading comprehension tasks, which enhances previous attentive readers in two
aspects. First, a reattention mechanism is proposed to refine current
attentions by directly accessing to past attentions that are temporally
memorized in a multi-round alignment architecture, so as to avoid the problems
of attention redundancy and attention deficiency. Second, a new optimization
approach, called dynamic-critical reinforcement learning, is introduced to
extend the standard supervised method. It always encourages to predict a more
acceptable answer so as to address the convergence suppression problem occurred
in traditional reinforcement learning algorithms. Extensive experiments on the
Stanford Question Answering Dataset (SQuAD) show that our model achieves
state-of-the-art results. Meanwhile, our model outperforms previous systems by
over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD
datasets.
| 2,018 | Computation and Language |
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment | Type-level word embeddings use the same set of parameters to represent all
instances of a word regardless of its context, ignoring the inherent lexical
ambiguity in language. Instead, we embed semantic concepts (or synsets) as
defined in WordNet and represent a word token in a particular context by
estimating a distribution over relevant semantic concepts. We use the new,
context-sensitive embeddings in a model for predicting prepositional phrase(PP)
attachments and jointly learn the concept embeddings and model parameters. We
show that using context-sensitive embeddings improves the accuracy of the PP
attachment model by 5.4% absolute points, which amounts to a 34.4% relative
reduction in errors.
| 2,017 | Computation and Language |
Convolutional Sequence to Sequence Learning | The prevalent approach to sequence to sequence learning maps an input
sequence to a variable length output sequence via recurrent neural networks. We
introduce an architecture based entirely on convolutional neural networks.
Compared to recurrent models, computations over all elements can be fully
parallelized during training and optimization is easier since the number of
non-linearities is fixed and independent of the input length. Our use of gated
linear units eases gradient propagation and we equip each decoder layer with a
separate attention module. We outperform the accuracy of the deep LSTM setup of
Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French
translation at an order of magnitude faster speed, both on GPU and CPU.
| 2,017 | Computation and Language |
Word and Phrase Translation with word2vec | Word and phrase tables are key inputs to machine translations, but costly to
produce. New unsupervised learning methods represent words and phrases in a
high-dimensional vector space, and these monolingual embeddings have been shown
to encode syntactic and semantic relationships between language elements. The
information captured by these embeddings can be exploited for bilingual
translation by learning a transformation matrix that allows matching relative
positions across two monolingual vector spaces. This method aims to identify
high-quality candidates for word and phrase translation more cost-effectively
from unlabeled data.
This paper expands the scope of previous attempts of bilingual translation to
four languages (English, German, Spanish, and French). It shows how to process
the source data, train a neural network to learn the high-dimensional
embeddings for individual languages and expands the framework for testing their
quality beyond the English language. Furthermore, it shows how to learn
bilingual transformation matrices and obtain candidates for word and phrase
translation, and assess their quality.
| 2,018 | Computation and Language |
Phonetic Temporal Neural Model for Language Identification | Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.
| 2,017 | Computation and Language |
Phone-aware Neural Language Identification | Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great success. We present a phone-aware neural LID architecture,
which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR
system. By utilizing the phonetic knowledge, the LID performance can be
significantly improved. Interestingly, even if the test language is not
involved in the ASR training, the phonetic knowledge still presents a large
contribution. Our experiments conducted on four languages within the Babel
corpus demonstrated that the phone-aware approach is highly effective.
| 2,017 | Computation and Language |
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation
Learning with Confidence | Knowledge graphs (KGs), which could provide essential relational information
between entities, have been widely utilized in various knowledge-driven
applications. Since the overall human knowledge is innumerable that still grows
explosively and changes frequently, knowledge construction and update
inevitably involve automatic mechanisms with less human supervision, which
usually bring in plenty of noises and conflicts to KGs. However, most
conventional knowledge representation learning methods assume that all triple
facts in existing KGs share the same significance without any noises. To
address this problem, we propose a novel confidence-aware knowledge
representation learning framework (CKRL), which detects possible noises in KGs
while learning knowledge representations with confidence simultaneously.
Specifically, we introduce the triple confidence to conventional
translation-based methods for knowledge representation learning. To make triple
confidence more flexible and universal, we only utilize the internal structural
information in KGs, and propose three kinds of triple confidences considering
both local and global structural information. In experiments, We evaluate our
models on knowledge graph noise detection, knowledge graph completion and
triple classification. Experimental results demonstrate that our
confidence-aware models achieve significant and consistent improvements on all
tasks, which confirms the capability of CKRL modeling confidence with
structural information in both KG noise detection and knowledge representation
learning.
| 2,018 | Computation and Language |
A Systematic Review of Hindi Prosody | Prosody describes both form and function of a sentence using the
suprasegmental features of speech. Prosody phenomena are explored in the domain
of higher phonological constituents such as word, phonological phrase and
intonational phrase. The study of prosody at the word level is called word
prosody and above word level is called sentence prosody. Word Prosody describes
stress pattern by comparing the prosodic features of its constituent syllables.
Sentence Prosody involves the study on phrasing pattern and intonatonal pattern
of a language. The aim of this study is to summarize the existing works on
Hindi prosody carried out in different domain of language and speech
processing. The review is presented in a systematic fashion so that it could be
a useful resource for one who wants to build on the existing works.
| 2,017 | Computation and Language |
Drug-drug Interaction Extraction via Recurrent Neural Network with
Multiple Attention Layers | Drug-drug interaction (DDI) is a vital information when physicians and
pharmacists intend to co-administer two or more drugs. Thus, several DDI
databases are constructed to avoid mistakenly combined use. In recent years,
automatically extracting DDIs from biomedical text has drawn researchers'
attention. However, the existing work utilize either complex feature
engineering or NLP tools, both of which are insufficient for sentence
comprehension. Inspired by the deep learning approaches in natural language
processing, we propose a recur- rent neural network model with multiple
attention layers for DDI classification. We evaluate our model on 2013 SemEval
DDIExtraction dataset. The experiments show that our model classifies most of
the drug pairs into correct DDI categories, which outperforms the existing NLP
or deep learning methods.
| 2,017 | Computation and Language |
Logical Parsing from Natural Language Based on a Neural Translation
Model | Semantic parsing has emerged as a significant and powerful paradigm for
natural language interface and question answering systems. Traditional methods
of building a semantic parser rely on high-quality lexicons, hand-crafted
grammars and linguistic features which are limited by applied domain or
representation. In this paper, we propose a general approach to learn from
denotations based on Seq2Seq model augmented with attention mechanism. We
encode input sequence into vectors and use dynamic programming to infer
candidate logical forms. We utilize the fact that similar utterances should
have similar logical forms to help reduce the searching space. Under our
learning policy, the Seq2Seq model can learn mappings gradually with noises.
Curriculum learning is adopted to make the learning smoother. We test our
method on the arithmetic domain which shows our model can successfully infer
the correct logical forms and learn the word meanings, compositionality and
operation orders simultaneously.
| 2,017 | Computation and Language |
The Pragmatics of Indirect Commands in Collaborative Discourse | Today's artificial assistants are typically prompted to perform tasks through
direct, imperative commands such as \emph{Set a timer} or \emph{Pick up the
box}. However, to progress toward more natural exchanges between humans and
these assistants, it is important to understand the way non-imperative
utterances can indirectly elicit action of an addressee. In this paper, we
investigate command types in the setting of a grounded, collaborative game. We
focus on a less understood family of utterances for eliciting agent action,
locatives like \emph{The chair is in the other room}, and demonstrate how these
utterances indirectly command in specific game state contexts. Our work shows
that models with domain-specific grounding can effectively realize the
pragmatic reasoning that is necessary for more robust natural language
interaction.
| 2,017 | Computation and Language |
Sequential Dialogue Context Modeling for Spoken Language Understanding | Spoken Language Understanding (SLU) is a key component of goal oriented
dialogue systems that would parse user utterances into semantic frame
representations. Traditionally SLU does not utilize the dialogue history beyond
the previous system turn and contextual ambiguities are resolved by the
downstream components. In this paper, we explore novel approaches for modeling
dialogue context in a recurrent neural network (RNN) based language
understanding system. We propose the Sequential Dialogue Encoder Network, that
allows encoding context from the dialogue history in chronological order. We
compare the performance of our proposed architecture with two context models,
one that uses just the previous turn context and another that encodes dialogue
context in a memory network, but loses the order of utterances in the dialogue
history. Experiments with a multi-domain dialogue dataset demonstrate that the
proposed architecture results in reduced semantic frame error rates.
| 2,017 | Computation and Language |
DeepDeath: Learning to Predict the Underlying Cause of Death with Big
Data | Multiple cause-of-death data provides a valuable source of information that
can be used to enhance health standards by predicting health related
trajectories in societies with large populations. These data are often
available in large quantities across U.S. states and require Big Data
techniques to uncover complex hidden patterns. We design two different classes
of models suitable for large-scale analysis of mortality data, a Hadoop-based
ensemble of random forests trained over N-grams, and the DeepDeath, a deep
classifier based on the recurrent neural network (RNN). We apply both classes
to the mortality data provided by the National Center for Health Statistics and
show that while both perform significantly better than the random classifier,
the deep model that utilizes long short-term memory networks (LSTMs), surpasses
the N-gram based models and is capable of learning the temporal aspect of the
data without a need for building ad-hoc, expert-driven features.
| 2,017 | Computation and Language |
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for
Reading Comprehension | We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for answering the questions. We show that, in comparison to
other recently introduced large-scale datasets, TriviaQA (1) has relatively
complex, compositional questions, (2) has considerable syntactic and lexical
variability between questions and corresponding answer-evidence sentences, and
(3) requires more cross sentence reasoning to find answers. We also present two
baseline algorithms: a feature-based classifier and a state-of-the-art neural
network, that performs well on SQuAD reading comprehension. Neither approach
comes close to human performance (23% and 40% vs. 80%), suggesting that
TriviaQA is a challenging testbed that is worth significant future study. Data
and code available at -- http://nlp.cs.washington.edu/triviaqa/
| 2,017 | Computation and Language |
DeepTingle | DeepTingle is a text prediction and classification system trained on the
collected works of the renowned fantastic gay erotica author Chuck Tingle.
Whereas the writing assistance tools you use everyday (in the form of
predictive text, translation, grammar checking and so on) are trained on
generic, purportedly "neutral" datasets, DeepTingle is trained on a very
specific, internally consistent but externally arguably eccentric dataset. This
allows us to foreground and confront the norms embedded in data-driven
creativity and productivity assistance tools. As such tools effectively
function as extensions of our cognition into technology, it is important to
identify the norms they embed within themselves and, by extension, us.
DeepTingle is realized as a web application based on LSTM networks and the
GloVe word embedding, implemented in JavaScript with Keras-JS.
| 2,019 | Computation and Language |
A Survey of Deep Learning Methods for Relation Extraction | Relation Extraction is an important sub-task of Information Extraction which
has the potential of employing deep learning (DL) models with the creation of
large datasets using distant supervision. In this review, we compare the
contributions and pitfalls of the various DL models that have been used for the
task, to help guide the path ahead.
| 2,017 | Computation and Language |
Analysing Data-To-Text Generation Benchmarks | Recently, several data-sets associating data to text have been created to
train data-to-text surface realisers. It is unclear however to what extent the
surface realisation task exercised by these data-sets is linguistically
challenging. Do these data-sets provide enough variety to encourage the
development of generic, high-quality data-to-text surface realisers ? In this
paper, we argue that these data-sets have important drawbacks. We back up our
claim using statistics, metrics and manual evaluation. We conclude by eliciting
a set of criteria for the creation of a data-to-text benchmark which could help
better support the development, evaluation and comparison of linguistically
sophisticated data-to-text surface realisers.
| 2,017 | Computation and Language |
Survey of Visual Question Answering: Datasets and Techniques | Visual question answering (or VQA) is a new and exciting problem that
combines natural language processing and computer vision techniques. We present
a survey of the various datasets and models that have been used to tackle this
task. The first part of the survey details the various datasets for VQA and
compares them along some common factors. The second part of this survey details
the different approaches for VQA, classified into four types: non-deep learning
models, deep learning models without attention, deep learning models with
attention, and other models which do not fit into the first three. Finally, we
compare the performances of these approaches and provide some directions for
future work.
| 2,017 | Computation and Language |
A Minimal Span-Based Neural Constituency Parser | In this work, we present a minimal neural model for constituency parsing
based on independent scoring of labels and spans. We show that this model is
not only compatible with classical dynamic programming techniques, but also
admits a novel greedy top-down inference algorithm based on recursive
partitioning of the input. We demonstrate empirically that both prediction
schemes are competitive with recent work, and when combined with basic
extensions to the scoring model are capable of achieving state-of-the-art
single-model performance on the Penn Treebank (91.79 F1) and strong performance
on the French Treebank (82.23 F1).
| 2,017 | Computation and Language |
Learning with Noise: Enhance Distantly Supervised Relation Extraction
with Dynamic Transition Matrix | Distant supervision significantly reduces human efforts in building training
data for many classification tasks. While promising, this technique often
introduces noise to the generated training data, which can severely affect the
model performance. In this paper, we take a deep look at the application of
distant supervision in relation extraction. We show that the dynamic transition
matrix can effectively characterize the noise in the training data built by
distant supervision. The transition matrix can be effectively trained using a
novel curriculum learning based method without any direct supervision about the
noise. We thoroughly evaluate our approach under a wide range of extraction
scenarios. Experimental results show that our approach consistently improves
the extraction results and outperforms the state-of-the-art in various
evaluation scenarios.
| 2,018 | Computation and Language |
Content-based Approach for Vietnamese Spam SMS Filtering | Short Message Service (SMS) spam is a serious problem in Vietnam because of
the availability of very cheap pre-paid SMS packages. There are some systems to
detect and filter spam messages for English, most of which use machine learning
techniques to analyze the content of messages and classify them. For
Vietnamese, there is some research on spam email filtering but none focused on
SMS. In this work, we propose the first system for filtering Vietnamese spam
SMS. We first propose an appropriate preprocessing method since existing tools
for Vietnamese preprocessing cannot give good accuracy on our dataset. We then
experiment with vector representations and classifiers to find the best model
for this problem. Our system achieves an accuracy of 94% when labelling spam
messages while the misclassification rate of legitimate messages is relatively
small, about only 0.4%. This is an encouraging result compared to that of
English and can be served as a strong baseline for future development of
Vietnamese SMS spam prevention systems.
| 2,017 | Computation and Language |
Building a Semantic Role Labelling System for Vietnamese | Semantic role labelling (SRL) is a task in natural language processing which
detects and classifies the semantic arguments associated with the predicates of
a sentence. It is an important step towards understanding the meaning of a
natural language. There exists SRL systems for well-studied languages like
English, Chinese or Japanese but there is not any such system for the
Vietnamese language. In this paper, we present the first SRL system for
Vietnamese with encouraging accuracy. We first demonstrate that a simple
application of SRL techniques developed for English could not give a good
accuracy for Vietnamese. We then introduce a new algorithm for extracting
candidate syntactic constituents, which is much more accurate than the common
node-mapping algorithm usually used in the identification step. Finally, in the
classification step, in addition to the common linguistic features, we propose
novel and useful features for use in SRL. Our SRL system achieves an $F_1$
score of 73.53\% on the Vietnamese PropBank corpus. This system, including
software and corpus, is available as an open source project and we believe that
it is a good baseline for the development of future Vietnamese SRL systems.
| 2,017 | Computation and Language |
End-to-end Recurrent Neural Network Models for Vietnamese Named Entity
Recognition: Word-level vs. Character-level | This paper demonstrates end-to-end neural network architectures for
Vietnamese named entity recognition. Our best model is a combination of
bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
input, which achieves an F1 score of 88.59% on a standard test set. Our system
is able to achieve a comparable performance to the first-rank system of the
VLSP campaign without using any syntactic or hand-crafted features. We also
give an extensive empirical study on using common deep learning models for
Vietnamese NER, at both word and character level.
| 2,017 | Computation and Language |
Dynamic Compositional Neural Networks over Tree Structure | Tree-structured neural networks have proven to be effective in learning
semantic representations by exploiting syntactic information. In spite of their
success, most existing models suffer from the underfitting problem: they
recursively use the same shared compositional function throughout the whole
compositional process and lack expressive power due to inability to capture the
richness of compositionality. In this paper, we address this issue by
introducing the dynamic compositional neural networks over tree structure
(DC-TreeNN), in which the compositional function is dynamically generated by a
meta network. The role of meta-network is to capture the metaknowledge across
the different compositional rules and formulate them. Experimental results on
two typical tasks show the effectiveness of the proposed models.
| 2,017 | Computation and Language |
On the role of words in the network structure of texts: application to
authorship attribution | Well-established automatic analyses of texts mainly consider frequencies of
linguistic units, e.g. letters, words and bigrams, while methods based on
co-occurrence networks consider the structure of texts regardless of the nodes
label (i.e. the words semantics). In this paper, we reconcile these distinct
viewpoints by introducing a generalized similarity measure to compare texts
which accounts for both the network structure of texts and the role of
individual words in the networks. We use the similarity measure for authorship
attribution of three collections of books, each composed of 8 authors and 10
books per author. High accuracy rates were obtained with typical values from
90% to 98.75%, much higher than with the traditional the TF-IDF approach for
the same collections. These accuracies are also higher than taking only the
topology of networks into account. We conclude that the different properties of
specific words on the macroscopic scale structure of a whole text are as
relevant as their frequency of appearance; conversely, considering the identity
of nodes brings further knowledge about a piece of text represented as a
network.
| 2,018 | Computation and Language |
Sketching Word Vectors Through Hashing | We propose a new fast word embedding technique using hash functions. The
method is a derandomization of a new type of random projections: By
disregarding the classic constraint used in designing random projections (i.e.,
preserving pairwise distances in a particular normed space), our solution
exploits extremely sparse non-negative random projections. Our experiments show
that the proposed method can achieve competitive results, comparable to neural
embedding learning techniques, however, with only a fraction of the
computational complexity of these methods. While the proposed derandomization
enhances the computational and space complexity of our method, the possibility
of applying weighting methods such as positive pointwise mutual information
(PPMI) to our models after their construction (and at a reduced dimensionality)
imparts a high discriminatory power to the resulting embeddings. Obviously,
this method comes with other known benefits of random projection-based
techniques such as ease of update.
| 2,018 | Computation and Language |
A Deep Reinforced Model for Abstractive Summarization | Attentional, RNN-based encoder-decoder models for abstractive summarization
have achieved good performance on short input and output sequences. For longer
documents and summaries however these models often include repetitive and
incoherent phrases. We introduce a neural network model with a novel
intra-attention that attends over the input and continuously generated output
separately, and a new training method that combines standard supervised word
prediction and reinforcement learning (RL). Models trained only with supervised
learning often exhibit "exposure bias" - they assume ground truth is provided
at each step during training. However, when standard word prediction is
combined with the global sequence prediction training of RL the resulting
summaries become more readable. We evaluate this model on the CNN/Daily Mail
and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the
CNN/Daily Mail dataset, an improvement over previous state-of-the-art models.
Human evaluation also shows that our model produces higher quality summaries.
| 2,017 | Computation and Language |
Imagination improves Multimodal Translation | We decompose multimodal translation into two sub-tasks: learning to translate
and learning visually grounded representations. In a multitask learning
framework, translations are learned in an attention-based encoder-decoder, and
grounded representations are learned through image representation prediction.
Our approach improves translation performance compared to the state of the art
on the Multi30K dataset. Furthermore, it is equally effective if we train the
image prediction task on the external MS COCO dataset, and we find improvements
if we train the translation model on the external News Commentary parallel
text.
| 2,017 | Computation and Language |
Reducing Bias in Production Speech Models | Replacing hand-engineered pipelines with end-to-end deep learning systems has
enabled strong results in applications like speech and object recognition.
However, the causality and latency constraints of production systems put
end-to-end speech models back into the underfitting regime and expose biases in
the model that we show cannot be overcome by "scaling up", i.e., training
bigger models on more data. In this work we systematically identify and address
sources of bias, reducing error rates by up to 20% while remaining practical
for deployment. We achieve this by utilizing improved neural architectures for
streaming inference, solving optimization issues, and employing strategies that
increase audio and label modelling versatility.
| 2,017 | Computation and Language |
Evaluating vector-space models of analogy | Vector-space representations provide geometric tools for reasoning about the
similarity of a set of objects and their relationships. Recent machine learning
methods for deriving vector-space embeddings of words (e.g., word2vec) have
achieved considerable success in natural language processing. These vector
spaces have also been shown to exhibit a surprising capacity to capture verbal
analogies, with similar results for natural images, giving new life to a
classic model of analogies as parallelograms that was first proposed by
cognitive scientists. We evaluate the parallelogram model of analogy as applied
to modern word embeddings, providing a detailed analysis of the extent to which
this approach captures human relational similarity judgments in a large
benchmark dataset. We find that that some semantic relationships are better
captured than others. We then provide evidence for deeper limitations of the
parallelogram model based on the intrinsic geometric constraints of vector
spaces, paralleling classic results for first-order similarity.
| 2,017 | Computation and Language |
Arc-swift: A Novel Transition System for Dependency Parsing | Transition-based dependency parsers often need sequences of local shift and
reduce operations to produce certain attachments. Correct individual decisions
hence require global information about the sentence context and mistakes cause
error propagation. This paper proposes a novel transition system, arc-swift,
that enables direct attachments between tokens farther apart with a single
transition. This allows the parser to leverage lexical information more
directly in transition decisions. Hence, arc-swift can achieve significantly
better performance with a very small beam size. Our parsers reduce error by
3.7--7.6% relative to those using existing transition systems on the Penn
Treebank dependency parsing task and English Universal Dependencies.
| 2,017 | Computation and Language |
Learning Semantic Correspondences in Technical Documentation | We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.
| 2,017 | Computation and Language |
Annotating and Modeling Empathy in Spoken Conversations | Empathy, as defined in behavioral sciences, expresses the ability of human
beings to recognize, understand and react to emotions, attitudes and beliefs of
others. The lack of an operational definition of empathy makes it difficult to
measure it. In this paper, we address two related problems in automatic
affective behavior analysis: the design of the annotation protocol and the
automatic recognition of empathy from spoken conversations. We propose and
evaluate an annotation scheme for empathy inspired by the modal model of
emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic
spoken conversations. In the context of behavioral analysis, we designed an
automatic segmentation and classification system for empathy. Given the
different speech and language levels of representation where empathy may be
communicated, we investigated features derived from the lexical and acoustic
spaces. The feature development process was designed to support both the fusion
and automatic selection of relevant features from high dimensional space. The
automatic classification system was evaluated on call center conversations
where it showed significantly better performance than the baseline.
| 2,018 | Computation and Language |
Joint Modeling of Content and Discourse Relations in Dialogues | We present a joint modeling approach to identify salient discussion points in
spoken meetings as well as to label the discourse relations between speaker
turns. A variation of our model is also discussed when discourse relations are
treated as latent variables. Experimental results on two popular meeting
corpora show that our joint model can outperform state-of-the-art approaches
for both phrase-based content selection and discourse relation prediction
tasks. We also evaluate our model on predicting the consistency among team
members' understanding of their group decisions. Classifiers trained with
features constructed from our model achieve significant better predictive
performance than the state-of-the-art.
| 2,017 | Computation and Language |
Winning on the Merits: The Joint Effects of Content and Style on Debate
Outcomes | Debate and deliberation play essential roles in politics and government, but
most models presume that debates are won mainly via superior style or agenda
control. Ideally, however, debates would be won on the merits, as a function of
which side has the stronger arguments. We propose a predictive model of debate
that estimates the effects of linguistic features and the latent persuasive
strengths of different topics, as well as the interactions between the two.
Using a dataset of 118 Oxford-style debates, our model's combination of content
(as latent topics) and style (as linguistic features) allows us to predict
audience-adjudicated winners with 74% accuracy, significantly outperforming
linguistic features alone (66%). Our model finds that winning sides employ
stronger arguments, and allows us to identify the linguistic features
associated with strong or weak arguments.
| 2,017 | Computation and Language |
Representation learning of drug and disease terms for drug repositioning | Drug repositioning (DR) refers to identification of novel indications for the
approved drugs. The requirement of huge investment of time as well as money and
risk of failure in clinical trials have led to surge in interest in drug
repositioning. DR exploits two major aspects associated with drugs and
diseases: existence of similarity among drugs and among diseases due to their
shared involved genes or pathways or common biological effects. Existing
methods of identifying drug-disease association majorly rely on the information
available in the structured databases only. On the other hand, abundant
information available in form of free texts in biomedical research articles are
not being fully exploited. Word-embedding or obtaining vector representation of
words from a large corpora of free texts using neural network methods have been
shown to give significant performance for several natural language processing
tasks. In this work we propose a novel way of representation learning to obtain
features of drugs and diseases by combining complementary information available
in unstructured texts and structured datasets. Next we use matrix completion
approach on these feature vectors to learn projection matrix between drug and
disease vector spaces. The proposed method has shown competitive performance
with state-of-the-art methods. Further, the case studies on Alzheimer's and
Hypertension diseases have shown that the predicted associations are matching
with the existing knowledge.
| 2,017 | Computation and Language |
Key-Value Retrieval Networks for Task-Oriented Dialogue | Neural task-oriented dialogue systems often struggle to smoothly interface
with a knowledge base. In this work, we seek to address this problem by
proposing a new neural dialogue agent that is able to effectively sustain
grounded, multi-domain discourse through a novel key-value retrieval mechanism.
The model is end-to-end differentiable and does not need to explicitly model
dialogue state or belief trackers. We also release a new dataset of 3,031
dialogues that are grounded through underlying knowledge bases and span three
distinct tasks in the in-car personal assistant space: calendar scheduling,
weather information retrieval, and point-of-interest navigation. Our
architecture is simultaneously trained on data from all domains and
significantly outperforms a competitive rule-based system and other existing
neural dialogue architectures on the provided domains according to both
automatic and human evaluation metrics.
| 2,017 | Computation and Language |
A Biomedical Information Extraction Primer for NLP Researchers | Biomedical Information Extraction is an exciting field at the crossroads of
Natural Language Processing, Biology and Medicine. It encompasses a variety of
different tasks that require application of state-of-the-art NLP techniques,
such as NER and Relation Extraction. This paper provides an overview of the
problems in the field and discusses some of the techniques used for solving
them.
| 2,017 | Computation and Language |
NeuroNER: an easy-to-use program for named-entity recognition based on
neural networks | Named-entity recognition (NER) aims at identifying entities of interest in a
text. Artificial neural networks (ANNs) have recently been shown to outperform
existing NER systems. However, ANNs remain challenging to use for non-expert
users. In this paper, we present NeuroNER, an easy-to-use named-entity
recognition tool based on ANNs. Users can annotate entities using a graphical
web-based user interface (BRAT): the annotations are then used to train an ANN,
which in turn predict entities' locations and categories in new texts. NeuroNER
makes this annotation-training-prediction flow smooth and accessible to anyone.
| 2,017 | Computation and Language |
Social Media-based Substance Use Prediction | In this paper, we demonstrate how the state-of-the-art machine learning and
text mining techniques can be used to build effective social media-based
substance use detection systems. Since a substance use ground truth is
difficult to obtain on a large scale, to maximize system performance, we
explore different feature learning methods to take advantage of a large amount
of unsupervised social media data. We also demonstrate the benefit of using
multi-view unsupervised feature learning to combine heterogeneous user
information such as Facebook `"likes" and "status updates" to enhance system
performance. Based on our evaluation, our best models achieved 86% AUC for
predicting tobacco use, 81% for alcohol use and 84% for drug use, all of which
significantly outperformed existing methods. Our investigation has also
uncovered interesting relations between a user's social media behavior (e.g.,
word usage) and substance use.
| 2,017 | Computation and Language |
Subregular Complexity and Deep Learning | This paper argues that the judicial use of formal language theory and
grammatical inference are invaluable tools in understanding how deep neural
networks can and cannot represent and learn long-term dependencies in temporal
sequences. Learning experiments were conducted with two types of Recurrent
Neural Networks (RNNs) on six formal languages drawn from the Strictly Local
(SL) and Strictly Piecewise (SP) classes. The networks were Simple RNNs
(s-RNNs) and Long Short-Term Memory RNNs (LSTMs) of varying sizes. The SL and
SP classes are among the simplest in a mathematically well-understood hierarchy
of subregular classes. They encode local and long-term dependencies,
respectively. The grammatical inference algorithm Regular Positive and Negative
Inference (RPNI) provided a baseline. According to earlier research, the LSTM
architecture should be capable of learning long-term dependencies and should
outperform s-RNNs. The results of these experiments challenge this narrative.
First, the LSTMs' performance was generally worse in the SP experiments than in
the SL ones. Second, the s-RNNs out-performed the LSTMs on the most complex SP
experiment and performed comparably to them on the others.
| 2,017 | Computation and Language |
A Novel Neural Network Model for Joint POS Tagging and Graph-based
Dependency Parsing | We present a novel neural network model that learns POS tagging and
graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to
learn feature representations shared for both POS tagging and dependency
parsing tasks, thus handling the feature-engineering problem. Our extensive
experiments, on 19 languages from the Universal Dependencies project, show that
our model outperforms the state-of-the-art neural network-based
Stack-propagation model for joint POS tagging and transition-based dependency
parsing, resulting in a new state of the art. Our code is open-source and
available together with pre-trained models at:
https://github.com/datquocnguyen/jPTDP
| 2,017 | Computation and Language |
Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model | Frame stacking is broadly applied in end-to-end neural network training like
connectionist temporal classification (CTC), and it leads to more accurate
models and faster decoding. However, it is not well-suited to conventional
neural network based on context-dependent state acoustic model, if the decoder
is unchanged. In this paper, we propose a novel frame retaining method which is
applied in decoding. The system which combined frame retaining with frame
stacking could reduces the time consumption of both training and decoding. Long
short-term memory (LSTM) recurrent neural networks (RNNs) using it achieve
almost linear training speedup and reduces relative 41\% real time factor
(RTF). At the same time, recognition performance is no degradation or improves
sightly on Shenma voice search dataset in Mandarin.
| 2,017 | Computation and Language |
Learning to Identify Ambiguous and Misleading News Headlines | Accuracy is one of the basic principles of journalism. However, it is
increasingly hard to manage due to the diversity of news media. Some editors of
online news tend to use catchy headlines which trick readers into clicking.
These headlines are either ambiguous or misleading, degrading the reading
experience of the audience. Thus, identifying inaccurate news headlines is a
task worth studying. Previous work names these headlines "clickbaits" and
mainly focus on the features extracted from the headlines, which limits the
performance since the consistency between headlines and news bodies is
underappreciated. In this paper, we clearly redefine the problem and identify
ambiguous and misleading headlines separately. We utilize class sequential
rules to exploit structure information when detecting ambiguous headlines. For
the identification of misleading headlines, we extract features based on the
congruence between headlines and bodies. To make use of the large unlabeled
data set, we apply a co-training method and gain an increase in performance.
The experiment results show the effectiveness of our methods. Then we use our
classifiers to detect inaccurate headlines crawled from different sources and
conduct a data analysis.
| 2,017 | Computation and Language |
Unlabeled Data for Morphological Generation With Character-Based
Sequence-to-Sequence Models | We present a semi-supervised way of training a character-based
encoder-decoder recurrent neural network for morphological reinflection, the
task of generating one inflected word form from another. This is achieved by
using unlabeled tokens or random strings as training data for an autoencoding
task, adapting a network for morphological reinflection, and performing
multi-task training. We thus use limited labeled data more effectively,
obtaining up to 9.9% improvement over state-of-the-art baselines for 8
different languages.
| 2,017 | Computation and Language |
Utility of General and Specific Word Embeddings for Classifying
Translational Stages of Research | Conventional text classification models make a bag-of-words assumption
reducing text into word occurrence counts per document. Recent algorithms such
as word2vec are capable of learning semantic meaning and similarity between
words in an entirely unsupervised manner using a contextual window and doing so
much faster than previous methods. Each word is projected into vector space
such that similar meaning words such as "strong" and "powerful" are projected
into the same general Euclidean space. Open questions about these embeddings
include their utility across classification tasks and the optimal properties
and source of documents to construct broadly functional embeddings. In this
work, we demonstrate the usefulness of pre-trained embeddings for
classification in our task and demonstrate that custom word embeddings, built
in the domain and for the tasks, can improve performance over word embeddings
learnt on more general data including news articles or Wikipedia.
| 2,018 | Computation and Language |
Transfer Learning for Named-Entity Recognition with Neural Networks | Recent approaches based on artificial neural networks (ANNs) have shown
promising results for named-entity recognition (NER). In order to achieve high
performances, ANNs need to be trained on a large labeled dataset. However,
labels might be difficult to obtain for the dataset on which the user wants to
perform NER: label scarcity is particularly pronounced for patient note
de-identification, which is an instance of NER. In this work, we analyze to
what extent transfer learning may address this issue. In particular, we
demonstrate that transferring an ANN model trained on a large labeled dataset
to another dataset with a limited number of labels improves upon the
state-of-the-art results on two different datasets for patient note
de-identification.
| 2,017 | Computation and Language |
Political Footprints: Political Discourse Analysis using Pre-Trained
Word Vectors | In this paper, we discuss how machine learning could be used to produce a
systematic and more objective political discourse analysis. Political
footprints are vector space models (VSMs) applied to political discourse. Each
of their vectors represents a word, and is produced by training the English
lexicon on large text corpora. This paper presents a simple implementation of
political footprints, some heuristics on how to use them, and their application
to four cases: the U.N. Kyoto Protocol and Paris Agreement, and two U.S.
presidential elections. The reader will be offered a number of reasons to
believe that political footprints produce meaningful results, along with some
suggestions on how to improve their implementation.
| 2,017 | Computation and Language |
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