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Towards a continuous modeling of natural language domains | Humans continuously adapt their style and language to a variety of domains.
However, a reliable definition of `domain' has eluded researchers thus far.
Additionally, the notion of discrete domains stands in contrast to the
multiplicity of heterogeneous domains that humans navigate, many of which
overlap. In order to better understand the change and variation of human
language, we draw on research in domain adaptation and extend the notion of
discrete domains to the continuous spectrum. We propose representation
learning-based models that can adapt to continuous domains and detail how these
can be used to investigate variation in language. To this end, we propose to
use dialogue modeling as a test bed due to its proximity to language modeling
and its social component.
| 2,016 | Computation and Language |
Text Segmentation using Named Entity Recognition and Co-reference
Resolution in English and Greek Texts | In this paper we examine the benefit of performing named entity recognition
(NER) and co-reference resolution to an English and a Greek corpus used for
text segmentation. The aim here is to examine whether the combination of text
segmentation and information extraction can be beneficial for the
identification of the various topics that appear in a document. NER was
performed manually in the English corpus and was compared with the output
produced by publicly available annotation tools while, an already existing tool
was used for the Greek corpus. Produced annotations from both corpora were
manually corrected and enriched to cover four types of named entities.
Co-reference resolution i.e., substitution of every reference of the same
instance with the same named entity identifier was subsequently performed. The
evaluation, using five text segmentation algorithms for the English corpus and
four for the Greek corpus leads to the conclusion that, the benefit highly
depends on the segment's topic, the number of named entity instances appearing
in it, as well as the segment's length.
| 2,016 | Computation and Language |
Word Embeddings for the Construction Domain | We introduce word vectors for the construction domain. Our vectors were
obtained by running word2vec on an 11M-word corpus that we created from scratch
by leveraging freely-accessible online sources of construction-related text. We
first explore the embedding space and show that our vectors capture meaningful
construction-specific concepts. We then evaluate the performance of our vectors
against that of ones trained on a 100B-word corpus (Google News) within the
framework of an injury report classification task. Without any parameter
tuning, our embeddings give competitive results, and outperform the Google News
vectors in many cases. Using a keyword-based compression of the reports also
leads to a significant speed-up with only a limited loss in performance. We
release our corpus and the data set we created for the classification task as
publicly available, in the hope that they will be used by future studies for
benchmarking and building on our work.
| 2,016 | Computation and Language |
Sequence-to-sequence neural network models for transliteration | Transliteration is a key component of machine translation systems and
software internationalization. This paper demonstrates that neural
sequence-to-sequence models obtain state of the art or close to state of the
art results on existing datasets. In an effort to make machine transliteration
accessible, we open source a new Arabic to English transliteration dataset and
our trained models.
| 2,016 | Computation and Language |
Feature-Augmented Neural Networks for Patient Note De-identification | Patient notes contain a wealth of information of potentially great interest
to medical investigators. However, to protect patients' privacy, Protected
Health Information (PHI) must be removed from the patient notes before they can
be legally released, a process known as patient note de-identification. The
main objective for a de-identification system is to have the highest possible
recall. Recently, the first neural-network-based de-identification system has
been proposed, yielding state-of-the-art results. Unlike other systems, it does
not rely on human-engineered features, which allows it to be quickly deployed,
but does not leverage knowledge from human experts or from electronic health
records (EHRs). In this work, we explore a method to incorporate
human-engineered features as well as features derived from EHRs to a
neural-network-based de-identification system. Our results show that the
addition of features, especially the EHR-derived features, further improves the
state-of-the-art in patient note de-identification, including for some of the
most sensitive PHI types such as patient names. Since in a real-life setting
patient notes typically come with EHRs, we recommend developers of
de-identification systems to leverage the information EHRs contain.
| 2,016 | Computation and Language |
Represent, Aggregate, and Constrain: A Novel Architecture for Machine
Reading from Noisy Sources | In order to extract event information from text, a machine reading model must
learn to accurately read and interpret the ways in which that information is
expressed. But it must also, as the human reader must, aggregate numerous
individual value hypotheses into a single coherent global analysis, applying
global constraints which reflect prior knowledge of the domain.
In this work we focus on the task of extracting plane crash event information
from clusters of related news articles whose labels are derived via distant
supervision. Unlike previous machine reading work, we assume that while most
target values will occur frequently in most clusters, they may also be missing
or incorrect.
We introduce a novel neural architecture to explicitly model the noisy nature
of the data and to deal with these aforementioned learning issues. Our models
are trained end-to-end and achieve an improvement of more than 12.1 F$_1$ over
previous work, despite using far less linguistic annotation. We apply factor
graph constraints to promote more coherent event analyses, with belief
propagation inference formulated within the transitions of a recurrent neural
network. We show this technique additionally improves maximum F$_1$ by up to
2.8 points, resulting in a relative improvement of $50\%$ over the previous
state-of-the-art.
| 2,016 | Computation and Language |
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled
Data Scarcity | In this paper we describe an end to end Neural Model for Named Entity
Recognition NER) which is based on Bi-Directional RNN-LSTM. Almost all NER
systems for Hindi use Language Specific features and handcrafted rules with
gazetteers. Our model is language independent and uses no domain specific
features or any handcrafted rules. Our models rely on semantic information in
the form of word vectors which are learnt by an unsupervised learning algorithm
on an unannotated corpus. Our model attained state of the art performance in
both English and Hindi without the use of any morphological analysis or without
using gazetteers of any sort.
| 2,017 | Computation and Language |
Experiments with POS Tagging Code-mixed Indian Social Media Text | This paper presents Centre for Development of Advanced Computing Mumbai's
(CDACM) submission to the NLP Tools Contest on Part-Of-Speech (POS) Tagging For
Code-mixed Indian Social Media Text (POSCMISMT) 2015 (collocated with ICON
2015). We submitted results for Hindi (hi), Bengali (bn), and Telugu (te)
languages mixed with English (en). In this paper, we have described our
approaches to the POS tagging techniques, we exploited for this task. Machine
learning has been used to POS tag the mixed language text. For POS tagging,
distributed representations of words in vector space (word2vec) for feature
extraction and Log-linear models have been tried. We report our work on all
three languages hi, bn, and te mixed with en.
| 2,015 | Computation and Language |
Chinese Poetry Generation with Planning based Neural Network | Chinese poetry generation is a very challenging task in natural language
processing. In this paper, we propose a novel two-stage poetry generating
method which first plans the sub-topics of the poem according to the user's
writing intent, and then generates each line of the poem sequentially, using a
modified recurrent neural network encoder-decoder framework. The proposed
planning-based method can ensure that the generated poem is coherent and
semantically consistent with the user's intent. A comprehensive evaluation with
human judgments demonstrates that our proposed approach outperforms the
state-of-the-art poetry generating methods and the poem quality is somehow
comparable to human poets.
| 2,016 | Computation and Language |
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | Recurrent neural networks (RNNs) have achieved state-of-the-art performances
in many natural language processing tasks, such as language modeling and
machine translation. However, when the vocabulary is large, the RNN model will
become very big (e.g., possibly beyond the memory capacity of a GPU device) and
its training will become very inefficient. In this work, we propose a novel
technique to tackle this challenge. The key idea is to use 2-Component (2C)
shared embedding for word representations. We allocate every word in the
vocabulary into a table, each row of which is associated with a vector, and
each column associated with another vector. Depending on its position in the
table, a word is jointly represented by two components: a row vector and a
column vector. Since the words in the same row share the row vector and the
words in the same column share the column vector, we only need $2 \sqrt{|V|}$
vectors to represent a vocabulary of $|V|$ unique words, which are far less
than the $|V|$ vectors required by existing approaches. Based on the
2-Component shared embedding, we design a new RNN algorithm and evaluate it
using the language modeling task on several benchmark datasets. The results
show that our algorithm significantly reduces the model size and speeds up the
training process, without sacrifice of accuracy (it achieves similar, if not
better, perplexity as compared to state-of-the-art language models).
Remarkably, on the One-Billion-Word benchmark Dataset, our algorithm achieves
comparable perplexity to previous language models, whilst reducing the model
size by a factor of 40-100, and speeding up the training process by a factor of
2. We name our proposed algorithm \emph{LightRNN} to reflect its very small
model size and very high training speed.
| 2,016 | Computation and Language |
Named Entity Recognition for Novel Types by Transfer Learning | In named entity recognition, we often don't have a large in-domain training
corpus or a knowledge base with adequate coverage to train a model directly. In
this paper, we propose a method where, given training data in a related domain
with similar (but not identical) named entity (NE) types and a small amount of
in-domain training data, we use transfer learning to learn a domain-specific NE
model. That is, the novelty in the task setup is that we assume not just domain
mismatch, but also label mismatch.
| 2,016 | Computation and Language |
Knowledge Questions from Knowledge Graphs | We address the novel problem of automatically generating quiz-style knowledge
questions from a knowledge graph such as DBpedia. Questions of this kind have
ample applications, for instance, to educate users about or to evaluate their
knowledge in a specific domain. To solve the problem, we propose an end-to-end
approach. The approach first selects a named entity from the knowledge graph as
an answer. It then generates a structured triple-pattern query, which yields
the answer as its sole result. If a multiple-choice question is desired, the
approach selects alternative answer options. Finally, our approach uses a
template-based method to verbalize the structured query and yield a natural
language question. A key challenge is estimating how difficult the generated
question is to human users. To do this, we make use of historical data from the
Jeopardy! quiz show and a semantically annotated Web-scale document collection,
engineer suitable features, and train a logistic regression classifier to
predict question difficulty. Experiments demonstrate the viability of our
overall approach.
| 2,017 | Computation and Language |
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large
Vocabulary Speech Recognition | We present results that show it is possible to build a competitive, greatly
simplified, large vocabulary continuous speech recognition system with whole
words as acoustic units. We model the output vocabulary of about 100,000 words
directly using deep bi-directional LSTM RNNs with CTC loss. The model is
trained on 125,000 hours of semi-supervised acoustic training data, which
enables us to alleviate the data sparsity problem for word models. We show that
the CTC word models work very well as an end-to-end all-neural speech
recognition model without the use of traditional context-dependent sub-word
phone units that require a pronunciation lexicon, and without any language
model removing the need to decode. We demonstrate that the CTC word models
perform better than a strong, more complex, state-of-the-art baseline with
sub-word units.
| 2,016 | Computation and Language |
Generating Sentiment Lexicons for German Twitter | Despite a substantial progress made in developing new sentiment lexicon
generation (SLG) methods for English, the task of transferring these approaches
to other languages and domains in a sound way still remains open. In this
paper, we contribute to the solution of this problem by systematically
comparing semi-automatic translations of common English polarity lists with the
results of the original automatic SLG algorithms, which were applied directly
to German data. We evaluate these lexicons on a corpus of 7,992 manually
annotated tweets. In addition to that, we also collate the results of
dictionary- and corpus-based SLG methods in order to find out which of these
paradigms is better suited for the inherently noisy domain of social media. Our
experiments show that semi-automatic translations notably outperform automatic
systems (reaching a macro-averaged F1-score of 0.589), and that
dictionary-based techniques produce much better polarity lists as compared to
corpus-based approaches (whose best F1-scores run up to 0.479 and 0.419
respectively) even for the non-standard Twitter genre.
| 2,016 | Computation and Language |
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension | This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading
comprehension (RC) model that is able to extract and rank a set of answer
candidates from a given document to answer questions. DCR is able to predict
answers of variable lengths, whereas previous neural RC models primarily
focused on predicting single tokens or entities. DCR encodes a document and an
input question with recurrent neural networks, and then applies a word-by-word
attention mechanism to acquire question-aware representations for the document,
followed by the generation of chunk representations and a ranking module to
propose the top-ranked chunk as the answer. Experimental results show that DCR
achieves state-of-the-art exact match and F1 scores on the SQuAD dataset.
| 2,016 | Computation and Language |
Neural Machine Translation in Linear Time | We present a novel neural network for processing sequences. The ByteNet is a
one-dimensional convolutional neural network that is composed of two parts, one
to encode the source sequence and the other to decode the target sequence. The
two network parts are connected by stacking the decoder on top of the encoder
and preserving the temporal resolution of the sequences. To address the
differing lengths of the source and the target, we introduce an efficient
mechanism by which the decoder is dynamically unfolded over the representation
of the encoder. The ByteNet uses dilation in the convolutional layers to
increase its receptive field. The resulting network has two core properties: it
runs in time that is linear in the length of the sequences and it sidesteps the
need for excessive memorization. The ByteNet decoder attains state-of-the-art
performance on character-level language modelling and outperforms the previous
best results obtained with recurrent networks. The ByteNet also achieves
state-of-the-art performance on character-to-character machine translation on
the English-to-German WMT translation task, surpassing comparable neural
translation models that are based on recurrent networks with attentional
pooling and run in quadratic time. We find that the latent alignment structure
contained in the representations reflects the expected alignment between the
tokens.
| 2,017 | Computation and Language |
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with
Weak Supervision | Harnessing the statistical power of neural networks to perform language
understanding and symbolic reasoning is difficult, when it requires executing
efficient discrete operations against a large knowledge-base. In this work, we
introduce a Neural Symbolic Machine, which contains (a) a neural "programmer",
i.e., a sequence-to-sequence model that maps language utterances to programs
and utilizes a key-variable memory to handle compositionality (b) a symbolic
"computer", i.e., a Lisp interpreter that performs program execution, and helps
find good programs by pruning the search space. We apply REINFORCE to directly
optimize the task reward of this structured prediction problem. To train with
weak supervision and improve the stability of REINFORCE, we augment it with an
iterative maximum-likelihood training process. NSM outperforms the
state-of-the-art on the WebQuestionsSP dataset when trained from
question-answer pairs only, without requiring any feature engineering or
domain-specific knowledge.
| 2,017 | Computation and Language |
CBAS: context based arabic stemmer | Arabic morphology encapsulates many valuable features such as word root.
Arabic roots are being utilized for many tasks; the process of extracting a
word root is referred to as stemming. Stemming is an essential part of most
Natural Language Processing tasks, especially for derivative languages such as
Arabic. However, stemming is faced with the problem of ambiguity, where two or
more roots could be extracted from the same word. On the other hand,
distributional semantics is a powerful co-occurrence model. It captures the
meaning of a word based on its context. In this paper, a distributional
semantics model utilizing Smoothed Pointwise Mutual Information (SPMI) is
constructed to investigate its effectiveness on the stemming analysis task. It
showed an accuracy of 81.5%, with a at least 9.4% improvement over other
stemmers.
| 2,015 | Computation and Language |
RNN Approaches to Text Normalization: A Challenge | This paper presents a challenge to the community: given a large corpus of
written text aligned to its normalized spoken form, train an RNN to learn the
correct normalization function. We present a data set of general text where the
normalizations were generated using an existing text normalization component of
a text-to-speech system. This data set will be released open-source in the near
future.
We also present our own experiments with this data set with a variety of
different RNN architectures. While some of the architectures do in fact produce
very good results when measured in terms of overall accuracy, the errors that
are produced are problematic, since they would convey completely the wrong
message if such a system were deployed in a speech application. On the other
hand, we show that a simple FST-based filter can mitigate those errors, and
achieve a level of accuracy not achievable by the RNN alone.
Though our conclusions are largely negative on this point, we are actually
not arguing that the text normalization problem is intractable using an pure
RNN approach, merely that it is not going to be something that can be solved
merely by having huge amounts of annotated text data and feeding that to a
general RNN model. And when we open-source our data, we will be providing a
novel data set for sequence-to-sequence modeling in the hopes that the the
community can find better solutions.
The data used in this work have been released and are available at:
https://github.com/rwsproat/text-normalization-data
| 2,017 | Computation and Language |
Improving Twitter Sentiment Classification via Multi-Level
Sentiment-Enriched Word Embeddings | Most of existing work learn sentiment-specific word representation for
improving Twitter sentiment classification, which encoded both n-gram and
distant supervised tweet sentiment information in learning process. They assume
all words within a tweet have the same sentiment polarity as the whole tweet,
which ignores the word its own sentiment polarity. To address this problem, we
propose to learn sentiment-specific word embedding by exploiting both lexicon
resource and distant supervised information. We develop a multi-level
sentiment-enriched word embedding learning method, which uses parallel
asymmetric neural network to model n-gram, word level sentiment and tweet level
sentiment in learning process. Experiments on standard benchmarks show our
approach outperforms state-of-the-art methods.
| 2,018 | Computation and Language |
Dual Learning for Machine Translation | While neural machine translation (NMT) is making good progress in the past
two years, tens of millions of bilingual sentence pairs are needed for its
training. However, human labeling is very costly. To tackle this training data
bottleneck, we develop a dual-learning mechanism, which can enable an NMT
system to automatically learn from unlabeled data through a dual-learning game.
This mechanism is inspired by the following observation: any machine
translation task has a dual task, e.g., English-to-French translation (primal)
versus French-to-English translation (dual); the primal and dual tasks can form
a closed loop, and generate informative feedback signals to train the
translation models, even if without the involvement of a human labeler. In the
dual-learning mechanism, we use one agent to represent the model for the primal
task and the other agent to represent the model for the dual task, then ask
them to teach each other through a reinforcement learning process. Based on the
feedback signals generated during this process (e.g., the language-model
likelihood of the output of a model, and the reconstruction error of the
original sentence after the primal and dual translations), we can iteratively
update the two models until convergence (e.g., using the policy gradient
methods). We call the corresponding approach to neural machine translation
\emph{dual-NMT}. Experiments show that dual-NMT works very well on
English$\leftrightarrow$French translation; especially, by learning from
monolingual data (with 10% bilingual data for warm start), it achieves a
comparable accuracy to NMT trained from the full bilingual data for the
French-to-English translation task.
| 2,016 | Computation and Language |
Recurrent Neural Network Language Model Adaptation Derived Document
Vector | In many natural language processing (NLP) tasks, a document is commonly
modeled as a bag of words using the term frequency-inverse document frequency
(TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature
vector is that it ignores word orders that carry syntactic and semantic
relationships among the words in a document, and they can be important in some
NLP tasks such as genre classification. This paper proposes a novel distributed
vector representation of a document: a simple recurrent-neural-network language
model (RNN-LM) or a long short-term memory RNN language model (LSTM-LM) is
first created from all documents in a task; some of the LM parameters are then
adapted by each document, and the adapted parameters are vectorized to
represent the document. The new document vectors are labeled as DV-RNN and
DV-LSTM respectively. We believe that our new document vectors can capture some
high-level sequential information in the documents, which other current
document representations fail to capture. The new document vectors were
evaluated in the genre classification of documents in three corpora: the Brown
Corpus, the BNC Baby Corpus and an artificially created Penn Treebank dataset.
Their classification performances are compared with the performance of TF-IDF
vector and the state-of-the-art distributed memory model of paragraph vector
(PV-DM). The results show that DV-LSTM significantly outperforms TF-IDF and
PV-DM in most cases, and combinations of the proposed document vectors with
TF-IDF or PV-DM may further improve performance.
| 2,016 | Computation and Language |
Faster decoding for subword level Phrase-based SMT between related
languages | A common and effective way to train translation systems between related
languages is to consider sub-word level basic units. However, this increases
the length of the sentences resulting in increased decoding time. The increase
in length is also impacted by the specific choice of data format for
representing the sentences as subwords. In a phrase-based SMT framework, we
investigate different choices of decoder parameters as well as data format and
their impact on decoding time and translation accuracy. We suggest best options
for these settings that significantly improve decoding time with little impact
on the translation accuracy.
| 2,016 | Computation and Language |
Towards Sub-Word Level Compositions for Sentiment Analysis of
Hindi-English Code Mixed Text | Sentiment analysis (SA) using code-mixed data from social media has several
applications in opinion mining ranging from customer satisfaction to social
campaign analysis in multilingual societies. Advances in this area are impeded
by the lack of a suitable annotated dataset. We introduce a Hindi-English
(Hi-En) code-mixed dataset for sentiment analysis and perform empirical
analysis comparing the suitability and performance of various state-of-the-art
SA methods in social media.
In this paper, we introduce learning sub-word level representations in LSTM
(Subword-LSTM) architecture instead of character-level or word-level
representations. This linguistic prior in our architecture enables us to learn
the information about sentiment value of important morphemes. This also seems
to work well in highly noisy text containing misspellings as shown in our
experiments which is demonstrated in morpheme-level feature maps learned by our
model. Also, we hypothesize that encoding this linguistic prior in the
Subword-LSTM architecture leads to the superior performance. Our system attains
accuracy 4-5% greater than traditional approaches on our dataset, and also
outperforms the available system for sentiment analysis in Hi-En code-mixed
text by 18%.
| 2,016 | Computation and Language |
Detecting Context Dependent Messages in a Conversational Environment | While automatic response generation for building chatbot systems has drawn a
lot of attention recently, there is limited understanding on when we need to
consider the linguistic context of an input text in the generation process. The
task is challenging, as messages in a conversational environment are short and
informal, and evidence that can indicate a message is context dependent is
scarce. After a study of social conversation data crawled from the web, we
observed that some characteristics estimated from the responses of messages are
discriminative for identifying context dependent messages. With the
characteristics as weak supervision, we propose using a Long Short Term Memory
(LSTM) network to learn a classifier. Our method carries out text
representation and classifier learning in a unified framework. Experimental
results show that the proposed method can significantly outperform baseline
methods on accuracy of classification.
| 2,016 | Computation and Language |
Ordinal Common-sense Inference | Humans have the capacity to draw common-sense inferences from natural
language: various things that are likely but not certain to hold based on
established discourse, and are rarely stated explicitly. We propose an
evaluation of automated common-sense inference based on an extension of
recognizing textual entailment: predicting ordinal human responses on the
subjective likelihood of an inference holding in a given context. We describe a
framework for extracting common-sense knowledge from corpora, which is then
used to construct a dataset for this ordinal entailment task. We train a neural
sequence-to-sequence model on this dataset, which we use to score and generate
possible inferences. Further, we annotate subsets of previously established
datasets via our ordinal annotation protocol in order to then analyze the
distinctions between these and what we have constructed.
| 2,017 | Computation and Language |
Fuzzy paraphrases in learning word representations with a lexicon | A synonym of a polysemous word is usually only the paraphrase of one sense
among many. When lexicons are used to improve vector-space word
representations, such paraphrases are unreliable and bring noise to the
vector-space. The prior works use a coefficient to adjust the overall learning
of the lexicons. They regard the paraphrases equally. In this paper, we propose
a novel approach that regards the paraphrases diversely to alleviate the
adverse effects of polysemy. We annotate each paraphrase with a degree of
reliability. The paraphrases are randomly eliminated according to the degrees
when our model learns word representations. In this way, our approach drops the
unreliable paraphrases, keeping more reliable paraphrases at the same time. The
experimental results show that the proposed method improves the word vectors.
Our approach is an attempt to address the polysemy problem keeping one vector
per word. It makes the approach easier to use than the conventional methods
that estimate multiple vectors for a word. Our approach also outperforms the
prior works in the experiments.
| 2,017 | Computation and Language |
A FOFE-based Local Detection Approach for Named Entity Recognition and
Mention Detection | In this paper, we study a novel approach for named entity recognition (NER)
and mention detection in natural language processing. Instead of treating NER
as a sequence labelling problem, we propose a new local detection approach,
which rely on the recent fixed-size ordinally forgetting encoding (FOFE) method
to fully encode each sentence fragment and its left/right contexts into a
fixed-size representation. Afterwards, a simple feedforward neural network is
used to reject or predict entity label for each individual fragment. The
proposed method has been evaluated in several popular NER and mention detection
tasks, including the CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016
Tri-lingual Entity Discovery and Linking (EDL) tasks. Our methods have yielded
pretty strong performance in all of these examined tasks. This local detection
approach has shown many advantages over the traditional sequence labelling
methods.
| 2,016 | Computation and Language |
An empirical study for Vietnamese dependency parsing | This paper presents an empirical comparison of different dependency parsers
for Vietnamese, which has some unusual characteristics such as copula drop and
verb serialization. Experimental results show that the neural network-based
parsers perform significantly better than the traditional parsers. We report
the highest parsing scores published to date for Vietnamese with the labeled
attachment score (LAS) at 73.53% and the unlabeled attachment score (UAS) at
80.66%.
| 2,016 | Computation and Language |
A Hybrid Approach to Word Sense Disambiguation Combining Supervised and
Unsupervised Learning | In this paper, we are going to find meaning of words based on distinct
situations. Word Sense Disambiguation is used to find meaning of words based on
live contexts using supervised and unsupervised approaches. Unsupervised
approaches use online dictionary for learning, and supervised approaches use
manual learning sets. Hand tagged data are populated which might not be
effective and sufficient for learning procedure. This limitation of information
is main flaw of the supervised approach. Our proposed approach focuses to
overcome the limitation using learning set which is enriched in dynamic way
maintaining new data. Trivial filtering method is utilized to achieve
appropriate training data. We introduce a mixed methodology having Modified
Lesk approach and Bag-of-Words having enriched bags using learning methods. Our
approach establishes the superiority over individual Modified Lesk and
Bag-of-Words approaches based on experimentation.
| 2,016 | Computation and Language |
CogALex-V Shared Task: ROOT18 | In this paper, we describe ROOT 18, a classifier using the scores of several
unsupervised distributional measures as features to discriminate between
semantically related and unrelated words, and then to classify the related
pairs according to their semantic relation (i.e. synonymy, antonymy, hypernymy,
part-whole meronymy). Our classifier participated in the CogALex-V Shared Task,
showing a solid performance on the first subtask, but a poor performance on the
second subtask. The low scores reported on the second subtask suggest that
distributional measures are not sufficient to discriminate between multiple
semantic relations at once.
| 2,016 | Computation and Language |
Binary Paragraph Vectors | Recently Le & Mikolov described two log-linear models, called Paragraph
Vector, that can be used to learn state-of-the-art distributed representations
of documents. Inspired by this work, we present Binary Paragraph Vector models:
simple neural networks that learn short binary codes for fast information
retrieval. We show that binary paragraph vectors outperform autoencoder-based
binary codes, despite using fewer bits. We also evaluate their precision in
transfer learning settings, where binary codes are inferred for documents
unrelated to the training corpus. Results from these experiments indicate that
binary paragraph vectors can capture semantics relevant for various
domain-specific documents. Finally, we present a model that simultaneously
learns short binary codes and longer, real-valued representations. This model
can be used to rapidly retrieve a short list of highly relevant documents from
a large document collection.
| 2,017 | Computation and Language |
Answering Complicated Question Intents Expressed in Decomposed Question
Sequences | Recent work in semantic parsing for question answering has focused on long
and complicated questions, many of which would seem unnatural if asked in a
normal conversation between two humans. In an effort to explore a
conversational QA setting, we present a more realistic task: answering
sequences of simple but inter-related questions. We collect a dataset of 6,066
question sequences that inquire about semi-structured tables from Wikipedia,
with 17,553 question-answer pairs in total. Existing QA systems face two major
problems when evaluated on our dataset: (1) handling questions that contain
coreferences to previous questions or answers, and (2) matching words or
phrases in a question to corresponding entries in the associated table. We
conclude by proposing strategies to handle both of these issues.
| 2,016 | Computation and Language |
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies | The success of long short-term memory (LSTM) neural networks in language
processing is typically attributed to their ability to capture long-distance
statistical regularities. Linguistic regularities are often sensitive to
syntactic structure; can such dependencies be captured by LSTMs, which do not
have explicit structural representations? We begin addressing this question
using number agreement in English subject-verb dependencies. We probe the
architecture's grammatical competence both using training objectives with an
explicit grammatical target (number prediction, grammaticality judgments) and
using language models. In the strongly supervised settings, the LSTM achieved
very high overall accuracy (less than 1% errors), but errors increased when
sequential and structural information conflicted. The frequency of such errors
rose sharply in the language-modeling setting. We conclude that LSTMs can
capture a non-trivial amount of grammatical structure given targeted
supervision, but stronger architectures may be required to further reduce
errors; furthermore, the language modeling signal is insufficient for capturing
syntax-sensitive dependencies, and should be supplemented with more direct
supervision if such dependencies need to be captured.
| 2,016 | Computation and Language |
Learning Recurrent Span Representations for Extractive Question
Answering | The reading comprehension task, that asks questions about a given evidence
document, is a central problem in natural language understanding. Recent
formulations of this task have typically focused on answer selection from a set
of candidates pre-defined manually or through the use of an external NLP
pipeline. However, Rajpurkar et al. (2016) recently released the SQuAD dataset
in which the answers can be arbitrary strings from the supplied text. In this
paper, we focus on this answer extraction task, presenting a novel model
architecture that efficiently builds fixed length representations of all spans
in the evidence document with a recurrent network. We show that scoring
explicit span representations significantly improves performance over other
approaches that factor the prediction into separate predictions about words or
start and end markers. Our approach improves upon the best published results of
Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.'s
baseline by > 50%.
| 2,017 | Computation and Language |
Morphological Inflection Generation with Hard Monotonic Attention | We present a neural model for morphological inflection generation which
employs a hard attention mechanism, inspired by the nearly-monotonic alignment
commonly found between the characters in a word and the characters in its
inflection. We evaluate the model on three previously studied morphological
inflection generation datasets and show that it provides state of the art
results in various setups compared to previous neural and non-neural
approaches. Finally we present an analysis of the continuous representations
learned by both the hard and soft attention \cite{bahdanauCB14} models for the
task, shedding some light on the features such models extract.
| 2,017 | Computation and Language |
Automated Generation of Multilingual Clusters for the Evaluation of
Distributed Representations | We propose a language-agnostic way of automatically generating sets of
semantically similar clusters of entities along with sets of "outlier"
elements, which may then be used to perform an intrinsic evaluation of word
embeddings in the outlier detection task. We used our methodology to create a
gold-standard dataset, which we call WikiSem500, and evaluated multiple
state-of-the-art embeddings. The results show a correlation between performance
on this dataset and performance on sentiment analysis.
| 2,017 | Computation and Language |
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks | Transfer and multi-task learning have traditionally focused on either a
single source-target pair or very few, similar tasks. Ideally, the linguistic
levels of morphology, syntax and semantics would benefit each other by being
trained in a single model. We introduce a joint many-task model together with a
strategy for successively growing its depth to solve increasingly complex
tasks. Higher layers include shortcut connections to lower-level task
predictions to reflect linguistic hierarchies. We use a simple regularization
term to allow for optimizing all model weights to improve one task's loss
without exhibiting catastrophic interference of the other tasks. Our single
end-to-end model obtains state-of-the-art or competitive results on five
different tasks from tagging, parsing, relatedness, and entailment tasks.
| 2,017 | Computation and Language |
Bidirectional Attention Flow for Machine Comprehension | Machine comprehension (MC), answering a query about a given context
paragraph, requires modeling complex interactions between the context and the
query. Recently, attention mechanisms have been successfully extended to MC.
Typically these methods use attention to focus on a small portion of the
context and summarize it with a fixed-size vector, couple attentions
temporally, and/or often form a uni-directional attention. In this paper we
introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage
hierarchical process that represents the context at different levels of
granularity and uses bi-directional attention flow mechanism to obtain a
query-aware context representation without early summarization. Our
experimental evaluations show that our model achieves the state-of-the-art
results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze
test.
| 2,018 | Computation and Language |
Dynamic Coattention Networks For Question Answering | Several deep learning models have been proposed for question answering.
However, due to their single-pass nature, they have no way to recover from
local maxima corresponding to incorrect answers. To address this problem, we
introduce the Dynamic Coattention Network (DCN) for question answering. The DCN
first fuses co-dependent representations of the question and the document in
order to focus on relevant parts of both. Then a dynamic pointing decoder
iterates over potential answer spans. This iterative procedure enables the
model to recover from initial local maxima corresponding to incorrect answers.
On the Stanford question answering dataset, a single DCN model improves the
previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains
80.4% F1.
| 2,018 | Computation and Language |
Reference-Aware Language Models | We propose a general class of language models that treat reference as an
explicit stochastic latent variable. This architecture allows models to create
mentions of entities and their attributes by accessing external databases
(required by, e.g., dialogue generation and recipe generation) and internal
state (required by, e.g. language models which are aware of coreference). This
facilitates the incorporation of information that can be accessed in
predictable locations in databases or discourse context, even when the targets
of the reference may be rare words. Experiments on three tasks shows our model
variants based on deterministic attention.
| 2,017 | Computation and Language |
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency | In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based
language model designed to directly capture the global semantic meaning
relating words in a document via latent topics. Because of their sequential
nature, RNNs are good at capturing the local structure of a word sequence -
both semantic and syntactic - but might face difficulty remembering long-range
dependencies. Intuitively, these long-range dependencies are of semantic
nature. In contrast, latent topic models are able to capture the global
underlying semantic structure of a document but do not account for word
ordering. The proposed TopicRNN model integrates the merits of RNNs and latent
topic models: it captures local (syntactic) dependencies using an RNN and
global (semantic) dependencies using latent topics. Unlike previous work on
contextual RNN language modeling, our model is learned end-to-end. Empirical
results on word prediction show that TopicRNN outperforms existing contextual
RNN baselines. In addition, TopicRNN can be used as an unsupervised feature
extractor for documents. We do this for sentiment analysis on the IMDB movie
review dataset and report an error rate of $6.28\%$. This is comparable to the
state-of-the-art $5.91\%$ resulting from a semi-supervised approach. Finally,
TopicRNN also yields sensible topics, making it a useful alternative to
document models such as latent Dirichlet allocation.
| 2,017 | Computation and Language |
Words or Characters? Fine-grained Gating for Reading Comprehension | Previous work combines word-level and character-level representations using
concatenation or scalar weighting, which is suboptimal for high-level tasks
like reading comprehension. We present a fine-grained gating mechanism to
dynamically combine word-level and character-level representations based on
properties of the words. We also extend the idea of fine-grained gating to
modeling the interaction between questions and paragraphs for reading
comprehension. Experiments show that our approach can improve the performance
on reading comprehension tasks, achieving new state-of-the-art results on the
Children's Book Test dataset. To demonstrate the generality of our gating
mechanism, we also show improved results on a social media tag prediction task.
| 2,017 | Computation and Language |
Deep Biaffine Attention for Neural Dependency Parsing | This paper builds off recent work from Kiperwasser & Goldberg (2016) using
neural attention in a simple graph-based dependency parser. We use a larger but
more thoroughly regularized parser than other recent BiLSTM-based approaches,
with biaffine classifiers to predict arcs and labels. Our parser gets state of
the art or near state of the art performance on standard treebanks for six
different languages, achieving 95.7% UAS and 94.1% LAS on the most popular
English PTB dataset. This makes it the highest-performing graph-based parser on
this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and
2.2%---and comparable to the highest performing transition-based parser
(Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show
which hyperparameter choices had a significant effect on parsing accuracy,
allowing us to achieve large gains over other graph-based approaches.
| 2,017 | Computation and Language |
A Compare-Aggregate Model for Matching Text Sequences | Many NLP tasks including machine comprehension, answer selection and text
entailment require the comparison between sequences. Matching the important
units between sequences is a key to solve these problems. In this paper, we
present a general "compare-aggregate" framework that performs word-level
matching followed by aggregation using Convolutional Neural Networks. We
particularly focus on the different comparison functions we can use to match
two vectors. We use four different datasets to evaluate the model. We find that
some simple comparison functions based on element-wise operations can work
better than standard neural network and neural tensor network.
| 2,016 | Computation and Language |
Domain Adaptation For Formant Estimation Using Deep Learning | In this paper we present a domain adaptation technique for formant estimation
using a deep network. We first train a deep learning network on a small read
speech dataset. We then freeze the parameters of the trained network and use
several different datasets to train an adaptation layer that makes the obtained
network universal in the sense that it works well for a variety of speakers and
speech domains with very different characteristics. We evaluated our adapted
network on three datasets, each of which has different speaker characteristics
and speech styles. The performance of our method compares favorably with
alternative methods for formant estimation.
| 2,016 | Computation and Language |
Hierarchical Question Answering for Long Documents | We present a framework for question answering that can efficiently scale to
longer documents while maintaining or even improving performance of
state-of-the-art models. While most successful approaches for reading
comprehension rely on recurrent neural networks (RNNs), running them over long
documents is prohibitively slow because it is difficult to parallelize over
sequences. Inspired by how people first skim the document, identify relevant
parts, and carefully read these parts to produce an answer, we combine a
coarse, fast model for selecting relevant sentences and a more expensive RNN
for producing the answer from those sentences. We treat sentence selection as a
latent variable trained jointly from the answer only using reinforcement
learning. Experiments demonstrate the state of the art performance on a
challenging subset of the Wikireading and on a new dataset, while speeding up
the model by 3.5x-6.7x.
| 2,017 | Computation and Language |
Latent Attention For If-Then Program Synthesis | Automatic translation from natural language descriptions into programs is a
longstanding challenging problem. In this work, we consider a simple yet
important sub-problem: translation from textual descriptions to If-Then
programs. We devise a novel neural network architecture for this task which we
train end-to-end. Specifically, we introduce Latent Attention, which computes
multiplicative weights for the words in the description in a two-stage process
with the goal of better leveraging the natural language structures that
indicate the relevant parts for predicting program elements. Our architecture
reduces the error rate by 28.57% compared to prior art. We also propose a
one-shot learning scenario of If-Then program synthesis and simulate it with
our existing dataset. We demonstrate a variation on the training procedure for
this scenario that outperforms the original procedure, significantly closing
the gap to the model trained with all data.
| 2,016 | Computation and Language |
Truth Discovery with Memory Network | Truth discovery is to resolve conflicts and find the truth from
multiple-source statements. Conventional methods mostly research based on the
mutual effect between the reliability of sources and the credibility of
statements, however, pay no attention to the mutual effect among the
credibility of statements about the same object. We propose memory network
based models to incorporate these two ideas to do the truth discovery. We use
feedforward memory network and feedback memory network to learn the
representation of the credibility of statements which are about the same
object. Specially, we adopt memory mechanism to learn source reliability and
use it through truth prediction. During learning models, we use multiple types
of data (categorical data and continuous data) by assigning different weights
automatically in the loss function based on their own effect on truth discovery
prediction. The experiment results show that the memory network based models
much outperform the state-of-the-art method and other baseline methods.
| 2,016 | Computation and Language |
Neural Machine Translation with Reconstruction | Although end-to-end Neural Machine Translation (NMT) has achieved remarkable
progress in the past two years, it suffers from a major drawback: translations
generated by NMT systems often lack of adequacy. It has been widely observed
that NMT tends to repeatedly translate some source words while mistakenly
ignoring other words. To alleviate this problem, we propose a novel
encoder-decoder-reconstructor framework for NMT. The reconstructor,
incorporated into the NMT model, manages to reconstruct the input source
sentence from the hidden layer of the output target sentence, to ensure that
the information in the source side is transformed to the target side as much as
possible. Experiments show that the proposed framework significantly improves
the adequacy of NMT output and achieves superior translation result over
state-of-the-art NMT and statistical MT systems.
| 2,016 | Computation and Language |
AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text
Classification | Recently deeplearning models have been shown to be capable of making
remarkable performance in sentences and documents classification tasks. In this
work, we propose a novel framework called AC-BLSTM for modeling sentences and
documents, which combines the asymmetric convolution neural network (ACNN) with
the Bidirectional Long Short-Term Memory network (BLSTM). Experiment results
demonstrate that our model achieves state-of-the-art results on five tasks,
including sentiment analysis, question type classification, and subjectivity
classification. In order to further improve the performance of AC-BLSTM, we
propose a semi-supervised learning framework called G-AC-BLSTM for text
classification by combining the generative model with AC-BLSTM.
| 2,017 | Computation and Language |
Keyphrase Annotation with Graph Co-Ranking | Keyphrase annotation is the task of identifying textual units that represent
the main content of a document. Keyphrase annotation is either carried out by
extracting the most important phrases from a document, keyphrase extraction, or
by assigning entries from a controlled domain-specific vocabulary, keyphrase
assignment. Assignment methods are generally more reliable. They provide
better-formed keyphrases, as well as keyphrases that do not occur in the
document. But they are often silent on the contrary of extraction methods that
do not depend on manually built resources. This paper proposes a new method to
perform both keyphrase extraction and keyphrase assignment in an integrated and
mutual reinforcing manner. Experiments have been carried out on datasets
covering different domains of humanities and social sciences. They show
statistically significant improvements compared to both keyphrase extraction
and keyphrase assignment state-of-the art methods.
| 2,016 | Computation and Language |
Presenting a New Dataset for the Timeline Generation Problem | The timeline generation task summarises an entity's biography by selecting
stories representing key events from a large pool of relevant documents. This
paper addresses the lack of a standard dataset and evaluative methodology for
the problem. We present and make publicly available a new dataset of 18,793
news articles covering 39 entities. For each entity, we provide a gold standard
timeline and a set of entity-related articles. We propose ROUGE as an
evaluation metric and validate our dataset by showing that top Google results
outperform straw-man baselines.
| 2,016 | Computation and Language |
:telephone::person::sailboat::whale::okhand:; or "Call me Ishmael" - How
do you translate emoji? | We report on an exploratory analysis of Emoji Dick, a project that leverages
crowdsourcing to translate Melville's Moby Dick into emoji. This distinctive
use of emoji removes textual context, and leads to a varying translation
quality. In this paper, we use statistical word alignment and part-of-speech
tagging to explore how people use emoji. Despite these simple methods, we
observed differences in token and part-of-speech distributions. Experiments
also suggest that semantics are preserved in the translation, and repetition is
more common in emoji.
| 2,016 | Computation and Language |
Building a comprehensive syntactic and semantic corpus of Chinese
clinical texts | Objective: To build a comprehensive corpus covering syntactic and semantic
annotations of Chinese clinical texts with corresponding annotation guidelines
and methods as well as to develop tools trained on the annotated corpus, which
supplies baselines for research on Chinese texts in the clinical domain.
Materials and methods: An iterative annotation method was proposed to train
annotators and to develop annotation guidelines. Then, by using annotation
quality assurance measures, a comprehensive corpus was built, containing
annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions,
and relations. Inter-annotator agreement (IAA) was calculated to evaluate the
annotation quality and a Chinese clinical text processing and information
extraction system (CCTPIES) was developed based on our annotated corpus.
Results: The syntactic corpus consists of 138 Chinese clinical documents with
47,424 tokens and 2553 full parsing trees, while the semantic corpus includes
992 documents that annotated 39,511 entities with their assertions and 7695
relations. IAA evaluation shows that this comprehensive corpus is of good
quality, and the system modules are effective.
Discussion: The annotated corpus makes a considerable contribution to natural
language processing (NLP) research into Chinese texts in the clinical domain.
However, this corpus has a number of limitations. Some additional types of
clinical text should be introduced to improve corpus coverage and active
learning methods should be utilized to promote annotation efficiency.
Conclusions: In this study, several annotation guidelines and an annotation
method for Chinese clinical texts were proposed, and a comprehensive corpus
with its NLP modules were constructed, providing a foundation for further study
of applying NLP techniques to Chinese texts in the clinical domain.
| 2,016 | Computation and Language |
A Convolutional Encoder Model for Neural Machine Translation | The prevalent approach to neural machine translation relies on bi-directional
LSTMs to encode the source sentence. In this paper we present a faster and
simpler architecture based on a succession of convolutional layers. This allows
to encode the entire source sentence simultaneously compared to recurrent
networks for which computation is constrained by temporal dependencies. On
WMT'16 English-Romanian translation we achieve competitive accuracy to the
state-of-the-art and we outperform several recently published results on the
WMT'15 English-German task. Our models obtain almost the same accuracy as a
very deep LSTM setup on WMT'14 English-French translation. Our convolutional
encoder speeds up CPU decoding by more than two times at the same or higher
accuracy as a strong bi-directional LSTM baseline.
| 2,017 | Computation and Language |
Cruciform: Solving Crosswords with Natural Language Processing | Crossword puzzles are popular word games that require not only a large
vocabulary, but also a broad knowledge of topics. Answering each clue is a
natural language task on its own as many clues contain nuances, puns, or
counter-intuitive word definitions. Additionally, it can be extremely difficult
to ascertain definitive answers without the constraints of the crossword grid
itself. This task is challenging for both humans and computers. We describe
here a new crossword solving system, Cruciform. We employ a group of natural
language components, each of which returns a list of candidate words with
scores when given a clue. These lists are used in conjunction with the fill
intersections in the puzzle grid to formulate a constraint satisfaction
problem, in a manner similar to the one used in the Dr. Fill system. We
describe the results of several of our experiments with the system.
| 2,016 | Computation and Language |
Dependency Sensitive Convolutional Neural Networks for Modeling
Sentences and Documents | The goal of sentence and document modeling is to accurately represent the
meaning of sentences and documents for various Natural Language Processing
tasks. In this work, we present Dependency Sensitive Convolutional Neural
Networks (DSCNN) as a general-purpose classification system for both sentences
and documents. DSCNN hierarchically builds textual representations by
processing pretrained word embeddings via Long Short-Term Memory networks and
subsequently extracting features with convolution operators. Compared with
existing recursive neural models with tree structures, DSCNN does not rely on
parsers and expensive phrase labeling, and thus is not restricted to
sentence-level tasks. Moreover, unlike other CNN-based models that analyze
sentences locally by sliding windows, our system captures both the dependency
information within each sentence and relationships across sentences in the same
document. Experiment results demonstrate that our approach is achieving
state-of-the-art performance on several tasks, including sentiment analysis,
question type classification, and subjectivity classification.
| 2,016 | Computation and Language |
A Surrogate-based Generic Classifier for Chinese TV Series Reviews | With the emerging of various online video platforms like Youtube, Youku and
LeTV, online TV series' reviews become more and more important both for viewers
and producers. Customers rely heavily on these reviews before selecting TV
series, while producers use them to improve the quality. As a result,
automatically classifying reviews according to different requirements evolves
as a popular research topic and is essential in our daily life. In this paper,
we focused on reviews of hot TV series in China and successfully trained
generic classifiers based on eight predefined categories. The experimental
results showed promising performance and effectiveness of its generalization to
different TV series.
| 2,016 | Computation and Language |
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based
Approaches | Acoustic word embeddings --- fixed-dimensional vector representations of
variable-length spoken word segments --- have begun to be considered for tasks
such as speech recognition and query-by-example search. Such embeddings can be
learned discriminatively so that they are similar for speech segments
corresponding to the same word, while being dissimilar for segments
corresponding to different words. Recent work has found that acoustic word
embeddings can outperform dynamic time warping on query-by-example search and
related word discrimination tasks. However, the space of embedding models and
training approaches is still relatively unexplored. In this paper we present
new discriminative embedding models based on recurrent neural networks (RNNs).
We consider training losses that have been successful in prior work, in
particular a cross entropy loss for word classification and a contrastive loss
that explicitly aims to separate same-word and different-word pairs in a
"Siamese network" training setting. We find that both classifier-based and
Siamese RNN embeddings improve over previously reported results on a word
discrimination task, with Siamese RNNs outperforming classification models. In
addition, we present analyses of the learned embeddings and the effects of
variables such as dimensionality and network structure.
| 2,016 | Computation and Language |
The Neural Noisy Channel | We formulate sequence to sequence transduction as a noisy channel decoding
problem and use recurrent neural networks to parameterise the source and
channel models. Unlike direct models which can suffer from explaining-away
effects during training, noisy channel models must produce outputs that explain
their inputs, and their component models can be trained with not only paired
training samples but also unpaired samples from the marginal output
distribution. Using a latent variable to control how much of the conditioning
sequence the channel model needs to read in order to generate a subsequent
symbol, we obtain a tractable and effective beam search decoder. Experimental
results on abstractive sentence summarisation, morphological inflection, and
machine translation show that noisy channel models outperform direct models,
and that they significantly benefit from increased amounts of unpaired output
data that direct models cannot easily use.
| 2,017 | Computation and Language |
Contradiction Detection for Rumorous Claims | The utilization of social media material in journalistic workflows is
increasing, demanding automated methods for the identification of mis- and
disinformation. Since textual contradiction across social media posts can be a
signal of rumorousness, we seek to model how claims in Twitter posts are being
textually contradicted. We identify two different contexts in which
contradiction emerges: its broader form can be observed across independently
posted tweets and its more specific form in threaded conversations. We define
how the two scenarios differ in terms of central elements of argumentation:
claims and conversation structure. We design and evaluate models for the two
scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to
represent claims and conversation structure implicitly in a generic inference
model, while previous studies used explicit or no representation of these
properties. To address noisy text, our classifiers use simple similarity
features derived from the string and part-of-speech level. Corpus statistics
reveal distribution differences for these features in contradictory as opposed
to non-contradictory tweet relations, and the classifiers yield state of the
art performance.
| 2,016 | Computation and Language |
Veracity Computing from Lexical Cues and Perceived Certainty Trends | We present a data-driven method for determining the veracity of a set of
rumorous claims on social media data. Tweets from different sources pertaining
to a rumor are processed on three levels: first, factuality values are assigned
to each tweet based on four textual cue categories relevant for our journalism
use case; these amalgamate speaker support in terms of polarity and commitment
in terms of certainty and speculation. Next, the proportions of these lexical
cues are utilized as predictors for tweet certainty in a generalized linear
regression model. Subsequently, lexical cue proportions, predicted certainty,
as well as their time course characteristics are used to compute veracity for
each rumor in terms of the identity of the rumor-resolving tweet and its binary
resolution value judgment. The system operates without access to
extralinguistic resources. Evaluated on the data portion for which hand-labeled
examples were available, it achieves .74 F1-score on identifying rumor
resolving tweets and .76 F1-score on predicting if a rumor is resolved as true
or false.
| 2,016 | Computation and Language |
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks | Modeling the structure of coherent texts is a key NLP problem. The task of
coherently organizing a given set of sentences has been commonly used to build
and evaluate models that understand such structure. We propose an end-to-end
unsupervised deep learning approach based on the set-to-sequence framework to
address this problem. Our model strongly outperforms prior methods in the order
discrimination task and a novel task of ordering abstracts from scientific
articles. Furthermore, our work shows that useful text representations can be
obtained by learning to order sentences. Visualizing the learned sentence
representations shows that the model captures high-level logical structure in
paragraphs. Our representations perform comparably to state-of-the-art
pre-training methods on sentence similarity and paraphrase detection tasks.
| 2,017 | Computation and Language |
Unsupervised Pretraining for Sequence to Sequence Learning | This work presents a general unsupervised learning method to improve the
accuracy of sequence to sequence (seq2seq) models. In our method, the weights
of the encoder and decoder of a seq2seq model are initialized with the
pretrained weights of two language models and then fine-tuned with labeled
data. We apply this method to challenging benchmarks in machine translation and
abstractive summarization and find that it significantly improves the
subsequent supervised models. Our main result is that pretraining improves the
generalization of seq2seq models. We achieve state-of-the art results on the
WMT English$\rightarrow$German task, surpassing a range of methods using both
phrase-based machine translation and neural machine translation. Our method
achieves a significant improvement of 1.3 BLEU from the previous best models on
both WMT'14 and WMT'15 English$\rightarrow$German. We also conduct human
evaluations on abstractive summarization and find that our method outperforms a
purely supervised learning baseline in a statistically significant manner.
| 2,018 | Computation and Language |
Automatic recognition of child speech for robotic applications in noisy
environments | Automatic speech recognition (ASR) allows a natural and intuitive interface
for robotic educational applications for children. However there are a number
of challenges to overcome to allow such an interface to operate robustly in
realistic settings, including the intrinsic difficulties of recognising child
speech and high levels of background noise often present in classrooms. As part
of the EU EASEL project we have provided several contributions to address these
challenges, implementing our own ASR module for use in robotics applications.
We used the latest deep neural network algorithms which provide a leap in
performance over the traditional GMM approach, and apply data augmentation
methods to improve robustness to noise and speaker variation. We provide a
close integration between the ASR module and the rest of the dialogue system,
allowing the ASR to receive in real-time the language models relevant to the
current section of the dialogue, greatly improving the accuracy. We integrated
our ASR module into an interactive, multimodal system using a small humanoid
robot to help children learn about exercise and energy. The system was
installed at a public museum event as part of a research study where 320
children (aged 3 to 14) interacted with the robot, with our ASR achieving 90%
accuracy for fluent and near-fluent speech.
| 2,016 | Computation and Language |
Old Content and Modern Tools - Searching Named Entities in a Finnish
OCRed Historical Newspaper Collection 1771-1910 | Named Entity Recognition (NER), search, classification and tagging of names
and name like frequent informational elements in texts, has become a standard
information extraction procedure for textual data. NER has been applied to many
types of texts and different types of entities: newspapers, fiction, historical
records, persons, locations, chemical compounds, protein families, animals etc.
In general a NER system's performance is genre and domain dependent and also
used entity categories vary (Nadeau and Sekine, 2007). The most general set of
named entities is usually some version of three partite categorization of
locations, persons and organizations. In this paper we report first large scale
trials and evaluation of NER with data out of a digitized Finnish historical
newspaper collection Digi. Experiments, results and discussion of this research
serve development of the Web collection of historical Finnish newspapers.
Digi collection contains 1,960,921 pages of newspaper material from years
1771-1910 both in Finnish and Swedish. We use only material of Finnish
documents in our evaluation. The OCRed newspaper collection has lots of OCR
errors; its estimated word level correctness is about 70-75 % (Kettunen and
P\"a\"akk\"onen, 2016). Our principal NER tagger is a rule-based tagger of
Finnish, FiNER, provided by the FIN-CLARIN consortium. We show also results of
limited category semantic tagging with tools of the Semantic Computing Research
Group (SeCo) of the Aalto University. Three other tools are also evaluated
briefly.
This research reports first published large scale results of NER in a
historical Finnish OCRed newspaper collection. Results of the research
supplement NER results of other languages with similar noisy data.
| 2,016 | Computation and Language |
Increasing the throughput of machine translation systems using clouds | The manuscript presents an experiment at implementation of a Machine
Translation system in a MapReduce model. The empirical evaluation was done
using fully implemented translation systems embedded into the MapReduce
programming model. Two machine translation paradigms were studied: shallow
transfer Rule Based Machine Translation and Statistical Machine Translation.
The results show that the MapReduce model can be successfully used to
increase the throughput of a machine translation system. Furthermore this
method enhances the throughput of a machine translation system without
decreasing the quality of the translation output.
Thus, the present manuscript also represents a contribution to the seminal
work in natural language processing, specifically Machine Translation. It first
points toward the importance of the definition of the metric of throughput of
translation system and, second, the applicability of the machine translation
task to the MapReduce paradigm.
| 2,016 | Computation and Language |
A Comparison of Word Embeddings for English and Cross-Lingual Chinese
Word Sense Disambiguation | Word embeddings are now ubiquitous forms of word representation in natural
language processing. There have been applications of word embeddings for
monolingual word sense disambiguation (WSD) in English, but few comparisons
have been done. This paper attempts to bridge that gap by examining popular
embeddings for the task of monolingual English WSD. Our simplified method leads
to comparable state-of-the-art performance without expensive retraining.
Cross-Lingual WSD - where the word senses of a word in a source language e come
from a separate target translation language f - can also assist in language
learning; for example, when providing translations of target vocabulary for
learners. Thus we have also applied word embeddings to the novel task of
cross-lingual WSD for Chinese and provide a public dataset for further
benchmarking. We have also experimented with using word embeddings for LSTM
networks and found surprisingly that a basic LSTM network does not work well.
We discuss the ramifications of this outcome.
| 2,016 | Computation and Language |
Distant supervision for emotion detection using Facebook reactions | We exploit the Facebook reaction feature in a distant supervised fashion to
train a support vector machine classifier for emotion detection, using several
feature combinations and combining different Facebook pages. We test our models
on existing benchmarks for emotion detection and show that employing only
information that is derived completely automatically, thus without relying on
any handcrafted lexicon as it's usually done, we can achieve competitive
results. The results also show that there is large room for improvement,
especially by gearing the collection of Facebook pages, with a view to the
target domain.
| 2,016 | Computation and Language |
When silver glitters more than gold: Bootstrapping an Italian
part-of-speech tagger for Twitter | We bootstrap a state-of-the-art part-of-speech tagger to tag Italian Twitter
data, in the context of the Evalita 2016 PoSTWITA shared task. We show that
training the tagger on native Twitter data enriched with little amounts of
specifically selected gold data and additional silver-labelled data scraped
from Facebook, yields better results than using large amounts of manually
annotated data from a mix of genres.
| 2,016 | Computation and Language |
Tracing metaphors in time through self-distance in vector spaces | From a diachronic corpus of Italian, we build consecutive vector spaces in
time and use them to compare a term's cosine similarity to itself in different
time spans. We assume that a drop in similarity might be related to the
emergence of a metaphorical sense at a given time. Similarity-based
observations are matched to the actual year when a figurative meaning was
documented in a reference dictionary and through manual inspection of corpus
occurrences.
| 2,016 | Computation and Language |
Efficient Summarization with Read-Again and Copy Mechanism | Encoder-decoder models have been widely used to solve sequence to sequence
prediction tasks. However current approaches suffer from two shortcomings.
First, the encoders compute a representation of each word taking into account
only the history of the words it has read so far, yielding suboptimal
representations. Second, current decoders utilize large vocabularies in order
to minimize the problem of unknown words, resulting in slow decoding times. In
this paper we address both shortcomings. Towards this goal, we first introduce
a simple mechanism that first reads the input sequence before committing to a
representation of each word. Furthermore, we propose a simple copy mechanism
that is able to exploit very small vocabularies and handle out-of-vocabulary
words. We demonstrate the effectiveness of our approach on the Gigaword dataset
and DUC competition outperforming the state-of-the-art.
| 2,016 | Computation and Language |
Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory
Detection: A Natural Language Processing Approach | The aim of syntactic tracking is to classify spatio-temporal patterns of a
target's motion using natural language processing models. In this paper, we
generalize earlier work by considering a constrained stochastic context free
grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained
grammar facilitates modeling specific directions and road names in a roadmap.
We present a novel particle filtering algorithm that exploits the CSCFG model
for estimating the target's patterns. This meta-level algorithm operates in
conjunction with a base-level tracking algorithm. Extensive numerical results
using simulated ground moving target indicator (GMTI) radar measurements show
substantial improvement in target tracking accuracy.
| 2,018 | Computation and Language |
Landmark-based consonant voicing detection on multilingual corpora | This paper tests the hypothesis that distinctive feature classifiers anchored
at phonetic landmarks can be transferred cross-lingually without loss of
accuracy. Three consonant voicing classifiers were developed: (1) manually
selected acoustic features anchored at a phonetic landmark, (2) MFCCs (either
averaged across the segment or anchored at the landmark), and(3) acoustic
features computed using a convolutional neural network (CNN). All detectors are
trained on English data (TIMIT),and tested on English, Turkish, and Spanish
(performance measured using F1 and accuracy). Experiments demonstrate that
manual features outperform all MFCC classifiers, while CNNfeatures outperform
both. MFCC-based classifiers suffer an F1reduction of 16% absolute when
generalized from English to other languages. Manual features suffer only a 5%
F1 reduction,and CNN features actually perform better in Turkish and Span-ish
than in the training language, demonstrating that features capable of
representing long-term spectral dynamics (CNN and landmark-based features) are
able to generalize cross-lingually with little or no loss of accuracy
| 2,017 | Computation and Language |
Neural Networks Models for Entity Discovery and Linking | This paper describes the USTC_NELSLIP systems submitted to the Trilingual
Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population
(KBP) contests. We have built two systems for entity discovery and mention
detection (MD): one uses the conditional RNNLM and the other one uses the
attention-based encoder-decoder framework. The entity linking (EL) system
consists of two modules: a rule based candidate generation and a neural
networks probability ranking model. Moreover, some simple string matching rules
are used for NIL clustering. At the end, our best system has achieved an F1
score of 0.624 in the end-to-end typed mention ceaf plus metric.
| 2,016 | Computation and Language |
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media
Text | Most neural network models for document classification on social media focus
on text infor-mation to the neglect of other information on these platforms. In
this paper, we classify post stance on social media channels and develop UTCNN,
a neural network model that incorporates user tastes, topic tastes, and user
comments on posts. UTCNN not only works on social media texts, but also
analyzes texts in forums and message boards. Experiments performed on Chinese
Facebook data and English online debate forum data show that UTCNN achieves a
0.755 macro-average f-score for supportive, neutral, and unsupportive stance
classes on Facebook data, which is significantly better than models in which
either user, topic, or comment information is withheld. This model design
greatly mitigates the lack of data for the minor class without the use of
oversampling. In addition, UTCNN yields a 0.842 accuracy on English online
debate forum data, which also significantly outperforms results from previous
work as well as other deep learning models, showing that UTCNN performs well
regardless of language or platform.
| 2,016 | Computation and Language |
Improving Reliability of Word Similarity Evaluation by Redesigning
Annotation Task and Performance Measure | We suggest a new method for creating and using gold-standard datasets for
word similarity evaluation. Our goal is to improve the reliability of the
evaluation, and we do this by redesigning the annotation task to achieve higher
inter-rater agreement, and by defining a performance measure which takes the
reliability of each annotation decision in the dataset into account.
| 2,017 | Computation and Language |
Training IBM Watson using Automatically Generated Question-Answer Pairs | IBM Watson is a cognitive computing system capable of question answering in
natural languages. It is believed that IBM Watson can understand large corpora
and answer relevant questions more effectively than any other
question-answering system currently available. To unleash the full power of
Watson, however, we need to train its instance with a large number of
well-prepared question-answer pairs. Obviously, manually generating such pairs
in a large quantity is prohibitively time consuming and significantly limits
the efficiency of Watson's training. Recently, a large-scale dataset of over 30
million question-answer pairs was reported. Under the assumption that using
such an automatically generated dataset could relieve the burden of manual
question-answer generation, we tried to use this dataset to train an instance
of Watson and checked the training efficiency and accuracy. According to our
experiments, using this auto-generated dataset was effective for training
Watson, complementing manually crafted question-answer pairs. To the best of
the authors' knowledge, this work is the first attempt to use a large-scale
dataset of automatically generated question-answer pairs for training IBM
Watson. We anticipate that the insights and lessons obtained from our
experiments will be useful for researchers who want to expedite Watson training
leveraged by automatically generated question-answer pairs.
| 2,016 | Computation and Language |
Linguistically Regularized LSTMs for Sentiment Classification | Sentiment understanding has been a long-term goal of AI in the past decades.
This paper deals with sentence-level sentiment classification. Though a variety
of neural network models have been proposed very recently, however, previous
models either depend on expensive phrase-level annotation, whose performance
drops substantially when trained with only sentence-level annotation; or do not
fully employ linguistic resources (e.g., sentiment lexicons, negation words,
intensity words), thus not being able to produce linguistically coherent
representations. In this paper, we propose simple models trained with
sentence-level annotation, but also attempt to generating linguistically
coherent representations by employing regularizers that model the linguistic
role of sentiment lexicons, negation words, and intensity words. Results show
that our models are effective to capture the sentiment shifting effect of
sentiment, negation, and intensity words, while still obtain competitive
results without sacrificing the models' simplicity.
| 2,017 | Computation and Language |
Multi-Language Identification Using Convolutional Recurrent Neural
Network | Language Identification, being an important aspect of Automatic Speaker
Recognition has had many changes and new approaches to ameliorate performance
over the last decade. We compare the performance of using audio spectrum in the
log scale and using Polyphonic sound sequences from raw audio samples to train
the neural network and to classify speech as either English or Spanish. To
achieve this, we use the novel approach of using a Convolutional Recurrent
Neural Network using Long Short Term Memory (LSTM) or a Gated Recurrent Unit
(GRU) for forward propagation of the neural network. Our hypothesis is that the
performance of using polyphonic sound sequence as features and both LSTM and
GRU as the gating mechanisms for the neural network outperform the traditional
MFCC features using a unidirectional Deep Neural Network.
| 2,017 | Computation and Language |
1.5 billion words Arabic Corpus | This study is an attempt to build a contemporary linguistic corpus for Arabic
language. The corpus produced, is a text corpus includes more than five million
newspaper articles. It contains over a billion and a half words in total, out
of which, there is about three million unique words. The data were collected
from newspaper articles in ten major news sources from eight Arabic countries,
over a period of fourteen years. The corpus was encoded with two types of
encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two
mark-up languages, namely: SGML, and XML.
| 2,016 | Computation and Language |
Semi-automatic Simultaneous Interpreting Quality Evaluation | Increasing interpreting needs a more objective and automatic measurement. We
hold a basic idea that 'translating means translating meaning' in that we can
assessment interpretation quality by comparing the meaning of the interpreting
output with the source input. That is, a translation unit of a 'chunk' named
Frame which comes from frame semantics and its components named Frame Elements
(FEs) which comes from Frame Net are proposed to explore their matching rate
between target and source texts. A case study in this paper verifies the
usability of semi-automatic graded semantic-scoring measurement for human
simultaneous interpreting and shows how to use frame and FE matches to score.
Experiments results show that the semantic-scoring metrics have a significantly
correlation coefficient with human judgment.
| 2,016 | Computation and Language |
Cross-lingual Dataless Classification for Languages with Small Wikipedia
Presence | This paper presents an approach to classify documents in any language into an
English topical label space, without any text categorization training data. The
approach, Cross-Lingual Dataless Document Classification (CLDDC) relies on
mapping the English labels or short category description into a Wikipedia-based
semantic representation, and on the use of the target language Wikipedia.
Consequently, performance could suffer when Wikipedia in the target language is
small. In this paper, we focus on languages with small Wikipedias,
(Small-Wikipedia languages, SWLs). We use a word-level dictionary to convert
documents in a SWL to a large-Wikipedia language (LWLs), and then perform CLDDC
based on the LWL's Wikipedia. This approach can be applied to thousands of
languages, which can be contrasted with machine translation, which is a
supervision heavy approach and can be done for about 100 languages. We also
develop a ranking algorithm that makes use of language similarity metrics to
automatically select a good LWL, and show that this significantly improves
classification of SWLs' documents, performing comparably to the best bridge
possible.
| 2,016 | Computation and Language |
Joint Representation Learning of Text and Knowledge for Knowledge Graph
Completion | Joint representation learning of text and knowledge within a unified semantic
space enables us to perform knowledge graph completion more accurately. In this
work, we propose a novel framework to embed words, entities and relations into
the same continuous vector space. In this model, both entity and relation
embeddings are learned by taking knowledge graph and plain text into
consideration. In experiments, we evaluate the joint learning model on three
tasks including entity prediction, relation prediction and relation
classification from text. The experiment results show that our model can
significantly and consistently improve the performance on the three tasks as
compared with other baselines.
| 2,016 | Computation and Language |
SummaRuNNer: A Recurrent Neural Network based Sequence Model for
Extractive Summarization of Documents | We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model
for extractive summarization of documents and show that it achieves performance
better than or comparable to state-of-the-art. Our model has the additional
advantage of being very interpretable, since it allows visualization of its
predictions broken up by abstract features such as information content,
salience and novelty. Another novel contribution of our work is abstractive
training of our extractive model that can train on human generated reference
summaries alone, eliminating the need for sentence-level extractive labels.
| 2,016 | Computation and Language |
A New Recurrent Neural CRF for Learning Non-linear Edge Features | Conditional Random Field (CRF) and recurrent neural models have achieved
success in structured prediction. More recently, there is a marriage of CRF and
recurrent neural models, so that we can gain from both non-linear dense
features and globally normalized CRF objective. These recurrent neural CRF
models mainly focus on encode node features in CRF undirected graphs. However,
edge features prove important to CRF in structured prediction. In this work, we
introduce a new recurrent neural CRF model, which learns non-linear edge
features, and thus makes non-linear features encoded completely. We compare our
model with different neural models in well-known structured prediction tasks.
Experiments show that our model outperforms state-of-the-art methods in NP
chunking, shallow parsing, Chinese word segmentation and POS tagging.
| 2,016 | Computation and Language |
F-Score Driven Max Margin Neural Network for Named Entity Recognition in
Chinese Social Media | We focus on named entity recognition (NER) for Chinese social media. With
massive unlabeled text and quite limited labelled corpus, we propose a
semi-supervised learning model based on B-LSTM neural network. To take
advantage of traditional methods in NER such as CRF, we combine transition
probability with deep learning in our model. To bridge the gap between label
accuracy and F-score of NER, we construct a model which can be directly trained
on F-score. When considering the instability of F-score driven method and
meaningful information provided by label accuracy, we propose an integrated
method to train on both F-score and label accuracy. Our integrated model yields
7.44\% improvement over previous state-of-the-art result.
| 2,017 | Computation and Language |
Classify or Select: Neural Architectures for Extractive Document
Summarization | We present two novel and contrasting Recurrent Neural Network (RNN) based
architectures for extractive summarization of documents. The Classifier based
architecture sequentially accepts or rejects each sentence in the original
document order for its membership in the final summary. The Selector
architecture, on the other hand, is free to pick one sentence at a time in any
arbitrary order to piece together the summary. Our models under both
architectures jointly capture the notions of salience and redundancy of
sentences. In addition, these models have the advantage of being very
interpretable, since they allow visualization of their predictions broken up by
abstract features such as information content, salience and redundancy. We show
that our models reach or outperform state-of-the-art supervised models on two
different corpora. We also recommend the conditions under which one
architecture is superior to the other based on experimental evidence.
| 2,017 | Computation and Language |
`Who would have thought of that!': A Hierarchical Topic Model for
Extraction of Sarcasm-prevalent Topics and Sarcasm Detection | Topic Models have been reported to be beneficial for aspect-based sentiment
analysis. This paper reports a simple topic model for sarcasm detection, a
first, to the best of our knowledge. Designed on the basis of the intuition
that sarcastic tweets are likely to have a mixture of words of both sentiments
as against tweets with literal sentiment (either positive or negative), our
hierarchical topic model discovers sarcasm-prevalent topics and topic-level
sentiment. Using a dataset of tweets labeled using hashtags, the model
estimates topic-level, and sentiment-level distributions. Our evaluation shows
that topics such as `work', `gun laws', `weather' are sarcasm-prevalent topics.
Our model is also able to discover the mixture of sentiment-bearing words that
exist in a text of a given sentiment-related label. Finally, we apply our model
to predict sarcasm in tweets. We outperform two prior work based on statistical
classifiers with specific features, by around 25\%.
| 2,016 | Computation and Language |
Character-level Convolutional Network for Text Classification Applied to
Chinese Corpus | This article provides an interesting exploration of character-level
convolutional neural network solving Chinese corpus text classification
problem. We constructed a large-scale Chinese language dataset, and the result
shows that character-level convolutional neural network works better on Chinese
corpus than its corresponding pinyin format dataset. This is the first time
that character-level convolutional neural network applied to text
classification problem.
| 2,016 | Computation and Language |
Attending to Characters in Neural Sequence Labeling Models | Sequence labeling architectures use word embeddings for capturing similarity,
but suffer when handling previously unseen or rare words. We investigate
character-level extensions to such models and propose a novel architecture for
combining alternative word representations. By using an attention mechanism,
the model is able to dynamically decide how much information to use from a
word- or character-level component. We evaluated different architectures on a
range of sequence labeling datasets, and character-level extensions were found
to improve performance on every benchmark. In addition, the proposed
attention-based architecture delivered the best results even with a smaller
number of trainable parameters.
| 2,016 | Computation and Language |
Ranking medical jargon in electronic health record notes by adapted
distant supervision | Objective: Allowing patients to access their own electronic health record
(EHR) notes through online patient portals has the potential to improve
patient-centered care. However, medical jargon, which abounds in EHR notes, has
been shown to be a barrier for patient EHR comprehension. Existing knowledge
bases that link medical jargon to lay terms or definitions play an important
role in alleviating this problem but have low coverage of medical jargon in
EHRs. We developed a data-driven approach that mines EHRs to identify and rank
medical jargon based on its importance to patients, to support the building of
EHR-centric lay language resources.
Methods: We developed an innovative adapted distant supervision (ADS) model
based on support vector machines to rank medical jargon from EHRs. For distant
supervision, we utilized the open-access, collaborative consumer health
vocabulary, a large, publicly available resource that links lay terms to
medical jargon. We explored both knowledge-based features from the Unified
Medical Language System and distributed word representations learned from
unlabeled large corpora. We evaluated the ADS model using physician-identified
important medical terms.
Results: Our ADS model significantly surpassed two state-of-the-art automatic
term recognition methods, TF*IDF and C-Value, yielding 0.810 ROC-AUC versus
0.710 and 0.667, respectively. Our model identified 10K important medical
jargon terms after ranking over 100K candidate terms mined from over 7,500 EHR
narratives.
Conclusion: Our work is an important step towards enriching lexical resources
that link medical jargon to lay terms/definitions to support patient EHR
comprehension. The identified medical jargon terms and their rankings are
available upon request.
| 2,016 | Computation and Language |
Multi-view Recurrent Neural Acoustic Word Embeddings | Recent work has begun exploring neural acoustic word
embeddings---fixed-dimensional vector representations of arbitrary-length
speech segments corresponding to words. Such embeddings are applicable to
speech retrieval and recognition tasks, where reasoning about whole words may
make it possible to avoid ambiguous sub-word representations. The main idea is
to map acoustic sequences to fixed-dimensional vectors such that examples of
the same word are mapped to similar vectors, while different-word examples are
mapped to very different vectors. In this work we take a multi-view approach to
learning acoustic word embeddings, in which we jointly learn to embed acoustic
sequences and their corresponding character sequences. We use deep
bidirectional LSTM embedding models and multi-view contrastive losses. We study
the effect of different loss variants, including fixed-margin and
cost-sensitive losses. Our acoustic word embeddings improve over previous
approaches for the task of word discrimination. We also present results on
other tasks that are enabled by the multi-view approach, including cross-view
word discrimination and word similarity.
| 2,017 | Computation and Language |
Zero-resource Machine Translation by Multimodal Encoder-decoder Network
with Multimedia Pivot | We propose an approach to build a neural machine translation system with no
supervised resources (i.e., no parallel corpora) using multimodal embedded
representation over texts and images. Based on the assumption that text
documents are often likely to be described with other multimedia information
(e.g., images) somewhat related to the content, we try to indirectly estimate
the relevance between two languages. Using multimedia as the "pivot", we
project all modalities into one common hidden space where samples belonging to
similar semantic concepts should come close to each other, whatever the
observed space of each sample is. This modality-agnostic representation is the
key to bridging the gap between different modalities. Putting a decoder on top
of it, our network can flexibly draw the outputs from any input modality.
Notably, in the testing phase, we need only source language texts as the input
for translation. In experiments, we tested our method on two benchmarks to show
that it can achieve reasonable translation performance. We compared and
investigated several possible implementations and found that an end-to-end
model that simultaneously optimized both rank loss in multimodal encoders and
cross-entropy loss in decoders performed the best.
| 2,017 | Computation and Language |
Google's Multilingual Neural Machine Translation System: Enabling
Zero-Shot Translation | We propose a simple solution to use a single Neural Machine Translation (NMT)
model to translate between multiple languages. Our solution requires no change
in the model architecture from our base system but instead introduces an
artificial token at the beginning of the input sentence to specify the required
target language. The rest of the model, which includes encoder, decoder and
attention, remains unchanged and is shared across all languages. Using a shared
wordpiece vocabulary, our approach enables Multilingual NMT using a single
model without any increase in parameters, which is significantly simpler than
previous proposals for Multilingual NMT. Our method often improves the
translation quality of all involved language pairs, even while keeping the
total number of model parameters constant. On the WMT'14 benchmarks, a single
multilingual model achieves comparable performance for
English$\rightarrow$French and surpasses state-of-the-art results for
English$\rightarrow$German. Similarly, a single multilingual model surpasses
state-of-the-art results for French$\rightarrow$English and
German$\rightarrow$English on WMT'14 and WMT'15 benchmarks respectively. On
production corpora, multilingual models of up to twelve language pairs allow
for better translation of many individual pairs. In addition to improving the
translation quality of language pairs that the model was trained with, our
models can also learn to perform implicit bridging between language pairs never
seen explicitly during training, showing that transfer learning and zero-shot
translation is possible for neural translation. Finally, we show analyses that
hints at a universal interlingua representation in our models and show some
interesting examples when mixing languages.
| 2,017 | Computation and Language |
Knowledge Enhanced Hybrid Neural Network for Text Matching | Long text brings a big challenge to semantic matching due to their
complicated semantic and syntactic structures. To tackle the challenge, we
consider using prior knowledge to help identify useful information and filter
out noise to matching in long text. To this end, we propose a knowledge
enhanced hybrid neural network (KEHNN). The model fuses prior knowledge into
word representations by knowledge gates and establishes three matching channels
with words, sequential structures of sentences given by Gated Recurrent Units
(GRU), and knowledge enhanced representations. The three channels are processed
by a convolutional neural network to generate high level features for matching,
and the features are synthesized as a matching score by a multilayer
perceptron. The model extends the existing methods by conducting matching on
words, local structures of sentences, and global context of sentences.
Evaluation results from extensive experiments on public data sets for question
answering and conversation show that KEHNN can significantly outperform
the-state-of-the-art matching models and particularly improve the performance
on pairs with long text.
| 2,016 | Computation and Language |
A Neural Architecture Mimicking Humans End-to-End for Natural Language
Inference | In this work we use the recent advances in representation learning to propose
a neural architecture for the problem of natural language inference. Our
approach is aligned to mimic how a human does the natural language inference
process given two statements. The model uses variants of Long Short Term Memory
(LSTM), attention mechanism and composable neural networks, to carry out the
task. Each part of our model can be mapped to a clear functionality humans do
for carrying out the overall task of natural language inference. The model is
end-to-end differentiable enabling training by stochastic gradient descent. On
Stanford Natural Language Inference(SNLI) dataset, the proposed model achieves
better accuracy numbers than all published models in literature.
| 2,017 | Computation and Language |
Toward Multilingual Neural Machine Translation with Universal Encoder
and Decoder | In this paper, we present our first attempts in building a multilingual
Neural Machine Translation framework under a unified approach. We are then able
to employ attention-based NMT for many-to-many multilingual translation tasks.
Our approach does not require any special treatment on the network architecture
and it allows us to learn minimal number of free parameters in a standard way
of training. Our approach has shown its effectiveness in an under-resourced
translation scenario with considerable improvements up to 2.6 BLEU points. In
addition, the approach has achieved interesting and promising results when
applied in the translation task that there is no direct parallel corpus between
source and target languages.
| 2,016 | Computation and Language |
SimDoc: Topic Sequence Alignment based Document Similarity Framework | Document similarity is the problem of estimating the degree to which a given
pair of documents has similar semantic content. An accurate document similarity
measure can improve several enterprise relevant tasks such as document
clustering, text mining, and question-answering. In this paper, we show that a
document's thematic flow, which is often disregarded by bag-of-word techniques,
is pivotal in estimating their similarity. To this end, we propose a novel
semantic document similarity framework, called SimDoc. We model documents as
topic-sequences, where topics represent latent generative clusters of related
words. Then, we use a sequence alignment algorithm to estimate their semantic
similarity. We further conceptualize a novel mechanism to compute topic-topic
similarity to fine tune our system. In our experiments, we show that SimDoc
outperforms many contemporary bag-of-words techniques in accurately computing
document similarity, and on practical applications such as document clustering.
| 2,017 | Computation and Language |
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