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A Multifaceted Evaluation of Neural versus Phrase-Based Machine
Translation for 9 Language Directions | We aim to shed light on the strengths and weaknesses of the newly introduced
neural machine translation paradigm. To that end, we conduct a multifaceted
evaluation in which we compare outputs produced by state-of-the-art neural
machine translation and phrase-based machine translation systems for 9 language
directions across a number of dimensions. Specifically, we measure the
similarity of the outputs, their fluency and amount of reordering, the effect
of sentence length and performance across different error categories. We find
out that translations produced by neural machine translation systems are
considerably different, more fluent and more accurate in terms of word order
compared to those produced by phrase-based systems. Neural machine translation
systems are also more accurate at producing inflected forms, but they perform
poorly when translating very long sentences.
| 2,017 | Computation and Language |
Question Analysis for Arabic Question Answering Systems | The first step of processing a question in Question Answering(QA) Systems is
to carry out a detailed analysis of the question for the purpose of determining
what it is asking for and how to perfectly approach answering it. Our Question
analysis uses several techniques to analyze any question given in natural
language: a Stanford POS Tagger & parser for Arabic language, a named entity
recognizer, tokenizer,Stop-word removal, Question expansion, Question
classification and Question focus extraction components. We employ numerous
detection rules and trained classifier using features from this analysis to
detect important elements of the question, including: 1) the portion of the
question that is a referring to the answer (the focus); 2) different terms in
the question that identify what type of entity is being asked for (the lexical
answer types); 3) Question expansion ; 4) a process of classifying the question
into one or more of several and different types; and We describe how these
elements are identified and evaluate the effect of accurate detection on our
question-answering system using the Mean Reciprocal Rank(MRR) accuracy measure.
| 2,017 | Computation and Language |
Cross-lingual RST Discourse Parsing | Discourse parsing is an integral part of understanding information flow and
argumentative structure in documents. Most previous research has focused on
inducing and evaluating models from the English RST Discourse Treebank.
However, discourse treebanks for other languages exist, including Spanish,
German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same
underlying linguistic theory, but differ slightly in the way documents are
annotated. In this paper, we present (a) a new discourse parser which is
simpler, yet competitive (significantly better on 2/3 metrics) to state of the
art for English, (b) a harmonization of discourse treebanks across languages,
enabling us to present (c) what to the best of our knowledge are the first
experiments on cross-lingual discourse parsing.
| 2,017 | Computation and Language |
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network | Distinguishing between antonyms and synonyms is a key task to achieve high
performance in NLP systems. While they are notoriously difficult to distinguish
by distributional co-occurrence models, pattern-based methods have proven
effective to differentiate between the relations. In this paper, we present a
novel neural network model AntSynNET that exploits lexico-syntactic patterns
from syntactic parse trees. In addition to the lexical and syntactic
information, we successfully integrate the distance between the related words
along the syntactic path as a new pattern feature. The results from
classification experiments show that AntSynNET improves the performance over
prior pattern-based methods.
| 2,017 | Computation and Language |
Decoding with Finite-State Transducers on GPUs | Weighted finite automata and transducers (including hidden Markov models and
conditional random fields) are widely used in natural language processing (NLP)
to perform tasks such as morphological analysis, part-of-speech tagging,
chunking, named entity recognition, speech recognition, and others.
Parallelizing finite state algorithms on graphics processing units (GPUs) would
benefit many areas of NLP. Although researchers have implemented GPU versions
of basic graph algorithms, limited previous work, to our knowledge, has been
done on GPU algorithms for weighted finite automata. We introduce a GPU
implementation of the Viterbi and forward-backward algorithm, achieving
decoding speedups of up to 5.2x over our serial implementation running on
different computer architectures and 6093x over OpenFST.
| 2,017 | Computation and Language |
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain
Dialog Systems | Open-domain human-computer conversation has been attracting increasing
attention over the past few years. However, there does not exist a standard
automatic evaluation metric for open-domain dialog systems; researchers usually
resort to human annotation for model evaluation, which is time- and
labor-intensive. In this paper, we propose RUBER, a Referenced metric and
Unreferenced metric Blended Evaluation Routine, which evaluates a reply by
taking into consideration both a groundtruth reply and a query (previous
user-issued utterance). Our metric is learnable, but its training does not
require labels of human satisfaction. Hence, RUBER is flexible and extensible
to different datasets and languages. Experiments on both retrieval and
generative dialog systems show that RUBER has a high correlation with human
annotation.
| 2,017 | Computation and Language |
Job Detection in Twitter | In this report, we propose a new application for twitter data called
\textit{job detection}. We identify people's job category based on their
tweets. As a preliminary work, we limited our task to identify only IT workers
from other job holders. We have used and compared both simple bag of words
model and a document representation based on Skip-gram model. Our results show
that the model based on Skip-gram, achieves a 76\% precision and 82\% recall.
| 2,017 | Computation and Language |
De-identification In practice | We report our effort to identify the sensitive information, subset of data
items listed by HIPAA (Health Insurance Portability and Accountability), from
medical text using the recent advances in natural language processing and
machine learning techniques. We represent the words with high dimensional
continuous vectors learned by a variant of Word2Vec called Continous Bag Of
Words (CBOW). We feed the word vectors into a simple neural network with a Long
Short-Term Memory (LSTM) architecture. Without any attempts to extract manually
crafted features and considering that our medical dataset is too small to be
fed into neural network, we obtained promising results. The results thrilled us
to think about the larger scale of the project with precise parameter tuning
and other possible improvements.
| 2,017 | Computation and Language |
Parsing Universal Dependencies without training | We propose UDP, the first training-free parser for Universal Dependencies
(UD). Our algorithm is based on PageRank and a small set of head attachment
rules. It features two-step decoding to guarantee that function words are
attached as leaf nodes. The parser requires no training, and it is competitive
with a delexicalized transfer system. UDP offers a linguistically sound
unsupervised alternative to cross-lingual parsing for UD, which can be used as
a baseline for such systems. The parser has very few parameters and is
distinctly robust to domain change across languages.
| 2,017 | Computation and Language |
Generating High-Quality and Informative Conversation Responses with
Sequence-to-Sequence Models | Sequence-to-sequence models have been applied to the conversation response
generation problem where the source sequence is the conversation history and
the target sequence is the response. Unlike translation, conversation
responding is inherently creative. The generation of long, informative,
coherent, and diverse responses remains a hard task. In this work, we focus on
the single turn setting. We add self-attention to the decoder to maintain
coherence in longer responses, and we propose a practical approach, called the
glimpse-model, for scaling to large datasets. We introduce a stochastic
beam-search algorithm with segment-by-segment reranking which lets us inject
diversity earlier in the generation process. We trained on a combined data set
of over 2.3B conversation messages mined from the web. In human evaluation
studies, our method produces longer responses overall, with a higher proportion
rated as acceptable and excellent as length increases, compared to baseline
sequence-to-sequence models with explicit length-promotion. A back-off strategy
produces better responses overall, in the full spectrum of lengths.
| 2,017 | Computation and Language |
An Empirical Comparison of Simple Domain Adaptation Methods for Neural
Machine Translation | In this paper, we propose a novel domain adaptation method named "mixed fine
tuning" for neural machine translation (NMT). We combine two existing
approaches namely fine tuning and multi domain NMT. We first train an NMT model
on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus
which is a mix of the in-domain and out-of-domain corpora. All corpora are
augmented with artificial tags to indicate specific domains. We empirically
compare our proposed method against fine tuning and multi domain methods and
discuss its benefits and shortcomings.
| 2,017 | Computation and Language |
Prior matters: simple and general methods for evaluating and improving
topic quality in topic modeling | Latent Dirichlet Allocation (LDA) models trained without stopword removal
often produce topics with high posterior probabilities on uninformative words,
obscuring the underlying corpus content. Even when canonical stopwords are
manually removed, uninformative words common in that corpus will still dominate
the most probable words in a topic. In this work, we first show how the
standard topic quality measures of coherence and pointwise mutual information
act counter-intuitively in the presence of common but irrelevant words, making
it difficult to even quantitatively identify situations in which topics may be
dominated by stopwords. We propose an additional topic quality metric that
targets the stopword problem, and show that it, unlike the standard measures,
correctly correlates with human judgements of quality. We also propose a
simple-to-implement strategy for generating topics that are evaluated to be of
much higher quality by both human assessment and our new metric. This approach,
a collection of informative priors easily introduced into most LDA-style
inference methods, automatically promotes terms with domain relevance and
demotes domain-specific stop words. We demonstrate this approach's
effectiveness in three very different domains: Department of Labor accident
reports, online health forum posts, and NIPS abstracts. Overall we find that
current practices thought to solve this problem do not do so adequately, and
that our proposal offers a substantial improvement for those interested in
interpreting their topics as objects in their own right.
| 2,017 | Computation and Language |
Single-Pass, Adaptive Natural Language Filtering: Measuring Value in
User Generated Comments on Large-Scale, Social Media News Forums | There are large amounts of insight and social discovery potential in mining
crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com,
Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of
participation with its varying quality of commentary, extracting value out of
such data isn't always obvious nor timely. By designing efficient, single-pass
and adaptive natural language filters to quickly prune spam, noise, copy-cats,
marketing diversions, and out-of-context posts, we can remove over a third of
entries and return the comments with a higher probability of relatedness to the
original article in question. The approach presented here uses an adaptive,
two-step filtering process. It first leverages the original article posted in
the thread as a starting corpus to parse comments by matching intersecting
words and term-ratio balance per sentence then grows the corpus by adding new
words harvested from high-matching comments to increase filtering accuracy over
time.
| 2,017 | Computation and Language |
A Data-Oriented Model of Literary Language | We consider the task of predicting how literary a text is, with a gold
standard from human ratings. Aside from a standard bigram baseline, we apply
rich syntactic tree fragments, mined from the training set, and a series of
hand-picked features. Our model is the first to distinguish degrees of highly
and less literary novels using a variety of lexical and syntactic features, and
explains 76.0 % of the variation in literary ratings.
| 2,017 | Computation and Language |
LanideNN: Multilingual Language Identification on Character Window | In language identification, a common first step in natural language
processing, we want to automatically determine the language of some input text.
Monolingual language identification assumes that the given document is written
in one language. In multilingual language identification, the document is
usually in two or three languages and we just want their names. We aim one step
further and propose a method for textual language identification where
languages can change arbitrarily and the goal is to identify the spans of each
of the languages. Our method is based on Bidirectional Recurrent Neural
Networks and it performs well in monolingual and multilingual language
identification tasks on six datasets covering 131 languages. The method keeps
the accuracy also for short documents and across domains, so it is ideal for
off-the-shelf use without preparation of training data.
| 2,017 | Computation and Language |
SMARTies: Sentiment Models for Arabic Target Entities | We consider entity-level sentiment analysis in Arabic, a morphologically rich
language with increasing resources. We present a system that is applied to
complex posts written in response to Arabic newspaper articles. Our goal is to
identify important entity "targets" within the post along with the polarity
expressed about each target. We achieve significant improvements over multiple
baselines, demonstrating that the use of specific morphological representations
improves the performance of identifying both important targets and their
sentiment, and that the use of distributional semantic clusters further boosts
performances for these representations, especially when richer linguistic
resources are not available.
| 2,017 | Computation and Language |
Scalable, Trie-based Approximate Entity Extraction for Real-Time
Financial Transaction Screening | Financial institutions have to screen their transactions to ensure that they
are not affiliated with terrorism entities. Developing appropriate solutions to
detect such affiliations precisely while avoiding any kind of interruption to
large amount of legitimate transactions is essential. In this paper, we present
building blocks of a scalable solution that may help financial institutions to
build their own software to extract terrorism entities out of both structured
and unstructured financial messages in real time and with approximate
similarity matching approach.
| 2,017 | Computation and Language |
Efficient Transfer Learning Schemes for Personalized Language Modeling
using Recurrent Neural Network | In this paper, we propose an efficient transfer leaning methods for training
a personalized language model using a recurrent neural network with long
short-term memory architecture. With our proposed fast transfer learning
schemes, a general language model is updated to a personalized language model
with a small amount of user data and a limited computing resource. These
methods are especially useful for a mobile device environment while the data is
prevented from transferring out of the device for privacy purposes. Through
experiments on dialogue data in a drama, it is verified that our transfer
learning methods have successfully generated the personalized language model,
whose output is more similar to the personal language style in both qualitative
and quantitative aspects.
| 2,017 | Computation and Language |
LIDE: Language Identification from Text Documents | The increase in the use of microblogging came along with the rapid growth on
short linguistic data. On the other hand deep learning is considered to be the
new frontier to extract meaningful information out of large amount of raw data
in an automated manner. In this study, we engaged these two emerging fields to
come up with a robust language identifier on demand, namely Language
Identification Engine (LIDE). As a result, we achieved 95.12% accuracy in
Discriminating between Similar Languages (DSL) Shared Task 2015 dataset, which
is comparable to the maximum reported accuracy of 95.54% achieved so far.
| 2,017 | Computation and Language |
Deep Neural Networks for Czech Multi-label Document Classification | This paper is focused on automatic multi-label document classification of
Czech text documents. The current approaches usually use some pre-processing
which can have negative impact (loss of information, additional implementation
work, etc). Therefore, we would like to omit it and use deep neural networks
that learn from simple features. This choice was motivated by their successful
usage in many other machine learning fields. Two different networks are
compared: the first one is a standard multi-layer perceptron, while the second
one is a popular convolutional network. The experiments on a Czech newspaper
corpus show that both networks significantly outperform baseline method which
uses a rich set of features with maximum entropy classifier. We have also shown
that convolutional network gives the best results.
| 2,020 | Computation and Language |
QCRI Machine Translation Systems for IWSLT 16 | This paper describes QCRI's machine translation systems for the IWSLT 2016
evaluation campaign. We participated in the Arabic->English and English->Arabic
tracks. We built both Phrase-based and Neural machine translation models, in an
effort to probe whether the newly emerged NMT framework surpasses the
traditional phrase-based systems in Arabic-English language pairs. We trained a
very strong phrase-based system including, a big language model, the Operation
Sequence Model, Neural Network Joint Model and Class-based models along with
different domain adaptation techniques such as MML filtering, mixture modeling
and using fine tuning over NNJM model. However, a Neural MT system, trained by
stacking data from different genres through fine-tuning, and applying ensemble
over 8 models, beat our very strong phrase-based system by a significant 2 BLEU
points margin in Arabic->English direction. We did not obtain similar gains in
the other direction but were still able to outperform the phrase-based system.
We also applied system combination on phrase-based and NMT outputs.
| 2,017 | Computation and Language |
A Copy-Augmented Sequence-to-Sequence Architecture Gives Good
Performance on Task-Oriented Dialogue | Task-oriented dialogue focuses on conversational agents that participate in
user-initiated dialogues on domain-specific topics. In contrast to chatbots,
which simply seek to sustain open-ended meaningful discourse, existing
task-oriented agents usually explicitly model user intent and belief states.
This paper examines bypassing such an explicit representation by depending on a
latent neural embedding of state and learning selective attention to dialogue
history together with copying to incorporate relevant prior context. We
complement recent work by showing the effectiveness of simple
sequence-to-sequence neural architectures with a copy mechanism. Our model
outperforms more complex memory-augmented models by 7% in per-response
generation and is on par with the current state-of-the-art on DSTC2.
| 2,017 | Computation and Language |
Neural Models for Sequence Chunking | Many natural language understanding (NLU) tasks, such as shallow parsing
(i.e., text chunking) and semantic slot filling, require the assignment of
representative labels to the meaningful chunks in a sentence. Most of the
current deep neural network (DNN) based methods consider these tasks as a
sequence labeling problem, in which a word, rather than a chunk, is treated as
the basic unit for labeling. These chunks are then inferred by the standard IOB
(Inside-Outside-Beginning) labels. In this paper, we propose an alternative
approach by investigating the use of DNN for sequence chunking, and propose
three neural models so that each chunk can be treated as a complete unit for
labeling. Experimental results show that the proposed neural sequence chunking
models can achieve start-of-the-art performance on both the text chunking and
slot filling tasks.
| 2,017 | Computation and Language |
Dialog Context Language Modeling with Recurrent Neural Networks | In this work, we propose contextual language models that incorporate dialog
level discourse information into language modeling. Previous works on
contextual language model treat preceding utterances as a sequence of inputs,
without considering dialog interactions. We design recurrent neural network
(RNN) based contextual language models that specially track the interactions
between speakers in a dialog. Experiment results on Switchboard Dialog Act
Corpus show that the proposed model outperforms conventional single turn based
RNN language model by 3.3% on perplexity. The proposed models also demonstrate
advantageous performance over other competitive contextual language models.
| 2,017 | Computation and Language |
Deep Memory Networks for Attitude Identification | We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.
| 2,017 | Computation and Language |
Machine Translation Approaches and Survey for Indian Languages | In this study, we present an analysis regarding the performance of the
state-of-art Phrase-based Statistical Machine Translation (SMT) on multiple
Indian languages. We report baseline systems on several language pairs. The
motivation of this study is to promote the development of SMT and linguistic
resources for these language pairs, as the current state-of-the-art is quite
bleak due to sparse data resources. The success of an SMT system is contingent
on the availability of a large parallel corpus. Such data is necessary to
reliably estimate translation probabilities. We report the performance of
baseline systems translating from Indian languages (Bengali, Guajarati, Hindi,
Malayalam, Punjabi, Tamil, Telugu and Urdu) into English with average 10%
accurate results for all the language pairs.
| 2,017 | Computation and Language |
End-to-End ASR-free Keyword Search from Speech | End-to-end (E2E) systems have achieved competitive results compared to
conventional hybrid hidden Markov model (HMM)-deep neural network based
automatic speech recognition (ASR) systems. Such E2E systems are attractive due
to the lack of dependence on alignments between input acoustic and output
grapheme or HMM state sequence during training. This paper explores the design
of an ASR-free end-to-end system for text query-based keyword search (KWS) from
speech trained with minimal supervision. Our E2E KWS system consists of three
sub-systems. The first sub-system is a recurrent neural network (RNN)-based
acoustic auto-encoder trained to reconstruct the audio through a
finite-dimensional representation. The second sub-system is a character-level
RNN language model using embeddings learned from a convolutional neural
network. Since the acoustic and text query embeddings occupy different
representation spaces, they are input to a third feed-forward neural network
that predicts whether the query occurs in the acoustic utterance or not. This
E2E ASR-free KWS system performs respectably despite lacking a conventional ASR
system and trains much faster.
| 2,018 | Computation and Language |
Community Question Answering Platforms vs. Twitter for Predicting
Characteristics of Urban Neighbourhoods | In this paper, we investigate whether text from a Community Question
Answering (QA) platform can be used to predict and describe real-world
attributes. We experiment with predicting a wide range of 62 demographic
attributes for neighbourhoods of London. We use the text from QA platform of
Yahoo! Answers and compare our results to the ones obtained from Twitter
microblogs. Outcomes show that the correlation between the predicted
demographic attributes using text from Yahoo! Answers discussions and the
observed demographic attributes can reach an average Pearson correlation
coefficient of \r{ho} = 0.54, slightly higher than the predictions obtained
using Twitter data. Our qualitative analysis indicates that there is semantic
relatedness between the highest correlated terms extracted from both datasets
and their relative demographic attributes. Furthermore, the correlations
highlight the different natures of the information contained in Yahoo! Answers
and Twitter. While the former seems to offer a more encyclopedic content, the
latter provides information related to the current sociocultural aspects or
phenomena.
| 2,017 | Computation and Language |
Assessing User Expertise in Spoken Dialog System Interactions | Identifying the level of expertise of its users is important for a system
since it can lead to a better interaction through adaptation techniques.
Furthermore, this information can be used in offline processes of root cause
analysis. However, not much effort has been put into automatically identifying
the level of expertise of an user, especially in dialog-based interactions. In
this paper we present an approach based on a specific set of task related
features. Based on the distribution of the features among the two classes -
Novice and Expert - we used Random Forests as a classification approach.
Furthermore, we used a Support Vector Machine classifier, in order to perform a
result comparison. By applying these approaches on data from a real system,
Let's Go, we obtained preliminary results that we consider positive, given the
difficulty of the task and the lack of competing approaches for comparison.
| 2,016 | Computation and Language |
A Joint Framework for Argumentative Text Analysis Incorporating Domain
Knowledge | For argumentation mining, there are several sub-tasks such as argumentation
component type classification, relation classification. Existing research tends
to solve such sub-tasks separately, but ignore the close relation between them.
In this paper, we present a joint framework incorporating logical relation
between sub-tasks to improve the performance of argumentation structure
generation. We design an objective function to combine the predictions from
individual models for each sub-task and solve the problem with some constraints
constructed from background knowledge. We evaluate our proposed model on two
public corpora and the experiment results show that our model can outperform
the baseline that uses a separate model significantly for each sub-task. Our
model also shows advantages on component-related sub-tasks compared to a
state-of-the-art joint model based on the evidence graph.
| 2,017 | Computation and Language |
Harnessing Cognitive Features for Sarcasm Detection | In this paper, we propose a novel mechanism for enriching the feature vector,
for the task of sarcasm detection, with cognitive features extracted from
eye-movement patterns of human readers. Sarcasm detection has been a
challenging research problem, and its importance for NLP applications such as
review summarization, dialog systems and sentiment analysis is well recognized.
Sarcasm can often be traced to incongruity that becomes apparent as the full
sentence unfolds. This presence of incongruity- implicit or explicit- affects
the way readers eyes move through the text. We observe the difference in the
behaviour of the eye, while reading sarcastic and non sarcastic sentences.
Motivated by his observation, we augment traditional linguistic and stylistic
features for sarcasm detection with the cognitive features obtained from
readers eye movement data. We perform statistical classification using the
enhanced feature set so obtained. The augmented cognitive features improve
sarcasm detection by 3.7% (in terms of F-score), over the performance of the
best reported system.
| 2,017 | Computation and Language |
Leveraging Cognitive Features for Sentiment Analysis | Sentiments expressed in user-generated short text and sentences are nuanced
by subtleties at lexical, syntactic, semantic and pragmatic levels. To address
this, we propose to augment traditional features used for sentiment analysis
and sarcasm detection, with cognitive features derived from the eye-movement
patterns of readers. Statistical classification using our enhanced feature set
improves the performance (F-score) of polarity detection by a maximum of 3.7%
and 9.3% on two datasets, over the systems that use only traditional features.
We perform feature significance analysis, and experiment on a held-out dataset,
showing that cognitive features indeed empower sentiment analyzers to handle
complex constructs.
| 2,017 | Computation and Language |
CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation | While the general analysis of named entities has received substantial
research attention on unstructured as well as structured data, the analysis of
relations among named entities has received limited focus. In fact, a review of
the literature revealed a deficiency in research on the abstract
conceptualization required to organize relations. We believe that such an
abstract conceptualization can benefit various communities and applications
such as natural language processing, information extraction, machine learning,
and ontology engineering. In this paper, we present Comprehensive EVent
Ontology (CEVO), built on Levin's conceptual hierarchy of English verbs that
categorizes verbs with shared meaning, and syntactic behavior. We present the
fundamental concepts and requirements for this ontology. Furthermore, we
present three use cases employing the CEVO ontology on annotation tasks: (i)
annotating relations in plain text, (ii) annotating ontological properties, and
(iii) linking textual relations to ontological properties. These use-cases
demonstrate the benefits of using CEVO for annotation: (i) annotating English
verbs from an abstract conceptualization, (ii) playing the role of an upper
ontology for organizing ontological properties, and (iii) facilitating the
annotation of text relations using any underlying vocabulary. This resource is
available at https://shekarpour.github.io/cevo.io/ using https://w3id.org/cevo
namespace.
| 2,018 | Computation and Language |
A Multichannel Convolutional Neural Network For Cross-language Dialog
State Tracking | The fifth Dialog State Tracking Challenge (DSTC5) introduces a new
cross-language dialog state tracking scenario, where the participants are asked
to build their trackers based on the English training corpus, while evaluating
them with the unlabeled Chinese corpus. Although the computer-generated
translations for both English and Chinese corpus are provided in the dataset,
these translations contain errors and careless use of them can easily hurt the
performance of the built trackers. To address this problem, we propose a
multichannel Convolutional Neural Networks (CNN) architecture, in which we
treat English and Chinese language as different input channels of one single
CNN model. In the evaluation of DSTC5, we found that such multichannel
architecture can effectively improve the robustness against translation errors.
Additionally, our method for DSTC5 is purely machine learning based and
requires no prior knowledge about the target language. We consider this a
desirable property for building a tracker in the cross-language context, as not
every developer will be familiar with both languages.
| 2,017 | Computation and Language |
Incorporating Global Visual Features into Attention-Based Neural Machine
Translation | We introduce multi-modal, attention-based neural machine translation (NMT)
models which incorporate visual features into different parts of both the
encoder and the decoder. We utilise global image features extracted using a
pre-trained convolutional neural network and incorporate them (i) as words in
the source sentence, (ii) to initialise the encoder hidden state, and (iii) as
additional data to initialise the decoder hidden state. In our experiments, we
evaluate how these different strategies to incorporate global image features
compare and which ones perform best. We also study the impact that adding
synthetic multi-modal, multilingual data brings and find that the additional
data have a positive impact on multi-modal models. We report new
state-of-the-art results and our best models also significantly improve on a
comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k
data set according to all metrics evaluated. To the best of our knowledge, it
is the first time a purely neural model significantly improves over a PBSMT
model on all metrics evaluated on this data set.
| 2,017 | Computation and Language |
Adversarial Learning for Neural Dialogue Generation | In this paper, drawing intuition from the Turing test, we propose using
adversarial training for open-domain dialogue generation: the system is trained
to produce sequences that are indistinguishable from human-generated dialogue
utterances. We cast the task as a reinforcement learning (RL) problem where we
jointly train two systems, a generative model to produce response sequences,
and a discriminator---analagous to the human evaluator in the Turing test--- to
distinguish between the human-generated dialogues and the machine-generated
ones. The outputs from the discriminator are then used as rewards for the
generative model, pushing the system to generate dialogues that mostly resemble
human dialogues.
In addition to adversarial training we describe a model for adversarial {\em
evaluation} that uses success in fooling an adversary as a dialogue evaluation
metric, while avoiding a number of potential pitfalls. Experimental results on
several metrics, including adversarial evaluation, demonstrate that the
adversarially-trained system generates higher-quality responses than previous
baselines.
| 2,017 | Computation and Language |
Learning to Decode for Future Success | We introduce a simple, general strategy to manipulate the behavior of a
neural decoder that enables it to generate outputs that have specific
properties of interest (e.g., sequences of a pre-specified length). The model
can be thought of as a simple version of the actor-critic model that uses an
interpolation of the actor (the MLE-based token generation policy) and the
critic (a value function that estimates the future values of the desired
property) for decision making. We demonstrate that the approach is able to
incorporate a variety of properties that cannot be handled by standard neural
sequence decoders, such as sequence length and backward probability
(probability of sources given targets), in addition to yielding consistent
improvements in abstractive summarization and machine translation when the
property to be optimized is BLEU or ROUGE scores.
| 2,017 | Computation and Language |
Hierarchical Recurrent Attention Network for Response Generation | We study multi-turn response generation in chatbots where a response is
generated according to a conversation context. Existing work has modeled the
hierarchy of the context, but does not pay enough attention to the fact that
words and utterances in the context are differentially important. As a result,
they may lose important information in context and generate irrelevant
responses. We propose a hierarchical recurrent attention network (HRAN) to
model both aspects in a unified framework. In HRAN, a hierarchical attention
mechanism attends to important parts within and among utterances with word
level attention and utterance level attention respectively. With the word level
attention, hidden vectors of a word level encoder are synthesized as utterance
vectors and fed to an utterance level encoder to construct hidden
representations of the context. The hidden vectors of the context are then
processed by the utterance level attention and formed as context vectors for
decoding the response. Empirical studies on both automatic evaluation and human
judgment show that HRAN can significantly outperform state-of-the-art models
for multi-turn response generation.
| 2,017 | Computation and Language |
Learning Word-Like Units from Joint Audio-Visual Analysis | Given a collection of images and spoken audio captions, we present a method
for discovering word-like acoustic units in the continuous speech signal and
grounding them to semantically relevant image regions. For example, our model
is able to detect spoken instances of the word 'lighthouse' within an utterance
and associate them with image regions containing lighthouses. We do not use any
form of conventional automatic speech recognition, nor do we use any text
transcriptions or conventional linguistic annotations. Our model effectively
implements a form of spoken language acquisition, in which the computer learns
not only to recognize word categories by sound, but also to enrich the words it
learns with semantics by grounding them in images.
| 2,017 | Computation and Language |
emLam -- a Hungarian Language Modeling baseline | This paper aims to make up for the lack of documented baselines for Hungarian
language modeling. Various approaches are evaluated on three publicly available
Hungarian corpora. Perplexity values comparable to models of similar-sized
English corpora are reported. A new, freely downloadable Hungar- ian benchmark
corpus is introduced.
| 2,017 | Computation and Language |
Emotion Recognition From Speech With Recurrent Neural Networks | In this paper the task of emotion recognition from speech is considered.
Proposed approach uses deep recurrent neural network trained on a sequence of
acoustic features calculated over small speech intervals. At the same time
special probabilistic-nature CTC loss function allows to consider long
utterances containing both emotional and neutral parts. The effectiveness of
such an approach is shown in two ways. Firstly, the comparison with recent
advances in this field is carried out. Secondly, human performance on the same
task is measured. Both criteria show the high quality of the proposed method.
| 2,018 | Computation and Language |
Measuring the Reliability of Hate Speech Annotations: The Case of the
European Refugee Crisis | Some users of social media are spreading racist, sexist, and otherwise
hateful content. For the purpose of training a hate speech detection system,
the reliability of the annotations is crucial, but there is no universally
agreed-upon definition. We collected potentially hateful messages and asked two
groups of internet users to determine whether they were hate speech or not,
whether they should be banned or not and to rate their degree of offensiveness.
One of the groups was shown a definition prior to completing the survey. We
aimed to assess whether hate speech can be annotated reliably, and the extent
to which existing definitions are in accordance with subjective ratings. Our
results indicate that showing users a definition caused them to partially align
their own opinion with the definition but did not improve reliability, which
was very low overall. We conclude that the presence of hate speech should
perhaps not be considered a binary yes-or-no decision, and raters need more
detailed instructions for the annotation.
| 2,016 | Computation and Language |
Adversarial Evaluation of Dialogue Models | The recent application of RNN encoder-decoder models has resulted in
substantial progress in fully data-driven dialogue systems, but evaluation
remains a challenge. An adversarial loss could be a way to directly evaluate
the extent to which generated dialogue responses sound like they came from a
human. This could reduce the need for human evaluation, while more directly
evaluating on a generative task. In this work, we investigate this idea by
training an RNN to discriminate a dialogue model's samples from human-generated
samples. Although we find some evidence this setup could be viable, we also
note that many issues remain in its practical application. We discuss both
aspects and conclude that future work is warranted.
| 2,017 | Computation and Language |
Image-Grounded Conversations: Multimodal Context for Natural Question
and Response Generation | The popularity of image sharing on social media and the engagement it creates
between users reflects the important role that visual context plays in everyday
conversations. We present a novel task, Image-Grounded Conversations (IGC), in
which natural-sounding conversations are generated about a shared image. To
benchmark progress, we introduce a new multiple-reference dataset of
crowd-sourced, event-centric conversations on images. IGC falls on the
continuum between chit-chat and goal-directed conversation models, where visual
grounding constrains the topic of conversation to event-driven utterances.
Experiments with models trained on social media data show that the combination
of visual and textual context enhances the quality of generated conversational
turns. In human evaluation, the gap between human performance and that of both
neural and retrieval architectures suggests that multi-modal IGC presents an
interesting challenge for dialogue research.
| 2,017 | Computation and Language |
Drug-Drug Interaction Extraction from Biomedical Text Using Long Short
Term Memory Network | Simultaneous administration of multiple drugs can have synergistic or
antagonistic effects as one drug can affect activities of other drugs.
Synergistic effects lead to improved therapeutic outcomes, whereas,
antagonistic effects can be life-threatening, may lead to increased healthcare
cost, or may even cause death. Thus identification of unknown drug-drug
interaction (DDI) is an important concern for efficient and effective
healthcare. Although multiple resources for DDI exist, they are often unable to
keep pace with rich amount of information available in fast growing biomedical
texts. Most existing methods model DDI extraction from text as a classification
problem and mainly rely on handcrafted features. Some of these features further
depend on domain specific tools. Recently neural network models using latent
features have been shown to give similar or better performance than the other
existing models dependent on handcrafted features. In this paper, we present
three models namely, {\it B-LSTM}, {\it AB-LSTM} and {\it Joint AB-LSTM} based
on long short-term memory (LSTM) network. All three models utilize word and
position embedding as latent features and thus do not rely on explicit feature
engineering. Further use of bidirectional long short-term memory (Bi-LSTM)
networks allow implicit feature extraction from the whole sentence. The two
models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the
output of Bi-LSTM layer to assign weights to features. Our experimental results
on the SemEval-2013 DDI extraction dataset show that the {\it Joint AB-LSTM}
model outperforms all the existing methods, including those relying on
handcrafted features. The other two proposed LSTM models also perform
competitively with state-of-the-art methods.
| 2,017 | Computation and Language |
Using English as Pivot to Extract Persian-Italian Parallel Sentences
from Non-Parallel Corpora | The effectiveness of a statistical machine translation system (SMT) is very
dependent upon the amount of parallel corpus used in the training phase. For
low-resource language pairs there are not enough parallel corpora to build an
accurate SMT. In this paper, a novel approach is presented to extract bilingual
Persian-Italian parallel sentences from a non-parallel (comparable) corpus. In
this study, English is used as the pivot language to compute the matching
scores between source and target sentences and candidate selection phase.
Additionally, a new monolingual sentence similarity metric, Normalized Google
Distance (NGD) is proposed to improve the matching process. Moreover, some
extensions of the baseline system are applied to improve the quality of
extracted sentences measured with BLEU. Experimental results show that using
the new pivot based extraction can increase the quality of bilingual corpus
significantly and consequently improves the performance of the Persian-Italian
SMT system.
| 2,017 | Computation and Language |
Extracting Bilingual Persian Italian Lexicon from Comparable Corpora
Using Different Types of Seed Dictionaries | Bilingual dictionaries are very important in various fields of natural
language processing. In recent years, research on extracting new bilingual
lexicons from non-parallel (comparable) corpora have been proposed. Almost all
use a small existing dictionary or other resources to make an initial list
called the "seed dictionary". In this paper, we discuss the use of different
types of dictionaries as the initial starting list for creating a bilingual
Persian-Italian lexicon from a comparable corpus. Our experiments apply
state-of-the-art techniques on three different seed dictionaries; an existing
dictionary, a dictionary created with pivot-based schema, and a dictionary
extracted from a small Persian-Italian parallel text. The interesting challenge
of our approach is to find a way to combine different dictionaries together in
order to produce a better and more accurate lexicon. In order to combine seed
dictionaries, we propose two different combination models and examine the
effect of our novel combination models on various comparable corpora that have
differing degrees of comparability. We conclude with a proposal for a new
weighting system to improve the extracted lexicon. The experimental results
produced by our implementation show the efficiency of our proposed models.
| 2,019 | Computation and Language |
Graph-Based Semi-Supervised Conditional Random Fields For Spoken
Language Understanding Using Unaligned Data | We experiment graph-based Semi-Supervised Learning (SSL) of Conditional
Random Fields (CRF) for the application of Spoken Language Understanding (SLU)
on unaligned data. The aligned labels for examples are obtained using IBM
Model. We adapt a baseline semi-supervised CRF by defining new feature set and
altering the label propagation algorithm. Our results demonstrate that our
proposed approach significantly improves the performance of the supervised
model by utilizing the knowledge gained from the graph.
| 2,017 | Computation and Language |
Structural Analysis of Hindi Phonetics and A Method for Extraction of
Phonetically Rich Sentences from a Very Large Hindi Text Corpus | Automatic speech recognition (ASR) and Text to speech (TTS) are two prominent
area of research in human computer interaction nowadays. A set of phonetically
rich sentences is in a matter of importance in order to develop these two
interactive modules of HCI. Essentially, the set of phonetically rich sentences
has to cover all possible phone units distributed uniformly. Selecting such a
set from a big corpus with maintaining phonetic characteristic based similarity
is still a challenging problem. The major objective of this paper is to devise
a criteria in order to select a set of sentences encompassing all phonetic
aspects of a corpus with size as minimum as possible. First, this paper
presents a statistical analysis of Hindi phonetics by observing the structural
characteristics. Further a two stage algorithm is proposed to extract
phonetically rich sentences with a high variety of triphones from the EMILLE
Hindi corpus. The algorithm consists of a distance measuring criteria to select
a sentence in order to improve the triphone distribution. Moreover, a special
preprocessing method is proposed to score each triphone in terms of inverse
probability in order to fasten the algorithm. The results show that the
approach efficiently build uniformly distributed phonetically-rich corpus with
optimum number of sentences.
| 2,017 | Computation and Language |
A Comparative Study on Different Types of Approaches to Bengali document
Categorization | Document categorization is a technique where the category of a document is
determined. In this paper three well-known supervised learning techniques which
are Support Vector Machine(SVM), Na\"ive Bayes(NB) and Stochastic Gradient
Descent(SGD) compared for Bengali document categorization. Besides classifier,
classification also depends on how feature is selected from dataset. For
analyzing those classifier performances on predicting a document against twelve
categories several feature selection techniques are also applied in this
article namely Chi square distribution, normalized TFIDF (term
frequency-inverse document frequency) with word analyzer. So, we attempt to
explore the efficiency of those three-classification algorithms by using two
different feature selection techniques in this article.
| 2,017 | Computation and Language |
Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language
Model | In this paper, we describe a research method that generates Bangla word
clusters on the basis of relating to meaning in language and contextual
similarity. The importance of word clustering is in parts of speech (POS)
tagging, word sense disambiguation, text classification, recommender system,
spell checker, grammar checker, knowledge discover and for many others Natural
Language Processing (NLP) applications. In the history of word clustering,
English and some other languages have already implemented some methods on word
clustering efficiently. But due to lack of the resources, word clustering in
Bangla has not been still implemented efficiently. Presently, its
implementation is in the beginning stage. In some research of word clustering
in English based on preceding and next five words of a key word they found an
efficient result. Now, we are trying to implement the tri-gram, 4-gram and
5-gram model of word clustering for Bangla to observe which one is the best
among them. We have started our research with quite a large corpus of
approximate 1 lakh Bangla words. We are using a machine learning technique in
this research. We will generate word clusters and analyze the clusters by
testing some different threshold values.
| 2,017 | Computation and Language |
Predicting Auction Price of Vehicle License Plate with Deep Recurrent
Neural Network | In Chinese societies, superstition is of paramount importance, and vehicle
license plates with desirable numbers can fetch very high prices in auctions.
Unlike other valuable items, license plates are not allocated an estimated
price before auction. I propose that the task of predicting plate prices can be
viewed as a natural language processing (NLP) task, as the value depends on the
meaning of each individual character on the plate and its semantics. I
construct a deep recurrent neural network (RNN) to predict the prices of
vehicle license plates in Hong Kong, based on the characters on a plate. I
demonstrate the importance of having a deep network and of retraining.
Evaluated on 13 years of historical auction prices, the deep RNN's predictions
can explain over 80 percent of price variations, outperforming previous models
by a significant margin. I also demonstrate how the model can be extended to
become a search engine for plates and to provide estimates of the expected
price distribution.
| 2,019 | Computation and Language |
Robust Multilingual Named Entity Recognition with Shallow
Semi-Supervised Features | We present a multilingual Named Entity Recognition approach based on a robust
and general set of features across languages and datasets. Our system combines
shallow local information with clustering semi-supervised features induced on
large amounts of unlabeled text. Understanding via empirical experimentation
how to effectively combine various types of clustering features allows us to
seamlessly export our system to other datasets and languages. The result is a
simple but highly competitive system which obtains state of the art results
across five languages and twelve datasets. The results are reported on standard
shared task evaluation data such as CoNLL for English, Spanish and Dutch.
Furthermore, and despite the lack of linguistically motivated features, we also
report best results for languages such as Basque and German. In addition, we
demonstrate that our method also obtains very competitive results even when the
amount of supervised data is cut by half, alleviating the dependency on
manually annotated data. Finally, the results show that our emphasis on
clustering features is crucial to develop robust out-of-domain models. The
system and models are freely available to facilitate its use and guarantee the
reproducibility of results.
| 2,016 | Computation and Language |
SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text | Use of social media has grown dramatically during the last few years. Users
follow informal languages in communicating through social media. The language
of communication is often mixed in nature, where people transcribe their
regional language with English and this technique is found to be extremely
popular. Natural language processing (NLP) aims to infer the information from
these text where Part-of-Speech (PoS) tagging plays an important role in
getting the prosody of the written text. For the task of PoS tagging on
Code-Mixed Indian Social Media Text, we develop a supervised system based on
Conditional Random Field classifier. In order to tackle the problem
effectively, we have focused on extracting rich linguistic features. We
participate in three different language pairs, ie. English-Hindi,
English-Bengali and English-Telugu on three different social media platforms,
Twitter, Facebook & WhatsApp. The proposed system is able to successfully
assign coarse as well as fine-grained PoS tag labels for a given a code-mixed
sentence. Experiments show that our system is quite generic that shows
encouraging performance levels on all the three language pairs in all the
domains.
| 2,017 | Computation and Language |
AMR-to-text Generation with Synchronous Node Replacement Grammar | This paper addresses the task of AMR-to-text generation by leveraging
synchronous node replacement grammar. During training, graph-to-string rules
are learned using a heuristic extraction algorithm. At test time, a graph
transducer is applied to collapse input AMRs and generate output sentences.
Evaluated on SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which
is the best reported so far.
| 2,017 | Computation and Language |
Multilingual and Cross-lingual Timeline Extraction | In this paper we present an approach to extract ordered timelines of events,
their participants, locations and times from a set of multilingual and
cross-lingual data sources. Based on the assumption that event-related
information can be recovered from different documents written in different
languages, we extend the Cross-document Event Ordering task presented at
SemEval 2015 by specifying two new tasks for, respectively, Multilingual and
Cross-lingual Timeline Extraction. We then develop three deterministic
algorithms for timeline extraction based on two main ideas. First, we address
implicit temporal relations at document level since explicit time-anchors are
too scarce to build a wide coverage timeline extraction system. Second, we
leverage several multilingual resources to obtain a single, inter-operable,
semantic representation of events across documents and across languages. The
result is a highly competitive system that strongly outperforms the current
state-of-the-art. Nonetheless, further analysis of the results reveals that
linking the event mentions with their target entities and time-anchors remains
a difficult challenge. The systems, resources and scorers are freely available
to facilitate its use and guarantee the reproducibility of results.
| 2,017 | Computation and Language |
Analysing Temporal Evolution of Interlingual Wikipedia Article Pairs | Wikipedia articles representing an entity or a topic in different language
editions evolve independently within the scope of the language-specific user
communities. This can lead to different points of views reflected in the
articles, as well as complementary and inconsistent information. An analysis of
how the information is propagated across the Wikipedia language editions can
provide important insights in the article evolution along the temporal and
cultural dimensions and support quality control. To facilitate such analysis,
we present MultiWiki - a novel web-based user interface that provides an
overview of the similarities and differences across the article pairs
originating from different language editions on a timeline. MultiWiki enables
users to observe the changes in the interlingual article similarity over time
and to perform a detailed visual comparison of the article snapshots at a
particular time point.
| 2,017 | Computation and Language |
Symbolic, Distributed and Distributional Representations for Natural
Language Processing in the Era of Deep Learning: a Survey | Natural language is inherently a discrete symbolic representation of human
knowledge. Recent advances in machine learning (ML) and in natural language
processing (NLP) seem to contradict the above intuition: discrete symbols are
fading away, erased by vectors or tensors called distributed and distributional
representations. However, there is a strict link between
distributed/distributional representations and discrete symbols, being the
first an approximation of the second. A clearer understanding of the strict
link between distributed/distributional representations and symbols may
certainly lead to radically new deep learning networks. In this paper we make a
survey that aims to renew the link between symbolic representations and
distributed/distributional representations. This is the right time to
revitalize the area of interpreting how discrete symbols are represented inside
neural networks.
| 2,020 | Computation and Language |
Topic Modeling the H\`an di\u{a}n Ancient Classics | Ancient Chinese texts present an area of enormous challenge and opportunity
for humanities scholars interested in exploiting computational methods to
assist in the development of new insights and interpretations of culturally
significant materials. In this paper we describe a collaborative effort between
Indiana University and Xi'an Jiaotong University to support exploration and
interpretation of a digital corpus of over 18,000 ancient Chinese documents,
which we refer to as the "Handian" ancient classics corpus (H\`an di\u{a}n
g\u{u} j\'i, i.e, the "Han canon" or "Chinese classics"). It contains classics
of ancient Chinese philosophy, documents of historical and biographical
significance, and literary works. We begin by describing the Digital Humanities
context of this joint project, and the advances in humanities computing that
made this project feasible. We describe the corpus and introduce our
application of probabilistic topic modeling to this corpus, with attention to
the particular challenges posed by modeling ancient Chinese documents. We give
a specific example of how the software we have developed can be used to aid
discovery and interpretation of themes in the corpus. We outline more advanced
forms of computer-aided interpretation that are also made possible by the
programming interface provided by our system, and the general implications of
these methods for understanding the nature of meaning in these texts.
| 2,017 | Computation and Language |
Structured Attention Networks | Attention networks have proven to be an effective approach for embedding
categorical inference within a deep neural network. However, for many tasks we
may want to model richer structural dependencies without abandoning end-to-end
training. In this work, we experiment with incorporating richer structural
distributions, encoded using graphical models, within deep networks. We show
that these structured attention networks are simple extensions of the basic
attention procedure, and that they allow for extending attention beyond the
standard soft-selection approach, such as attending to partial segmentations or
to subtrees. We experiment with two different classes of structured attention
networks: a linear-chain conditional random field and a graph-based parsing
model, and describe how these models can be practically implemented as neural
network layers. Experiments show that this approach is effective for
incorporating structural biases, and structured attention networks outperform
baseline attention models on a variety of synthetic and real tasks: tree
transduction, neural machine translation, question answering, and natural
language inference. We further find that models trained in this way learn
interesting unsupervised hidden representations that generalize simple
attention.
| 2,017 | Computation and Language |
Automatic Prediction of Discourse Connectives | Accurate prediction of suitable discourse connectives (however, furthermore,
etc.) is a key component of any system aimed at building coherent and fluent
discourses from shorter sentences and passages. As an example, a dialog system
might assemble a long and informative answer by sampling passages extracted
from different documents retrieved from the Web. We formulate the task of
discourse connective prediction and release a dataset of 2.9M sentence pairs
separated by discourse connectives for this task. Then, we evaluate the
hardness of the task for human raters, apply a recently proposed decomposable
attention (DA) model to this task and observe that the automatic predictor has
a higher F1 than human raters (32 vs. 30). Nevertheless, under specific
conditions the raters still outperform the DA model, suggesting that there is
headroom for future improvements.
| 2,018 | Computation and Language |
Multilingual Multi-modal Embeddings for Natural Language Processing | We propose a novel discriminative model that learns embeddings from
multilingual and multi-modal data, meaning that our model can take advantage of
images and descriptions in multiple languages to improve embedding quality. To
that end, we introduce a modification of a pairwise contrastive estimation
optimisation function as our training objective. We evaluate our embeddings on
an image-sentence ranking (ISR), a semantic textual similarity (STS), and a
neural machine translation (NMT) task. We find that the additional multilingual
signals lead to improvements on both the ISR and STS tasks, and the
discriminative cost can also be used in re-ranking $n$-best lists produced by
NMT models, yielding strong improvements.
| 2,017 | Computation and Language |
Predicting Target Language CCG Supertags Improves Neural Machine
Translation | Neural machine translation (NMT) models are able to partially learn syntactic
information from sequential lexical information. Still, some complex syntactic
phenomena such as prepositional phrase attachment are poorly modeled. This work
aims to answer two questions: 1) Does explicitly modeling target language
syntax help NMT? 2) Is tight integration of words and syntax better than
multitask training? We introduce syntactic information in the form of CCG
supertags in the decoder, by interleaving the target supertags with the word
sequence. Our results on WMT data show that explicitly modeling target-syntax
improves machine translation quality for German->English, a high-resource pair,
and for Romanian->English, a low-resource pair and also several syntactic
phenomena including prepositional phrase attachment. Furthermore, a tight
coupling of words and syntax improves translation quality more than multitask
training. By combining target-syntax with adding source-side dependency labels
in the embedding layer, we obtain a total improvement of 0.9 BLEU for
German->English and 1.2 BLEU for Romanian->English.
| 2,017 | Computation and Language |
Insights into Entity Name Evolution on Wikipedia | Working with Web archives raises a number of issues caused by their temporal
characteristics. Depending on the age of the content, additional knowledge
might be needed to find and understand older texts. Especially facts about
entities are subject to change. Most severe in terms of information retrieval
are name changes. In order to find entities that have changed their name over
time, search engines need to be aware of this evolution. We tackle this problem
by analyzing Wikipedia in terms of entity evolutions mentioned in articles
regardless the structural elements. We gathered statistics and automatically
extracted minimum excerpts covering name changes by incorporating lists
dedicated to that subject. In future work, these excerpts are going to be used
to discover patterns and detect changes in other sources. In this work we
investigate whether or not Wikipedia is a suitable source for extracting the
required knowledge.
| 2,017 | Computation and Language |
Named Entity Evolution Analysis on Wikipedia | Accessing Web archives raises a number of issues caused by their temporal
characteristics. Additional knowledge is needed to find and understand older
texts. Especially entities mentioned in texts are subject to change. Most
severe in terms of information retrieval are name changes. In order to find
entities that have changed their name over time, search engines need to be
aware of this evolution. We tackle this problem by analyzing Wikipedia in terms
of entity evolutions mentioned in articles. We present statistical data on
excerpts covering name changes, which will be used to discover similar text
passages and extract evolution knowledge in future work.
| 2,017 | Computation and Language |
Extraction of Evolution Descriptions from the Web | The evolution of named entities affects exploration and retrieval tasks in
digital libraries. An information retrieval system that is aware of name
changes can actively support users in finding former occurrences of evolved
entities. However, current structured knowledge bases, such as DBpedia or
Freebase, do not provide enough information about evolutions, even though the
data is available on their resources, like Wikipedia. Our \emph{Evolution Base}
prototype will demonstrate how excerpts describing name evolutions can be
identified on these websites with a promising precision. The descriptions are
classified by means of models that we trained based on a recent analysis of
named entity evolutions on Wikipedia.
| 2,017 | Computation and Language |
Named Entity Evolution Recognition on the Blogosphere | Advancements in technology and culture lead to changes in our language. These
changes create a gap between the language known by users and the language
stored in digital archives. It affects user's possibility to firstly find
content and secondly interpret that content. In previous work we introduced our
approach for Named Entity Evolution Recognition~(NEER) in newspaper
collections. Lately, increasing efforts in Web preservation lead to increased
availability of Web archives covering longer time spans. However, language on
the Web is more dynamic than in traditional media and many of the basic
assumptions from the newspaper domain do not hold for Web data. In this paper
we discuss the limitations of existing methodology for NEER. We approach these
by adapting an existing NEER method to work on noisy data like the Web and the
Blogosphere in particular. We develop novel filters that reduce the noise and
make use of Semantic Web resources to obtain more information about terms. Our
evaluation shows the potentials of the proposed approach.
| 2,015 | Computation and Language |
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation | We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and parts of an image independently by means of two
separate attention mechanisms as it generates words in the target language. We
find that our model can efficiently exploit not just back-translated in-domain
multi-modal data but also large general-domain text-only MT corpora. We also
report state-of-the-art results on the Multi30k data set.
| 2,017 | Computation and Language |
An Empirical Evaluation of Zero Resource Acoustic Unit Discovery | Acoustic unit discovery (AUD) is a process of automatically identifying a
categorical acoustic unit inventory from speech and producing corresponding
acoustic unit tokenizations. AUD provides an important avenue for unsupervised
acoustic model training in a zero resource setting where expert-provided
linguistic knowledge and transcribed speech are unavailable. Therefore, to
further facilitate zero-resource AUD process, in this paper, we demonstrate
acoustic feature representations can be significantly improved by (i)
performing linear discriminant analysis (LDA) in an unsupervised self-trained
fashion, and (ii) leveraging resources of other languages through building a
multilingual bottleneck (BN) feature extractor to give effective cross-lingual
generalization. Moreover, we perform comprehensive evaluations of AUD efficacy
on multiple downstream speech applications, and their correlated performance
suggests that AUD evaluations are feasible using different alternative language
resources when only a subset of these evaluation resources can be available in
typical zero resource applications.
| 2,017 | Computation and Language |
All-but-the-Top: Simple and Effective Postprocessing for Word
Representations | Real-valued word representations have transformed NLP applications; popular
examples are word2vec and GloVe, recognized for their ability to capture
linguistic regularities. In this paper, we demonstrate a {\em very simple}, and
yet counter-intuitive, postprocessing technique -- eliminate the common mean
vector and a few top dominating directions from the word vectors -- that
renders off-the-shelf representations {\em even stronger}. The postprocessing
is empirically validated on a variety of lexical-level intrinsic tasks (word
similarity, concept categorization, word analogy) and sentence-level tasks
(semantic textural similarity and { text classification}) on multiple datasets
and with a variety of representation methods and hyperparameter choices in
multiple languages; in each case, the processed representations are
consistently better than the original ones.
| 2,018 | Computation and Language |
Prepositions in Context | Prepositions are highly polysemous, and their variegated senses encode
significant semantic information. In this paper we match each preposition's
complement and attachment and their interplay crucially to the geometry of the
word vectors to the left and right of the preposition. Extracting such features
from the vast number of instances of each preposition and clustering them makes
for an efficient preposition sense disambigution (PSD) algorithm, which is
comparable to and better than state-of-the-art on two benchmark datasets. Our
reliance on no external linguistic resource allows us to scale the PSD
algorithm to a large WikiCorpus and learn sense-specific preposition
representations -- which we show to encode semantic relations and paraphrasing
of verb particle compounds, via simple vector operations.
| 2,017 | Computation and Language |
Opinion Recommendation using Neural Memory Model | We present opinion recommendation, a novel task of jointly predicting a
custom review with a rating score that a certain user would give to a certain
product or service, given existing reviews and rating scores to the product or
service by other users, and the reviews that the user has given to other
products and services. A characteristic of opinion recommendation is the
reliance of multiple data sources for multi-task joint learning, which is the
strength of neural models. We use a single neural network to model users and
products, capturing their correlation and generating customised product
representations using a deep memory network, from which customised ratings and
reviews are constructed jointly. Results show that our opinion recommendation
system gives ratings that are closer to real user ratings on Yelp.com data
compared with Yelp's own ratings, and our methods give better results compared
to several pipelines baselines using state-of-the-art sentiment rating and
summarization systems.
| 2,017 | Computation and Language |
Neural Semantic Parsing over Multiple Knowledge-bases | A fundamental challenge in developing semantic parsers is the paucity of
strong supervision in the form of language utterances annotated with logical
form. In this paper, we propose to exploit structural regularities in language
in different domains, and train semantic parsers over multiple knowledge-bases
(KBs), while sharing information across datasets. We find that we can
substantially improve parsing accuracy by training a single
sequence-to-sequence model over multiple KBs, when providing an encoding of the
domain at decoding time. Our model achieves state-of-the-art performance on the
Overnight dataset (containing eight domains), improves performance over a
single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the
number of model parameters.
| 2,018 | Computation and Language |
A Hybrid Approach For Hindi-English Machine Translation | In this paper, an extended combined approach of phrase based statistical
machine translation (SMT), example based MT (EBMT) and rule based MT (RBMT) is
proposed to develop a novel hybrid data driven MT system capable of
outperforming the baseline SMT, EBMT and RBMT systems from which it is derived.
In short, the proposed hybrid MT process is guided by the rule based MT after
getting a set of partial candidate translations provided by EBMT and SMT
subsystems. Previous works have shown that EBMT systems are capable of
outperforming the phrase-based SMT systems and RBMT approach has the strength
of generating structurally and morphologically more accurate results. This
hybrid approach increases the fluency, accuracy and grammatical precision which
improve the quality of a machine translation system. A comparison of the
proposed hybrid machine translation (HTM) model with renowned translators i.e.
Google, BING and Babylonian is also presented which shows that the proposed
model works better on sentences with ambiguity as well as comprised of idioms
than others.
| 2,017 | Computation and Language |
Q-WordNet PPV: Simple, Robust and (almost) Unsupervised Generation of
Polarity Lexicons for Multiple Languages | This paper presents a simple, robust and (almost) unsupervised
dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector)
to automatically generate polarity lexicons. We show that qwn-ppv outperforms
other automatically generated lexicons for the four extrinsic evaluations
presented here. It also shows very competitive and robust results with respect
to manually annotated ones. Results suggest that no single lexicon is best for
every task and dataset and that the intrinsic evaluation of polarity lexicons
is not a good performance indicator on a Sentiment Analysis task. The qwn-ppv
method allows to easily create quality polarity lexicons whenever no
domain-based annotated corpora are available for a given language.
| 2,014 | Computation and Language |
DNN adaptation by automatic quality estimation of ASR hypotheses | In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.
| 2,017 | Computation and Language |
Multi-task memory networks for category-specific aspect and opinion
terms co-extraction | In aspect-based sentiment analysis, most existing methods either focus on
aspect/opinion terms extraction or aspect terms categorization. However, each
task by itself only provides partial information to end users. To generate more
detailed and structured opinion analysis, we propose a finer-grained problem,
which we call category-specific aspect and opinion terms extraction. This
problem involves the identification of aspect and opinion terms within each
sentence, as well as the categorization of the identified terms. To this end,
we propose an end-to-end multi-task attention model, where each task
corresponds to aspect/opinion terms extraction for a specific category. Our
model benefits from exploring the commonalities and relationships among
different tasks to address the data sparsity issue. We demonstrate its
state-of-the-art performance on three benchmark datasets.
| 2,017 | Computation and Language |
Ensemble Distillation for Neural Machine Translation | Knowledge distillation describes a method for training a student network to
perform better by learning from a stronger teacher network. Translating a
sentence with an Neural Machine Translation (NMT) engine is time expensive and
having a smaller model speeds up this process. We demonstrate how to transfer
the translation quality of an ensemble and an oracle BLEU teacher network into
a single NMT system. Further, we present translation improvements from a
teacher network that has the same architecture and dimensions of the student
network. As the training of the student model is still expensive, we introduce
a data filtering method based on the knowledge of the teacher model that not
only speeds up the training, but also leads to better translation quality. Our
techniques need no code change and can be easily reproduced with any NMT
architecture to speed up the decoding process.
| 2,017 | Computation and Language |
Beam Search Strategies for Neural Machine Translation | The basic concept in Neural Machine Translation (NMT) is to train a large
Neural Network that maximizes the translation performance on a given parallel
corpus. NMT is then using a simple left-to-right beam-search decoder to
generate new translations that approximately maximize the trained conditional
probability. The current beam search strategy generates the target sentence
word by word from left-to- right while keeping a fixed amount of active
candidates at each time step. First, this simple search is less adaptive as it
also expands candidates whose scores are much worse than the current best.
Secondly, it does not expand hypotheses if they are not within the best scoring
candidates, even if their scores are close to the best one. The latter one can
be avoided by increasing the beam size until no performance improvement can be
observed. While you can reach better performance, this has the draw- back of a
slower decoding speed. In this paper, we concentrate on speeding up the decoder
by applying a more flexible beam search strategy whose candidate size may vary
at each time step depending on the candidate scores. We speed up the original
decoder by up to 43% for the two language pairs German-English and
Chinese-English without losing any translation quality.
| 2,017 | Computation and Language |
Living a discrete life in a continuous world: Reference with distributed
representations | Reference is a crucial property of language that allows us to connect
linguistic expressions to the world. Modeling it requires handling both
continuous and discrete aspects of meaning. Data-driven models excel at the
former, but struggle with the latter, and the reverse is true for symbolic
models.
This paper (a) introduces a concrete referential task to test both aspects,
called cross-modal entity tracking; (b) proposes a neural network architecture
that uses external memory to build an entity library inspired in the DRSs of
DRT, with a mechanism to dynamically introduce new referents or add information
to referents that are already in the library.
Our model shows promise: it beats traditional neural network architectures on
the task. However, it is still outperformed by Memory Networks, another model
with external memory.
| 2,017 | Computation and Language |
Neural Discourse Structure for Text Categorization | We show that discourse structure, as defined by Rhetorical Structure Theory
and provided by an existing discourse parser, benefits text categorization. Our
approach uses a recursive neural network and a newly proposed attention
mechanism to compute a representation of the text that focuses on salient
content, from the perspective of both RST and the task. Experiments consider
variants of the approach and illustrate its strengths and weaknesses.
| 2,017 | Computation and Language |
The Effect of Different Writing Tasks on Linguistic Style: A Case Study
of the ROC Story Cloze Task | A writer's style depends not just on personal traits but also on her intent
and mental state. In this paper, we show how variants of the same writing task
can lead to measurable differences in writing style. We present a case study
based on the story cloze task (Mostafazadeh et al., 2016a), where annotators
were assigned similar writing tasks with different constraints: (1) writing an
entire story, (2) adding a story ending for a given story context, and (3)
adding an incoherent ending to a story. We show that a simple linear classifier
informed by stylistic features is able to successfully distinguish among the
three cases, without even looking at the story context. In addition, combining
our stylistic features with language model predictions reaches state of the art
performance on the story cloze challenge. Our results demonstrate that
different task framings can dramatically affect the way people write.
| 2,017 | Computation and Language |
Comparative Study of CNN and RNN for Natural Language Processing | Deep neural networks (DNN) have revolutionized the field of natural language
processing (NLP). Convolutional neural network (CNN) and recurrent neural
network (RNN), the two main types of DNN architectures, are widely explored to
handle various NLP tasks. CNN is supposed to be good at extracting
position-invariant features and RNN at modeling units in sequence. The state of
the art on many NLP tasks often switches due to the battle between CNNs and
RNNs. This work is the first systematic comparison of CNN and RNN on a wide
range of representative NLP tasks, aiming to give basic guidance for DNN
selection.
| 2,017 | Computation and Language |
Effects of Stop Words Elimination for Arabic Information Retrieval: A
Comparative Study | The effectiveness of three stop words lists for Arabic Information
Retrieval---General Stoplist, Corpus-Based Stoplist, Combined Stoplist ---were
investigated in this study. Three popular weighting schemes were examined: the
inverse document frequency weight, probabilistic weighting, and statistical
language modelling. The Idea is to combine the statistical approaches with
linguistic approaches to reach an optimal performance, and compare their effect
on retrieval. The LDC (Linguistic Data Consortium) Arabic Newswire data set was
used with the Lemur Toolkit. The Best Match weighting scheme used in the Okapi
retrieval system had the best overall performance of the three weighting
algorithms used in the study, stoplists improved retrieval effectiveness
especially when used with the BM25 weight. The overall performance of a general
stoplist was better than the other two lists.
| 2,006 | Computation and Language |
A Knowledge-Grounded Neural Conversation Model | Neural network models are capable of generating extremely natural sounding
conversational interactions. Nevertheless, these models have yet to demonstrate
that they can incorporate content in the form of factual information or
entity-grounded opinion that would enable them to serve in more task-oriented
conversational applications. This paper presents a novel, fully data-driven,
and knowledge-grounded neural conversation model aimed at producing more
contentful responses without slot filling. We generalize the widely-used
Seq2Seq approach by conditioning responses on both conversation history and
external "facts", allowing the model to be versatile and applicable in an
open-domain setting. Our approach yields significant improvements over a
competitive Seq2Seq baseline. Human judges found that our outputs are
significantly more informative.
| 2,018 | Computation and Language |
EliXa: A Modular and Flexible ABSA Platform | This paper presents a supervised Aspect Based Sentiment Analysis (ABSA)
system. Our aim is to develop a modular platform which allows to easily conduct
experiments by replacing the modules or adding new features. We obtain the best
result in the Opinion Target Extraction (OTE) task (slot 2) using an
off-the-shelf sequence labeler. The target polarity classification (slot 3) is
addressed by means of a multiclass SVM algorithm which includes lexical based
features such as the polarity values obtained from domain and open polarity
lexicons. The system obtains accuracies of 0.70 and 0.73 for the restaurant and
laptop domain respectively, and performs second best in the out-of-domain
hotel, achieving an accuracy of 0.80.
| 2,015 | Computation and Language |
Representations of language in a model of visually grounded speech
signal | We present a visually grounded model of speech perception which projects
spoken utterances and images to a joint semantic space. We use a multi-layer
recurrent highway network to model the temporal nature of spoken speech, and
show that it learns to extract both form and meaning-based linguistic knowledge
from the input signal. We carry out an in-depth analysis of the representations
used by different components of the trained model and show that encoding of
semantic aspects tends to become richer as we go up the hierarchy of layers,
whereas encoding of form-related aspects of the language input tends to
initially increase and then plateau or decrease.
| 2,018 | Computation and Language |
Knowledge Adaptation: Teaching to Adapt | Domain adaptation is crucial in many real-world applications where the
distribution of the training data differs from the distribution of the test
data. Previous Deep Learning-based approaches to domain adaptation need to be
trained jointly on source and target domain data and are therefore unappealing
in scenarios where models need to be adapted to a large number of domains or
where a domain is evolving, e.g. spam detection where attackers continuously
change their tactics.
To fill this gap, we propose Knowledge Adaptation, an extension of Knowledge
Distillation (Bucilua et al., 2006; Hinton et al., 2015) to the domain
adaptation scenario. We show how a student model achieves state-of-the-art
results on unsupervised domain adaptation from multiple sources on a standard
sentiment analysis benchmark by taking into account the domain-specific
expertise of multiple teachers and the similarities between their domains.
When learning from a single teacher, using domain similarity to gauge
trustworthiness is inadequate. To this end, we propose a simple metric that
correlates well with the teacher's accuracy in the target domain. We
demonstrate that incorporating high-confidence examples selected by this metric
enables the student model to achieve state-of-the-art performance in the
single-source scenario.
| 2,017 | Computation and Language |
Characterisation of speech diversity using self-organising maps | We report investigations into speaker classification of larger quantities of
unlabelled speech data using small sets of manually phonemically annotated
speech. The Kohonen speech typewriter is a semi-supervised method comprised of
self-organising maps (SOMs) that achieves low phoneme error rates. A SOM is a
2D array of cells that learn vector representations of the data based on
neighbourhoods. In this paper, we report a method to evaluate pronunciation
using multilevel SOMs with /hVd/ single syllable utterances for the study of
vowels, for Australian pronunciation.
| 2,017 | Computation and Language |
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions | Today when many practitioners run basic NLP on the entire web and
large-volume traffic, faster methods are paramount to saving time and energy
costs. Recent advances in GPU hardware have led to the emergence of
bi-directional LSTMs as a standard method for obtaining per-token vector
representations serving as input to labeling tasks such as NER (often followed
by prediction in a linear-chain CRF). Though expressive and accurate, these
models fail to fully exploit GPU parallelism, limiting their computational
efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER:
Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better
capacity than traditional CNNs for large context and structured prediction.
Unlike LSTMs whose sequential processing on sentences of length N requires O(N)
time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions
to run in parallel across entire documents. We describe a distinct combination
of network structure, parameter sharing and training procedures that enable
dramatic 14-20x test-time speedups while retaining accuracy comparable to the
Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire
document are even more accurate while maintaining 8x faster test time speeds.
| 2,017 | Computation and Language |
How to evaluate word embeddings? On importance of data efficiency and
simple supervised tasks | Maybe the single most important goal of representation learning is making
subsequent learning faster. Surprisingly, this fact is not well reflected in
the way embeddings are evaluated. In addition, recent practice in word
embeddings points towards importance of learning specialized representations.
We argue that focus of word representation evaluation should reflect those
trends and shift towards evaluating what useful information is easily
accessible. Specifically, we propose that evaluation should focus on data
efficiency and simple supervised tasks, where the amount of available data is
varied and scores of a supervised model are reported for each subset (as
commonly done in transfer learning).
In order to illustrate significance of such analysis, a comprehensive
evaluation of selected word embeddings is presented. Proposed approach yields a
more complete picture and brings new insight into performance characteristics,
for instance information about word similarity or analogy tends to be
non--linearly encoded in the embedding space, which questions the cosine-based,
unsupervised, evaluation methods. All results and analysis scripts are
available online.
| 2,017 | Computation and Language |
Question Answering through Transfer Learning from Large Fine-grained
Supervision Data | We show that the task of question answering (QA) can significantly benefit
from the transfer learning of models trained on a different large, fine-grained
QA dataset. We achieve the state of the art in two well-studied QA datasets,
WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique
from SQuAD. For WikiQA, our model outperforms the previous best model by more
than 8%. We demonstrate that finer supervision provides better guidance for
learning lexical and syntactic information than coarser supervision, through
quantitative results and visual analysis. We also show that a similar transfer
learning procedure achieves the state of the art on an entailment task.
| 2,018 | Computation and Language |
Semi-Supervised QA with Generative Domain-Adaptive Nets | We study the problem of semi-supervised question answering----utilizing
unlabeled text to boost the performance of question answering models. We
propose a novel training framework, the Generative Domain-Adaptive Nets. In
this framework, we train a generative model to generate questions based on the
unlabeled text, and combine model-generated questions with human-generated
questions for training question answering models. We develop novel domain
adaptation algorithms, based on reinforcement learning, to alleviate the
discrepancy between the model-generated data distribution and the
human-generated data distribution. Experiments show that our proposed framework
obtains substantial improvement from unlabeled text.
| 2,017 | Computation and Language |
Fixing the Infix: Unsupervised Discovery of Root-and-Pattern Morphology | We present an unsupervised and language-agnostic method for learning
root-and-pattern morphology in Semitic languages. This form of morphology,
abundant in Semitic languages, has not been handled in prior unsupervised
approaches. We harness the syntactico-semantic information in distributed word
representations to solve the long standing problem of root-and-pattern
discovery in Semitic languages. Moreover, we construct an unsupervised root
extractor based on the learned rules. We prove the validity of learned rules
across Arabic, Hebrew, and Amharic, alongside showing that our root extractor
compares favorably with a widely used, carefully engineered root extractor:
ISRI.
| 2,017 | Computation and Language |
MORSE: Semantic-ally Drive-n MORpheme SEgment-er | We present in this paper a novel framework for morpheme segmentation which
uses the morpho-syntactic regularities preserved by word representations, in
addition to orthographic features, to segment words into morphemes. This
framework is the first to consider vocabulary-wide syntactico-semantic
information for this task. We also analyze the deficiencies of available
benchmarking datasets and introduce our own dataset that was created on the
basis of compositionality. We validate our algorithm across datasets and
present state-of-the-art results.
| 2,017 | Computation and Language |
Social media mining for identification and exploration of health-related
information from pregnant women | Widespread use of social media has led to the generation of substantial
amounts of information about individuals, including health-related information.
Social media provides the opportunity to study health-related information about
selected population groups who may be of interest for a particular study. In
this paper, we explore the possibility of utilizing social media to perform
targeted data collection and analysis from a particular population group --
pregnant women. We hypothesize that we can use social media to identify cohorts
of pregnant women and follow them over time to analyze crucial health-related
information. To identify potentially pregnant women, we employ simple
rule-based searches that attempt to detect pregnancy announcements with
moderate precision. To further filter out false positives and noise, we employ
a supervised classifier using a small number of hand-annotated data. We then
collect their posts over time to create longitudinal health timelines and
attempt to divide the timelines into different pregnancy trimesters. Finally,
we assess the usefulness of the timelines by performing a preliminary analysis
to estimate drug intake patterns of our cohort at different trimesters. Our
rule-based cohort identification technique collected 53,820 users over thirty
months from Twitter. Our pregnancy announcement classification technique
achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user
timelines. Analysis of the timelines revealed that pertinent health-related
information, such as drug-intake and adverse reactions can be mined from the
data. Our approach to using user timelines in this fashion has produced very
encouraging results and can be employed for other important tasks where
cohorts, for which health-related information may not be available from other
sources, are required to be followed over time to derive population-based
estimates.
| 2,017 | Computation and Language |
Neural Machine Translation with Source-Side Latent Graph Parsing | This paper presents a novel neural machine translation model which jointly
learns translation and source-side latent graph representations of sentences.
Unlike existing pipelined approaches using syntactic parsers, our end-to-end
model learns a latent graph parser as part of the encoder of an attention-based
neural machine translation model, and thus the parser is optimized according to
the translation objective. In experiments, we first show that our model
compares favorably with state-of-the-art sequential and pipelined syntax-based
NMT models. We also show that the performance of our model can be further
improved by pre-training it with a small amount of treebank annotations. Our
final ensemble model significantly outperforms the previous best models on the
standard English-to-Japanese translation dataset.
| 2,017 | Computation and Language |
Automatically Annotated Turkish Corpus for Named Entity Recognition and
Text Categorization using Large-Scale Gazetteers | Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC)
dataset is a collection of automatically categorized and annotated sentences
obtained from Wikipedia. We constructed large-scale gazetteers by using a graph
crawler algorithm to extract relevant entity and domain information from a
semantic knowledge base, Freebase. The constructed gazetteers contains
approximately 300K entities with thousands of fine-grained entity types under
77 different domains. Since automated processes are prone to ambiguity, we also
introduce two new content specific noise reduction methodologies. Moreover, we
map fine-grained entity types to the equivalent four coarse-grained types:
person, loc, org, misc. Eventually, we construct six different dataset versions
and evaluate the quality of annotations by comparing ground truths from human
annotators. We make these datasets publicly available to support studies on
Turkish named-entity recognition (NER) and text categorization (TC).
| 2,017 | Computation and Language |
Iterative Multi-document Neural Attention for Multiple Answer Prediction | People have information needs of varying complexity, which can be solved by
an intelligent agent able to answer questions formulated in a proper way,
eventually considering user context and preferences. In a scenario in which the
user profile can be considered as a question, intelligent agents able to answer
questions can be used to find the most relevant answers for a given user. In
this work we propose a novel model based on Artificial Neural Networks to
answer questions with multiple answers by exploiting multiple facts retrieved
from a knowledge base. The model is evaluated on the factoid Question Answering
and top-n recommendation tasks of the bAbI Movie Dialog dataset. After
assessing the performance of the model on both tasks, we try to define the
long-term goal of a conversational recommender system able to interact using
natural language and to support users in their information seeking processes in
a personalized way.
| 2,017 | Computation and Language |
A Hybrid Convolutional Variational Autoencoder for Text Generation | In this paper we explore the effect of architectural choices on learning a
Variational Autoencoder (VAE) for text generation. In contrast to the
previously introduced VAE model for text where both the encoder and decoder are
RNNs, we propose a novel hybrid architecture that blends fully feed-forward
convolutional and deconvolutional components with a recurrent language model.
Our architecture exhibits several attractive properties such as faster run time
and convergence, ability to better handle long sequences and, more importantly,
it helps to avoid some of the major difficulties posed by training VAE models
on textual data.
| 2,017 | Computation and Language |
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