Titles
stringlengths 6
220
| Abstracts
stringlengths 37
3.26k
| Years
int64 1.99k
2.02k
| Categories
stringclasses 1
value |
|---|---|---|---|
Attentive Tensor Product Learning | This paper proposes a new architecture - Attentive Tensor Product Learning
(ATPL) - to represent grammatical structures in deep learning models. ATPL is a
new architecture to bridge this gap by exploiting Tensor Product
Representations (TPR), a structured neural-symbolic model developed in
cognitive science, aiming to integrate deep learning with explicit language
structures and rules. The key ideas of ATPL are: 1) unsupervised learning of
role-unbinding vectors of words via TPR-based deep neural network; 2) employing
attention modules to compute TPR; and 3) integration of TPR with typical deep
learning architectures including Long Short-Term Memory (LSTM) and Feedforward
Neural Network (FFNN). The novelty of our approach lies in its ability to
extract the grammatical structure of a sentence by using role-unbinding
vectors, which are obtained in an unsupervised manner. This ATPL approach is
applied to 1) image captioning, 2) part of speech (POS) tagging, and 3)
constituency parsing of a sentence. Experimental results demonstrate the
effectiveness of the proposed approach.
| 2,018 | Computation and Language |
Combining Textual Content and Structure to Improve Dialog Similarity | Chatbots, taking advantage of the success of the messaging apps and recent
advances in Artificial Intelligence, have become very popular, from helping
business to improve customer services to chatting to users for the sake of
conversation and engagement (celebrity or personal bots). However, developing
and improving a chatbot requires understanding their data generated by its
users. Dialog data has a different nature of a simple question and answering
interaction, in which context and temporal properties (turn order) creates a
different understanding of such data. In this paper, we propose a novelty
metric to compute dialogs' similarity based not only on the text content but
also on the information related to the dialog structure. Our experimental
results performed over the Switchboard dataset show that using evidence from
both textual content and the dialog structure leads to more accurate results
than using each measure in isolation.
| 2,018 | Computation and Language |
CytonMT: an Efficient Neural Machine Translation Open-source Toolkit
Implemented in C++ | This paper presents an open-source neural machine translation toolkit named
CytonMT (https://github.com/arthurxlw/cytonMt). The toolkit is built from
scratch only using C++ and NVIDIA's GPU-accelerated libraries. The toolkit
features training efficiency, code simplicity and translation quality.
Benchmarks show that CytonMT accelerates the training speed by 64.5% to 110.8%
on neural networks of various sizes, and achieves competitive translation
quality.
| 2,018 | Computation and Language |
Implicit Argument Prediction with Event Knowledge | Implicit arguments are not syntactically connected to their predicates, and
are therefore hard to extract. Previous work has used models with large numbers
of features, evaluated on very small datasets. We propose to train models for
implicit argument prediction on a simple cloze task, for which data can be
generated automatically at scale. This allows us to use a neural model, which
draws on narrative coherence and entity salience for predictions. We show that
our model has superior performance on both synthetic and natural data.
| 2,018 | Computation and Language |
SufiSent - Universal Sentence Representations Using Suffix Encodings | Computing universal distributed representations of sentences is a fundamental
task in natural language processing. We propose a method to learn such
representations by encoding the suffixes of word sequences in a sentence and
training on the Stanford Natural Language Inference (SNLI) dataset. We
demonstrate the effectiveness of our approach by evaluating it on the SentEval
benchmark, improving on existing approaches on several transfer tasks.
| 2,018 | Computation and Language |
On the scaling of polynomial features for representation matching | In many neural models, new features as polynomial functions of existing ones
are used to augment representations. Using the natural language inference task
as an example, we investigate the use of scaled polynomials of degree 2 and
above as matching features. We find that scaling degree 2 features has the
highest impact on performance, reducing classification error by 5% in the best
models.
| 2,018 | Computation and Language |
Sequence-based Multi-lingual Low Resource Speech Recognition | Techniques for multi-lingual and cross-lingual speech recognition can help in
low resource scenarios, to bootstrap systems and enable analysis of new
languages and domains. End-to-end approaches, in particular sequence-based
techniques, are attractive because of their simplicity and elegance. While it
is possible to integrate traditional multi-lingual bottleneck feature
extractors as front-ends, we show that end-to-end multi-lingual training of
sequence models is effective on context independent models trained using
Connectionist Temporal Classification (CTC) loss. We show that our model
improves performance on Babel languages by over 6% absolute in terms of
word/phoneme error rate when compared to mono-lingual systems built in the same
setting for these languages. We also show that the trained model can be adapted
cross-lingually to an unseen language using just 25% of the target data. We
show that training on multiple languages is important for very low resource
cross-lingual target scenarios, but not for multi-lingual testing scenarios.
Here, it appears beneficial to include large well prepared datasets.
| 2,018 | Computation and Language |
Matching Article Pairs with Graphical Decomposition and Convolutions | Identifying the relationship between two articles, e.g., whether two articles
published from different sources describe the same breaking news, is critical
to many document understanding tasks. Existing approaches for modeling and
matching sentence pairs do not perform well in matching longer documents, which
embody more complex interactions between the enclosed entities than a sentence
does. To model article pairs, we propose the Concept Interaction Graph to
represent an article as a graph of concepts. We then match a pair of articles
by comparing the sentences that enclose the same concept vertex through a
series of encoding techniques, and aggregate the matching signals through a
graph convolutional network. To facilitate the evaluation of long article
matching, we have created two datasets, each consisting of about 30K pairs of
breaking news articles covering diverse topics in the open domain. Extensive
evaluations of the proposed methods on the two datasets demonstrate significant
improvements over a wide range of state-of-the-art methods for natural language
matching.
| 2,019 | Computation and Language |
CoVeR: Learning Covariate-Specific Vector Representations with Tensor
Decompositions | Word embedding is a useful approach to capture co-occurrence structures in
large text corpora. However, in addition to the text data itself, we often have
additional covariates associated with individual corpus documents---e.g. the
demographic of the author, time and venue of publication---and we would like
the embedding to naturally capture this information. We propose CoVeR, a new
tensor decomposition model for vector embeddings with covariates. CoVeR jointly
learns a \emph{base} embedding for all the words as well as a weighted diagonal
matrix to model how each covariate affects the base embedding. To obtain author
or venue-specific embedding, for example, we can then simply multiply the base
embedding by the associated transformation matrix. The main advantages of our
approach are data efficiency and interpretability of the covariate
transformation. Our experiments demonstrate that our joint model learns
substantially better covariate-specific embeddings compared to the standard
approach of learning a separate embedding for each covariate using only the
relevant subset of data, as well as other related methods. Furthermore, CoVeR
encourages the embeddings to be "topic-aligned" in that the dimensions have
specific independent meanings. This allows our covariate-specific embeddings to
be compared by topic, enabling downstream differential analysis. We empirically
evaluate the benefits of our algorithm on datasets, and demonstrate how it can
be used to address many natural questions about covariate effects.
Accompanying code to this paper can be found at
http://github.com/kjtian/CoVeR.
| 2,018 | Computation and Language |
MPST: A Corpus of Movie Plot Synopses with Tags | Social tagging of movies reveals a wide range of heterogeneous information
about movies, like the genre, plot structure, soundtracks, metadata, visual and
emotional experiences. Such information can be valuable in building automatic
systems to create tags for movies. Automatic tagging systems can help
recommendation engines to improve the retrieval of similar movies as well as
help viewers to know what to expect from a movie in advance. In this paper, we
set out to the task of collecting a corpus of movie plot synopses and tags. We
describe a methodology that enabled us to build a fine-grained set of around 70
tags exposing heterogeneous characteristics of movie plots and the multi-label
associations of these tags with some 14K movie plot synopses. We investigate
how these tags correlate with movies and the flow of emotions throughout
different types of movies. Finally, we use this corpus to explore the
feasibility of inferring tags from plot synopses. We expect the corpus will be
useful in other tasks where analysis of narratives is relevant.
| 2,018 | Computation and Language |
Multimodal Named Entity Recognition for Short Social Media Posts | We introduce a new task called Multimodal Named Entity Recognition (MNER) for
noisy user-generated data such as tweets or Snapchat captions, which comprise
short text with accompanying images. These social media posts often come in
inconsistent or incomplete syntax and lexical notations with very limited
surrounding textual contexts, bringing significant challenges for NER. To this
end, we create a new dataset for MNER called SnapCaptions (Snapchat
image-caption pairs submitted to public and crowd-sourced stories with fully
annotated named entities). We then build upon the state-of-the-art Bi-LSTM
word/character based NER models with 1) a deep image network which incorporates
relevant visual context to augment textual information, and 2) a generic
modality-attention module which learns to attenuate irrelevant modalities while
amplifying the most informative ones to extract contexts from, adaptive to each
sample and token. The proposed MNER model with modality attention significantly
outperforms the state-of-the-art text-only NER models by successfully
leveraging provided visual contexts, opening up potential applications of MNER
on myriads of social media platforms.
| 2,018 | Computation and Language |
LIDIOMS: A Multilingual Linked Idioms Data Set | In this paper, we describe the LIDIOMS data set, a multilingual RDF
representation of idioms currently containing five languages: English, German,
Italian, Portuguese, and Russian. The data set is intended to support natural
language processing applications by providing links between idioms across
languages. The underlying data was crawled and integrated from various sources.
To ensure the quality of the crawled data, all idioms were evaluated by at
least two native speakers. Herein, we present the model devised for structuring
the data. We also provide the details of linking LIDIOMS to well-known
multilingual data sets such as BabelNet. The resulting data set complies with
best practices according to Linguistic Linked Open Data Community.
| 2,018 | Computation and Language |
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data | The generation of natural language from Resource Description Framework (RDF)
data has recently gained significant attention due to the continuous growth of
Linked Data. A number of these approaches generate natural language in
languages other than English, however, no work has been proposed to generate
Brazilian Portuguese texts out of RDF. We address this research gap by
presenting RDF2PT, an approach that verbalizes RDF data to Brazilian Portuguese
language. We evaluated RDF2PT in an open questionnaire with 44 native speakers
divided into experts and non-experts. Our results suggest that RDF2PT is able
to generate text which is similar to that generated by humans and can hence be
easily understood.
| 2,018 | Computation and Language |
Content-Based Citation Recommendation | We present a content-based method for recommending citations in an academic
paper draft. We embed a given query document into a vector space, then use its
nearest neighbors as candidates, and rerank the candidates using a
discriminative model trained to distinguish between observed and unobserved
citations. Unlike previous work, our method does not require metadata such as
author names which can be missing, e.g., during the peer review process.
Without using metadata, our method outperforms the best reported results on
PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and
over 22% in MRR. We show empirically that, although adding metadata improves
the performance on standard metrics, it favors self-citations which are less
useful in a citation recommendation setup. We release an online portal
(http://labs.semanticscholar.org/citeomatic/) for citation recommendation based
on our method, and a new dataset OpenCorpus of 7 million research articles to
facilitate future research on this task.
| 2,018 | Computation and Language |
High Order Recurrent Neural Networks for Acoustic Modelling | Vanishing long-term gradients are a major issue in training standard
recurrent neural networks (RNNs), which can be alleviated by long short-term
memory (LSTM) models with memory cells. However, the extra parameters
associated with the memory cells mean an LSTM layer has four times as many
parameters as an RNN with the same hidden vector size. This paper addresses the
vanishing gradient problem using a high order RNN (HORNN) which has additional
connections from multiple previous time steps. Speech recognition experiments
using British English multi-genre broadcast (MGB3) data showed that the
proposed HORNN architectures for rectified linear unit and sigmoid activation
functions reduced word error rates (WER) by 4.2% and 6.3% over the
corresponding RNNs, and gave similar WERs to a (projected) LSTM while using
only 20%--50% of the recurrent layer parameters and computation.
| 2,018 | Computation and Language |
Deep Multimodal Learning for Emotion Recognition in Spoken Language | In this paper, we present a novel deep multimodal framework to predict human
emotions based on sentence-level spoken language. Our architecture has two
distinctive characteristics. First, it extracts the high-level features from
both text and audio via a hybrid deep multimodal structure, which considers the
spatial information from text, temporal information from audio, and high-level
associations from low-level handcrafted features. Second, we fuse all features
by using a three-layer deep neural network to learn the correlations across
modalities and train the feature extraction and fusion modules together,
allowing optimal global fine-tuning of the entire structure. We evaluated the
proposed framework on the IEMOCAP dataset. Our result shows promising
performance, achieving 60.4% in weighted accuracy for five emotion categories.
| 2,018 | Computation and Language |
Reusing Weights in Subword-aware Neural Language Models | We propose several ways of reusing subword embeddings and other weights in
subword-aware neural language models. The proposed techniques do not benefit a
competitive character-aware model, but some of them improve the performance of
syllable- and morpheme-aware models while showing significant reductions in
model sizes. We discover a simple hands-on principle: in a multi-layer input
embedding model, layers should be tied consecutively bottom-up if reused at
output. Our best morpheme-aware model with properly reused weights beats the
competitive word-level model by a large margin across multiple languages and
has 20%-87% fewer parameters.
| 2,018 | Computation and Language |
EmotionLines: An Emotion Corpus of Multi-Party Conversations | Feeling emotion is a critical characteristic to distinguish people from
machines. Among all the multi-modal resources for emotion detection, textual
datasets are those containing the least additional information in addition to
semantics, and hence are adopted widely for testing the developed systems.
However, most of the textual emotional datasets consist of emotion labels of
only individual words, sentences or documents, which makes it challenging to
discuss the contextual flow of emotions. In this paper, we introduce
EmotionLines, the first dataset with emotions labeling on all utterances in
each dialogue only based on their textual content. Dialogues in EmotionLines
are collected from Friends TV scripts and private Facebook messenger dialogues.
Then one of seven emotions, six Ekman's basic emotions plus the neutral
emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245
utterances from 2,000 dialogues are labeled in EmotionLines. We also provide
several strong baselines for emotion detection models on EmotionLines in this
paper.
| 2,018 | Computation and Language |
Towards end-to-end spoken language understanding | Spoken language understanding system is traditionally designed as a pipeline
of a number of components. First, the audio signal is processed by an automatic
speech recognizer for transcription or n-best hypotheses. With the recognition
results, a natural language understanding system classifies the text to
structured data as domain, intent and slots for down-streaming consumers, such
as dialog system, hands-free applications. These components are usually
developed and optimized independently. In this paper, we present our study on
an end-to-end learning system for spoken language understanding. With this
unified approach, we can infer the semantic meaning directly from audio
features without the intermediate text representation. This study showed that
the trained model can achieve reasonable good result and demonstrated that the
model can capture the semantic attention directly from the audio features.
| 2,018 | Computation and Language |
Interpretable Charge Predictions for Criminal Cases: Learning to
Generate Court Views from Fact Descriptions | In this paper, we propose to study the problem of COURT VIEW GENeration from
the fact description in a criminal case. The task aims to improve the
interpretability of charge prediction systems and help automatic legal document
generation. We formulate this task as a text-to-text natural language
generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge
performances in many NLG tasks. However, due to the non-distinctions of fact
descriptions, it is hard for Seq2Seq model to generate charge-discriminative
court views. In this work, we explore charge labels to tackle this issue. We
propose a label-conditioned Seq2Seq model with attention for this problem, to
decode court views conditioned on encoded charge labels. Experimental results
show the effectiveness of our method.
| 2,018 | Computation and Language |
Unsupervised Grammar Induction with Depth-bounded PCFG | There has been recent interest in applying cognitively or empirically
motivated bounds on recursion depth to limit the search space of grammar
induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al.,
2016). This work extends this depth-bounding approach to probabilistic
context-free grammar induction (DB-PCFG), which has a smaller parameter space
than hierarchical sequence models, and therefore more fully exploits the space
reductions of depth-bounding. Results for this model on grammar acquisition
from transcribed child-directed speech and newswire text exceed or are
competitive with those of other models when evaluated on parse accuracy.
Moreover, gram- mars acquired from this model demonstrate a consistent use of
category labels, something which has not been demonstrated by other acquisition
models.
| 2,018 | Computation and Language |
Evaluating Scoped Meaning Representations | Semantic parsing offers many opportunities to improve natural language
understanding. We present a semantically annotated parallel corpus for English,
German, Italian, and Dutch where sentences are aligned with scoped meaning
representations in order to capture the semantics of negation, modals,
quantification, and presupposition triggers. The semantic formalism is based on
Discourse Representation Theory, but concepts are represented by WordNet
synsets and thematic roles by VerbNet relations. Translating scoped meaning
representations to sets of clauses enables us to compare them for the purpose
of semantic parser evaluation and checking translations. This is done by
computing precision and recall on matching clauses, in a similar way as is done
for Abstract Meaning Representations. We show that our matching tool for
evaluating scoped meaning representations is both accurate and efficient.
Applying this matching tool to three baseline semantic parsers yields F-scores
between 43% and 54%. A pilot study is performed to automatically find changes
in meaning by comparing meaning representations of translations. This
comparison turns out to be an additional way of (i) finding annotation mistakes
and (ii) finding instances where our semantic analysis needs to be improved.
| 2,018 | Computation and Language |
Visualizing the Flow of Discourse with a Concept Ontology | Understanding and visualizing human discourse has long being a challenging
task. Although recent work on argument mining have shown success in classifying
the role of various sentences, the task of recognizing concepts and
understanding the ways in which they are discussed remains challenging. Given
an email thread or a transcript of a group discussion, our task is to extract
the relevant concepts and understand how they are referenced and re-referenced
throughout the discussion. In the present work, we present a preliminary
approach for extracting and visualizing group discourse by adapting Wikipedia's
category hierarchy to be an external concept ontology. From a user study, we
found that our method achieved better results than 4 strong alternative
approaches, and we illustrate our visualization method based on the extracted
discourse flows.
| 2,018 | Computation and Language |
Ranking Sentences for Extractive Summarization with Reinforcement
Learning | Single document summarization is the task of producing a shorter version of a
document while preserving its principal information content. In this paper we
conceptualize extractive summarization as a sentence ranking task and propose a
novel training algorithm which globally optimizes the ROUGE evaluation metric
through a reinforcement learning objective. We use our algorithm to train a
neural summarization model on the CNN and DailyMail datasets and demonstrate
experimentally that it outperforms state-of-the-art extractive and abstractive
systems when evaluated automatically and by humans.
| 2,018 | Computation and Language |
Automatic Speech Recognition and Topic Identification for
Almost-Zero-Resource Languages | Automatic speech recognition (ASR) systems often need to be developed for
extremely low-resource languages to serve end-uses such as audio content
categorization and search. While universal phone recognition is natural to
consider when no transcribed speech is available to train an ASR system in a
language, adapting universal phone models using very small amounts (minutes
rather than hours) of transcribed speech also needs to be studied, particularly
with state-of-the-art DNN-based acoustic models. The DARPA LORELEI program
provides a framework for such very-low-resource ASR studies, and provides an
extrinsic metric for evaluating ASR performance in a humanitarian assistance,
disaster relief setting. This paper presents our Kaldi-based systems for the
program, which employ a universal phone modeling approach to ASR, and describes
recipes for very rapid adaptation of this universal ASR system. The results we
obtain significantly outperform results obtained by many competing approaches
on the NIST LoReHLT 2017 Evaluation datasets.
| 2,018 | Computation and Language |
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for
Relation Classification in Scientific Papers using Piecewise Convolutional
Neural Networks | We describe our system for SemEval-2018 Shared Task on Semantic Relation
Extraction and Classification in Scientific Papers where we focus on the
Classification task. Our simple piecewise convolution neural encoder performs
decently in an end to end manner. A simple inter-task data augmentation
signifi- cantly boosts the performance of the model. Our best-performing
systems stood 8th out of 20 teams on the classification task on noisy data and
12th out of 28 teams on the classification task on clean data.
| 2,018 | Computation and Language |
Incorporating Discriminator in Sentence Generation: a Gibbs Sampling
Method | Generating plausible and fluent sentence with desired properties has long
been a challenge. Most of the recent works use recurrent neural networks (RNNs)
and their variants to predict following words given previous sequence and
target label. In this paper, we propose a novel framework to generate
constrained sentences via Gibbs Sampling. The candidate sentences are revised
and updated iteratively, with sampled new words replacing old ones. Our
experiments show the effectiveness of the proposed method to generate plausible
and diverse sentences.
| 2,018 | Computation and Language |
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to
the Linux Operating System | We present new data and semantic parsing methods for the problem of mapping
English sentences to Bash commands (NL2Bash). Our long-term goal is to enable
any user to perform operations such as file manipulation, search, and
application-specific scripting by simply stating their goals in English. We
take a first step in this domain, by providing a new dataset of challenging but
commonly used Bash commands and expert-written English descriptions, along with
baseline methods to establish performance levels on this task.
| 2,018 | Computation and Language |
Revisiting the poverty of the stimulus: hierarchical generalization
without a hierarchical bias in recurrent neural networks | Syntactic rules in natural language typically need to make reference to
hierarchical sentence structure. However, the simple examples that language
learners receive are often equally compatible with linear rules. Children
consistently ignore these linear explanations and settle instead on the correct
hierarchical one. This fact has motivated the proposal that the learner's
hypothesis space is constrained to include only hierarchical rules. We examine
this proposal using recurrent neural networks (RNNs), which are not constrained
in such a way. We simulate the acquisition of question formation, a
hierarchical transformation, in a fragment of English. We find that some RNN
architectures tend to learn the hierarchical rule, suggesting that hierarchical
cues within the language, combined with the implicit architectural biases
inherent in certain RNNs, may be sufficient to induce hierarchical
generalizations. The likelihood of acquiring the hierarchical generalization
increased when the language included an additional cue to hierarchy in the form
of subject-verb agreement, underscoring the role of cues to hierarchy in the
learner's input.
| 2,018 | Computation and Language |
Did You Really Just Have a Heart Attack? Towards Robust Detection of
Personal Health Mentions in Social Media | Millions of users share their experiences on social media sites, such as
Twitter, which in turn generate valuable data for public health monitoring,
digital epidemiology, and other analyses of population health at global scale.
The first, critical, task for these applications is classifying whether a
personal health event was mentioned, which we call the (PHM) problem. This task
is challenging for many reasons, including typically short length of social
media posts, inventive spelling and lexicons, and figurative language,
including hyperbole using diseases like "heart attack" or "cancer" for
emphasis, and not as a health self-report. This problem is even more
challenging for rarely reported, or frequent but ambiguously expressed
conditions, such as "stroke". To address this problem, we propose a general,
robust method for detecting PHMs in social media, which we call WESPAD, that
combines lexical, syntactic, word embedding-based, and context-based features.
WESPAD is able to generalize from few examples by automatically distorting the
word embedding space to most effectively detect the true health mentions.
Unlike previously proposed state-of-the-art supervised and deep-learning
techniques, WESPAD requires relatively little training data, which makes it
possible to adapt, with minimal effort, to each new disease and condition. We
evaluate WESPAD on both an established publicly available Flu detection
benchmark, and on a new dataset that we have constructed with mentions of
multiple health conditions. Our experiments show that WESPAD outperforms the
baselines and state-of-the-art methods, especially in cases when the number and
proportion of true health mentions in the training data is small.
| 2,018 | Computation and Language |
Language Distribution Prediction based on Batch Markov Monte Carlo
Simulation with Migration | Language spreading is a complex mechanism that involves issues like culture,
economics, migration, population etc. In this paper, we propose a set of
methods to model the dynamics of the spreading system. To model the randomness
of language spreading, we propose the Batch Markov Monte Carlo Simulation with
Migration(BMMCSM) algorithm, in which each agent is treated as a language
stack. The agent learns languages and migrates based on the proposed Batch
Markov Property according to the transition matrix T and migration matrix M.
Since population plays a crucial role in language spreading, we also introduce
the Mortality and Fertility Mechanism, which controls the birth and death of
the simulated agents, into the BMMCSM algorithm. The simulation results of
BMMCSM show that the numerical and geographic distribution of languages varies
across the time. The change of distribution fits the world cultural and
economic development trend. Next, when we construct Matrix T, there are some
entries of T can be directly calculated from historical statistics while some
entries of T is unknown. Thus, the key to the success of the BMMCSM lies in the
accurate estimation of transition matrix T by estimating the unknown entries of
T under the supervision of the known entries. To achieve this, we first
construct a 20 by 20 by 5 factor tensor X to characterize each entry of T. Then
we train a Random Forest Regressor on the known entries of T and use the
trained regressor to predict the unknown entries. The reason why we choose
Random Forest(RF) is that, compared to Single Decision Tree, it conquers the
problem of over fitting and the Shapiro test also suggests that the residual of
RF subjects to the Normal distribution.
| 2,018 | Computation and Language |
Deep Feed-forward Sequential Memory Networks for Speech Synthesis | The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among the
best parametric Text-to-Speech (TTS) systems in terms of the naturalness of
generated speech, especially the naturalness in prosody. However, the model
complexity and inference cost of BLSTM prevents its usage in many runtime
applications. Meanwhile, Deep Feed-forward Sequential Memory Networks (DFSMN)
has shown its consistent out-performance over BLSTM in both word error rate
(WER) and the runtime computation cost in speech recognition tasks. Since
speech synthesis also requires to model long-term dependencies compared to
speech recognition, in this paper, we investigate the Deep-FSMN (DFSMN) in
speech synthesis. Both objective and subjective experiments show that, compared
with BLSTM TTS method, the DFSMN system can generate synthesized speech with
comparable speech quality while drastically reduce model complexity and speech
generation time.
| 2,018 | Computation and Language |
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and
XGboost Regressors for Emotion Analysis of Tweets | This paper describes our system that has been used in Task1 Affect in Tweets.
We combine two different approaches. The first one called N-Stream ConvNets,
which is a deep learning approach where the second one is XGboost regresseor
based on a set of embedding and lexicons based features. Our system was
evaluated on the testing sets of the tasks outperforming all other approaches
for the Arabic version of valence intensity regression task and valence ordinal
classification task.
| 2,018 | Computation and Language |
Gender Aware Spoken Language Translation Applied to English-Arabic | Spoken Language Translation (SLT) is becoming more widely used and becoming a
communication tool that helps in crossing language barriers. One of the
challenges of SLT is the translation from a language without gender agreement
to a language with gender agreement such as English to Arabic. In this paper,
we introduce an approach to tackle such limitation by enabling a Neural Machine
Translation system to produce gender-aware translation. We show that NMT system
can model the speaker/listener gender information to produce gender-aware
translation. We propose a method to generate data used in adapting a NMT system
to produce gender-aware. The proposed approach can achieve significant
improvement of the translation quality by 2 BLEU points.
| 2,018 | Computation and Language |
From Phonology to Syntax: Unsupervised Linguistic Typology at Different
Levels with Language Embeddings | A core part of linguistic typology is the classification of languages
according to linguistic properties, such as those detailed in the World Atlas
of Language Structure (WALS). Doing this manually is prohibitively
time-consuming, which is in part evidenced by the fact that only 100 out of
over 7,000 languages spoken in the world are fully covered in WALS.
We learn distributed language representations, which can be used to predict
typological properties on a massively multilingual scale. Additionally,
quantitative and qualitative analyses of these language embeddings can tell us
how language similarities are encoded in NLP models for tasks at different
typological levels. The representations are learned in an unsupervised manner
alongside tasks at three typological levels: phonology (grapheme-to-phoneme
prediction, and phoneme reconstruction), morphology (morphological inflection),
and syntax (part-of-speech tagging).
We consider more than 800 languages and find significant differences in the
language representations encoded, depending on the target task. For instance,
although Norwegian Bokm{\aa}l and Danish are typologically close to one
another, they are phonologically distant, which is reflected in their language
embeddings growing relatively distant in a phonological task. We are also able
to predict typological features in WALS with high accuracies, even for unseen
language families.
| 2,018 | Computation and Language |
A Quality Type-aware Annotated Corpus and Lexicon for Harassment
Research | Having a quality annotated corpus is essential especially for applied
research. Despite the recent focus of Web science community on researching
about cyberbullying, the community dose not still have standard benchmarks. In
this paper, we publish first, a quality annotated corpus and second, an
offensive words lexicon capturing different types type of harassment as (i)
sexual harassment, (ii) racial harassment, (iii) appearance-related harassment,
(iv) intellectual harassment, and (v) political harassment.We crawled data from
Twitter using our offensive lexicon. Then relied on the human judge to annotate
the collected tweets w.r.t. the contextual types because using offensive words
is not sufficient to reliably detect harassment. Our corpus consists of 25,000
annotated tweets in five contextual types. We are pleased to share this novel
annotated corpus and the lexicon with the research community. The instruction
to acquire the corpus has been published on the Git repository.
| 2,018 | Computation and Language |
Live Blog Corpus for Summarization | Live blogs are an increasingly popular news format to cover breaking news and
live events in online journalism. Online news websites around the world are
using this medium to give their readers a minute by minute update on an event.
Good summaries enhance the value of the live blogs for a reader but are often
not available. In this paper, we study a way of collecting corpora for
automatic live blog summarization. In an empirical evaluation using well-known
state-of-the-art summarization systems, we show that live blogs corpus poses
new challenges in the field of summarization. We make our tools publicly
available to reconstruct the corpus to encourage the research community and
replicate our results.
| 2,018 | Computation and Language |
Multi-task Learning of Pairwise Sequence Classification Tasks Over
Disparate Label Spaces | We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
topic-based sentiment analysis.
| 2,018 | Computation and Language |
High-Dimensional Vector Semantics | In this paper we explore the "vector semantics" problem from the perspective
of "almost orthogonal" property of high-dimensional random vectors. We show
that this intriguing property can be used to "memorize" random vectors by
simply adding them, and we provide an efficient probabilistic solution to the
set membership problem. Also, we discuss several applications to word context
vector embeddings, document sentences similarity, and spam filtering.
| 2,018 | Computation and Language |
The Development of Darwin's Origin of Species | From 1837, when he returned to England aboard the $\textit{HMS Beagle}$, to
1860, just after publication of $\textit{The Origin of Species}$, Charles
Darwin kept detailed notes of each book he read or wanted to read. His notes
and manuscripts provide information about decades of individual scientific
practice. Previously, we trained topic models on the full texts of each
reading, and applied information-theoretic measures to detect that changes in
his reading patterns coincided with the boundaries of his three major
intellectual projects in the period 1837-1860. In this new work we apply the
reading model to five additional documents, four of them by Darwin: the first
edition of $\textit{The Origin of Species}$, two private essays stating
intermediate forms of his theory in 1842 and 1844, a third essay of disputed
dating, and Alfred Russel Wallace's essay, which Darwin received in 1858. We
address three historical inquiries, previously treated qualitatively: 1) the
mythology of "Darwin's Delay," that despite completing an extensive draft in
1844, Darwin waited until 1859 to publish $\textit{The Origin of Species}$ due
to external pressures; 2) the relationship between Darwin and Wallace's
contemporaneous theories, especially in light of their joint presentation; and
3) dating of the "Outline and Draft" which was rediscovered in 1975 and
postulated first as an 1839 draft preceding the Sketch of 1842, then as an
interstitial draft between the 1842 and 1844 essays.
| 2,018 | Computation and Language |
Convolutional Neural Networks for Toxic Comment Classification | Flood of information is produced in a daily basis through the global Internet
usage arising from the on-line interactive communications among users. While
this situation contributes significantly to the quality of human life,
unfortunately it involves enormous dangers, since on-line texts with high
toxicity can cause personal attacks, on-line harassment and bullying behaviors.
This has triggered both industrial and research community in the last few years
while there are several tries to identify an efficient model for on-line toxic
comment prediction. However, these steps are still in their infancy and new
approaches and frameworks are required. On parallel, the data explosion that
appears constantly, makes the construction of new machine learning
computational tools for managing this information, an imperative need.
Thankfully advances in hardware, cloud computing and big data management allow
the development of Deep Learning approaches appearing very promising
performance so far. For text classification in particular the use of
Convolutional Neural Networks (CNN) have recently been proposed approaching
text analytics in a modern manner emphasizing in the structure of words in a
document. In this work, we employ this approach to discover toxic comments in a
large pool of documents provided by a current Kaggle's competition regarding
Wikipedia's talk page edits. To justify this decision we choose to compare CNNs
against the traditional bag-of-words approach for text analysis combined with a
selection of algorithms proven to be very effective in text classification. The
reported results provide enough evidence that CNN enhance toxic comment
classification reinforcing research interest towards this direction.
| 2,019 | Computation and Language |
Classifying Idiomatic and Literal Expressions Using Topic Models and
Intensity of Emotions | We describe an algorithm for automatic classification of idiomatic and
literal expressions. Our starting point is that words in a given text segment,
such as a paragraph, that are highranking representatives of a common topic of
discussion are less likely to be a part of an idiomatic expression. Our
additional hypothesis is that contexts in which idioms occur, typically, are
more affective and therefore, we incorporate a simple analysis of the intensity
of the emotions expressed by the contexts. We investigate the bag of words
topic representation of one to three paragraphs containing an expression that
should be classified as idiomatic or literal (a target phrase). We extract
topics from paragraphs containing idioms and from paragraphs containing
literals using an unsupervised clustering method, Latent Dirichlet Allocation
(LDA) (Blei et al., 2003). Since idiomatic expressions exhibit the property of
non-compositionality, we assume that they usually present different semantics
than the words used in the local topic. We treat idioms as semantic outliers,
and the identification of a semantic shift as outlier detection. Thus, this
topic representation allows us to differentiate idioms from literals using
local semantic contexts. Our results are encouraging.
| 2,018 | Computation and Language |
A Hybrid Word-Character Approach to Abstractive Summarization | Automatic abstractive text summarization is an important and challenging
research topic of natural language processing. Among many widely used
languages, the Chinese language has a special property that a Chinese character
contains rich information comparable to a word. Existing Chinese text
summarization methods, either adopt totally character-based or word-based
representations, fail to fully exploit the information carried by both
representations. To accurately capture the essence of articles, we propose a
hybrid word-character approach (HWC) which preserves the advantages of both
word-based and character-based representations. We evaluate the advantage of
the proposed HWC approach by applying it to two existing methods, and discover
that it generates state-of-the-art performance with a margin of 24 ROUGE points
on a widely used dataset LCSTS. In addition, we find an issue contained in the
LCSTS dataset and offer a script to remove overlapping pairs (a summary and a
short text) to create a clean dataset for the community. The proposed HWC
approach also generates the best performance on the new, clean LCSTS dataset.
| 2,018 | Computation and Language |
Extractive Text Summarization using Neural Networks | Text Summarization has been an extensively studied problem. Traditional
approaches to text summarization rely heavily on feature engineering. In
contrast to this, we propose a fully data-driven approach using feedforward
neural networks for single document summarization. We train and evaluate the
model on standard DUC 2002 dataset which shows results comparable to the state
of the art models. The proposed model is scalable and is able to produce the
summary of arbitrarily sized documents by breaking the original document into
fixed sized parts and then feeding it recursively to the network.
| 2,018 | Computation and Language |
Collective Entity Disambiguation with Structured Gradient Tree Boosting | We present a gradient-tree-boosting-based structured learning model for
jointly disambiguating named entities in a document. Gradient tree boosting is
a widely used machine learning algorithm that underlies many top-performing
natural language processing systems. Surprisingly, most works limit the use of
gradient tree boosting as a tool for regular classification or regression
problems, despite the structured nature of language. To the best of our
knowledge, our work is the first one that employs the structured gradient tree
boosting (SGTB) algorithm for collective entity disambiguation. By defining
global features over previous disambiguation decisions and jointly modeling
them with local features, our system is able to produce globally optimized
entity assignments for mentions in a document. Exact inference is prohibitively
expensive for our globally normalized model. To solve this problem, we propose
Bidirectional Beam Search with Gold path (BiBSG), an approximate inference
algorithm that is a variant of the standard beam search algorithm. BiBSG makes
use of global information from both past and future to perform better local
search. Experiments on standard benchmark datasets show that SGTB significantly
improves upon published results. Specifically, SGTB outperforms the previous
state-of-the-art neural system by near 1\% absolute accuracy on the popular
AIDA-CoNLL dataset.
| 2,018 | Computation and Language |
Medical Exam Question Answering with Large-scale Reading Comprehension | Reading and understanding text is one important component in computer aided
diagnosis in clinical medicine, also being a major research problem in the
field of NLP. In this work, we introduce a question-answering task called MedQA
to study answering questions in clinical medicine using knowledge in a
large-scale document collection. The aim of MedQA is to answer real-world
questions with large-scale reading comprehension. We propose our solution
SeaReader--a modular end-to-end reading comprehension model based on LSTM
networks and dual-path attention architecture. The novel dual-path attention
models information flow from two perspectives and has the ability to
simultaneously read individual documents and integrate information across
multiple documents. In experiments our SeaReader achieved a large increase in
accuracy on MedQA over competing models. Additionally, we develop a series of
novel techniques to demonstrate the interpretation of the question answering
process in SeaReader.
| 2,018 | Computation and Language |
Simultaneously Self-Attending to All Mentions for Full-Abstract
Biological Relation Extraction | Most work in relation extraction forms a prediction by looking at a short
span of text within a single sentence containing a single entity pair mention.
This approach often does not consider interactions across mentions, requires
redundant computation for each mention pair, and ignores relationships
expressed across sentence boundaries. These problems are exacerbated by the
document- (rather than sentence-) level annotation common in biological text.
In response, we propose a model which simultaneously predicts relationships
between all mention pairs in a document. We form pairwise predictions over
entire paper abstracts using an efficient self-attention encoder. All-pairs
mention scores allow us to perform multi-instance learning by aggregating over
mentions to form entity pair representations. We further adapt to settings
without mention-level annotation by jointly training to predict named entities
and adding a corpus of weakly labeled data. In experiments on two Biocreative
benchmark datasets, we achieve state of the art performance on the Biocreative
V Chemical Disease Relation dataset for models without external KB resources.
We also introduce a new dataset an order of magnitude larger than existing
human-annotated biological information extraction datasets and more accurate
than distantly supervised alternatives.
| 2,018 | Computation and Language |
Analyzing Uncertainty in Neural Machine Translation | Machine translation is a popular test bed for research in neural
sequence-to-sequence models but despite much recent research, there is still a
lack of understanding of these models. Practitioners report performance
degradation with large beams, the under-estimation of rare words and a lack of
diversity in the final translations. Our study relates some of these issues to
the inherent uncertainty of the task, due to the existence of multiple valid
translations for a single source sentence, and to the extrinsic uncertainty
caused by noisy training data. We propose tools and metrics to assess how
uncertainty in the data is captured by the model distribution and how it
affects search strategies that generate translations. Our results show that
search works remarkably well but that models tend to spread too much
probability mass over the hypothesis space. Next, we propose tools to assess
model calibration and show how to easily fix some shortcomings of current
models. As part of this study, we release multiple human reference translations
for two popular benchmarks.
| 2,018 | Computation and Language |
Improving Sentiment Analysis in Arabic Using Word Representation | The complexities of Arabic language in morphology, orthography and dialects
makes sentiment analysis for Arabic more challenging. Also, text feature
extraction from short messages like tweets, in order to gauge the sentiment,
makes this task even more difficult. In recent years, deep neural networks were
often employed and showed very good results in sentiment classification and
natural language processing applications. Word embedding, or word distributing
approach, is a current and powerful tool to capture together the closest words
from a contextual text. In this paper, we describe how we construct Word2Vec
models from a large Arabic corpus obtained from ten newspapers in different
Arab countries. By applying different machine learning algorithms and
convolutional neural networks with different text feature selections, we report
improved accuracy of sentiment classification (91%-95%) on our publicly
available Arabic language health sentiment dataset [1]
| 2,018 | Computation and Language |
Matching Natural Language Sentences with Hierarchical Sentence
Factorization | Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning schemes for sentence matching. However, previous approaches
either omit or fail to fully utilize the ordered, hierarchical, and flexible
structures of language objects, as well as the interactions between them. In
this paper, we propose Hierarchical Sentence Factorization---a technique to
factorize a sentence into a hierarchical representation, with the components at
each different scale reordered into a "predicate-argument" form. The proposed
sentence factorization technique leads to the invention of: 1) a new
unsupervised distance metric which calculates the semantic distance between a
pair of text snippets by solving a penalized optimal transport problem while
preserving the logical relationship of words in the reordered sentences, and 2)
new multi-scale deep learning models for supervised semantic training, based on
factorized sentence hierarchies. We apply our techniques to text-pair
similarity estimation and text-pair relationship classification tasks, based on
multiple datasets such as STSbenchmark, the Microsoft Research paraphrase
identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments
show that the proposed hierarchical sentence factorization can be used to
significantly improve the performance of existing unsupervised distance-based
metrics as well as multiple supervised deep learning models based on the
convolutional neural network (CNN) and long short-term memory (LSTM).
| 2,018 | Computation and Language |
XNMT: The eXtensible Neural Machine Translation Toolkit | This paper describes XNMT, the eXtensible Neural Machine Translation toolkit.
XNMT distin- guishes itself from other open-source NMT toolkits by its focus on
modular code design, with the purpose of enabling fast iteration in research
and replicable, reliable results. In this paper we describe the design of XNMT
and its experiment configuration system, and demonstrate its utility on the
tasks of machine translation, speech recognition, and multi-tasked machine
translation/parsing. XNMT is available open-source at
https://github.com/neulab/xnmt
| 2,018 | Computation and Language |
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational
Knowledge for Commonsense Machine Comprehension | This paper describes our system for SemEval-2018 Task 11: Machine
Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks
(TriAN) to model interactions between the passage, question and answers. To
incorporate commonsense knowledge, we augment the input with relation embedding
from the graph of general knowledge ConceptNet (Speer et al., 2017). As a
result, our system achieves state-of-the-art performance with 83.95% accuracy
on the official test data. Code is publicly available at
https://github.com/intfloat/commonsense-rc
| 2,018 | Computation and Language |
A Deep Learning Approach for Multimodal Deception Detection | Automatic deception detection is an important task that has gained momentum
in computational linguistics due to its potential applications. In this paper,
we propose a simple yet tough to beat multi-modal neural model for deception
detection. By combining features from different modalities such as video,
audio, and text along with Micro-Expression features, we show that detecting
deception in real life videos can be more accurate. Experimental results on a
dataset of real-life deception videos show that our model outperforms existing
techniques for deception detection with an accuracy of 96.14% and ROC-AUC of
0.9799.
| 2,018 | Computation and Language |
Joint Training for Neural Machine Translation Models with Monolingual
Data | Monolingual data have been demonstrated to be helpful in improving
translation quality of both statistical machine translation (SMT) systems and
neural machine translation (NMT) systems, especially in resource-poor or domain
adaptation tasks where parallel data are not rich enough. In this paper, we
propose a novel approach to better leveraging monolingual data for neural
machine translation by jointly learning source-to-target and target-to-source
NMT models for a language pair with a joint EM optimization method. The
training process starts with two initial NMT models pre-trained on parallel
data for each direction, and these two models are iteratively updated by
incrementally decreasing translation losses on training data. In each iteration
step, both NMT models are first used to translate monolingual data from one
language to the other, forming pseudo-training data of the other NMT model.
Then two new NMT models are learnt from parallel data together with the pseudo
training data. Both NMT models are expected to be improved and better
pseudo-training data can be generated in next step. Experiment results on
Chinese-English and English-German translation tasks show that our approach can
simultaneously improve translation quality of source-to-target and
target-to-source models, significantly outperforming strong baseline systems
which are enhanced with monolingual data for model training including
back-translation.
| 2,018 | Computation and Language |
Cross-lingual and Multilingual Speech Emotion Recognition on English and
French | Research on multilingual speech emotion recognition faces the problem that
most available speech corpora differ from each other in important ways, such as
annotation methods or interaction scenarios. These inconsistencies complicate
building a multilingual system. We present results for cross-lingual and
multilingual emotion recognition on English and French speech data with similar
characteristics in terms of interaction (human-human conversations). Further,
we explore the possibility of fine-tuning a pre-trained cross-lingual model
with only a small number of samples from the target language, which is of great
interest for low-resource languages. To gain more insights in what is learned
by the deployed convolutional neural network, we perform an analysis on the
attention mechanism inside the network.
| 2,018 | Computation and Language |
A Factoid Question Answering System for Vietnamese | In this paper, we describe the development of an end-to-end factoid question
answering system for the Vietnamese language. This system combines both
statistical models and ontology-based methods in a chain of processing modules
to provide high-quality mappings from natural language text to entities. We
present the challenges in the development of such an intelligent user interface
for an isolating language like Vietnamese and show that techniques developed
for inflectional languages cannot be applied "as is". Our question answering
system can answer a wide range of general knowledge questions with promising
accuracy on a test set.
| 2,018 | Computation and Language |
Age Group Classification with Speech and Metadata Multimodality Fusion | Children comprise a significant proportion of TV viewers and it is worthwhile
to customize the experience for them. However, identifying who is a child in
the audience can be a challenging task. Identifying gender and age from audio
commands is a well-studied problem but is still very challenging to get good
accuracy when the utterances are typically only a couple of seconds long. We
present initial studies of a novel method which combines utterances with user
metadata. In particular, we develop an ensemble of different machine learning
techniques on different subsets of data to improve child detection. Our initial
results show a 9.2\% absolute improvement over the baseline, leading to a
state-of-the-art performance.
| 2,017 | Computation and Language |
Representing Verbs as Argument Concepts | Verbs play an important role in the understanding of natural language text.
This paper studies the problem of abstracting the subject and object arguments
of a verb into a set of noun concepts, known as the "argument concepts". This
set of concepts, whose size is parameterized, represents the fine-grained
semantics of a verb. For example, the object of "enjoy" can be abstracted into
time, hobby and event, etc. We present a novel framework to automatically infer
human readable and machine computable action concepts with high accuracy.
| 2,018 | Computation and Language |
Lexico-acoustic Neural-based Models for Dialog Act Classification | Recent works have proposed neural models for dialog act classification in
spoken dialogs. However, they have not explored the role and the usefulness of
acoustic information. We propose a neural model that processes both lexical and
acoustic features for classification. Our results on two benchmark datasets
reveal that acoustic features are helpful in improving the overall accuracy.
Finally, a deeper analysis shows that acoustic features are valuable in three
cases: when a dialog act has sufficient data, when lexical information is
limited and when strong lexical cues are not present.
| 2,018 | Computation and Language |
DEMorphy, German Language Morphological Analyzer | DEMorphy is a morphological analyzer for German. It is built onto large,
compactified lexicons from German Morphological Dictionary. A guesser based on
German declension suffixed is also provided. For German, we provided a
state-of-art morphological analyzer. DEMorphy is implemented in Python with
ease of usability and accompanying documentation. The package is suitable for
both academic and commercial purposes wit a permissive licence.
| 2,018 | Computation and Language |
Hybrid Model For Word Prediction Using Naive Bayes and Latent
Information | Historically, the Natural Language Processing area has been given too much
attention by many researchers. One of the main motivation beyond this interest
is related to the word prediction problem, which states that given a set words
in a sentence, one can recommend the next word. In literature, this problem is
solved by methods based on syntactic or semantic analysis. Solely, each of
these analysis cannot achieve practical results for end-user applications. For
instance, the Latent Semantic Analysis can handle semantic features of text,
but cannot suggest words considering syntactical rules. On the other hand,
there are models that treat both methods together and achieve state-of-the-art
results, e.g. Deep Learning. These models can demand high computational effort,
which can make the model infeasible for certain types of applications. With the
advance of the technology and mathematical models, it is possible to develop
faster systems with more accuracy. This work proposes a hybrid word suggestion
model, based on Naive Bayes and Latent Semantic Analysis, considering
neighbouring words around unfilled gaps. Results show that this model could
achieve 44.2% of accuracy in the MSR Sentence Completion Challenge.
| 2,018 | Computation and Language |
On Modular Training of Neural Acoustics-to-Word Model for LVCSR | End-to-end (E2E) automatic speech recognition (ASR) systems directly map
acoustics to words using a unified model. Previous works mostly focus on E2E
training a single model which integrates acoustic and language model into a
whole. Although E2E training benefits from sequence modeling and simplified
decoding pipelines, large amount of transcribed acoustic data is usually
required, and traditional acoustic and language modelling techniques cannot be
utilized. In this paper, a novel modular training framework of E2E ASR is
proposed to separately train neural acoustic and language models during
training stage, while still performing end-to-end inference in decoding stage.
Here, an acoustics-to-phoneme model (A2P) and a phoneme-to-word model (P2W) are
trained using acoustic data and text data respectively. A phone synchronous
decoding (PSD) module is inserted between A2P and P2W to reduce sequence
lengths without precision loss. Finally, modules are integrated into an
acousticsto-word model (A2W) and jointly optimized using acoustic data to
retain the advantage of sequence modeling. Experiments on a 300- hour
Switchboard task show significant improvement over the direct A2W model. The
efficiency in both training and decoding also benefits from the proposed
method.
| 2,018 | Computation and Language |
Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse
Relation Classification | Identifying implicit discourse relations between text spans is a challenging
task because it requires understanding the meaning of the text. To tackle this
task, recent studies have tried several deep learning methods but few of them
exploited the syntactic information. In this work, we explore the idea of
incorporating syntactic parse tree into neural networks. Specifically, we
employ the Tree-LSTM model and Tree-GRU model, which are based on the tree
structure, to encode the arguments in a relation. Moreover, we further leverage
the constituent tags to control the semantic composition process in these
tree-structured neural networks. Experimental results show that our method
achieves state-of-the-art performance on PDTB corpus.
| 2,018 | Computation and Language |
Understanding and Improving Multi-Sense Word Embeddings via Extended
Robust Principal Component Analysis | Unsupervised learned representations of polysemous words generate a large of
pseudo multi senses since unsupervised methods are overly sensitive to
contextual variations. In this paper, we address the pseudo multi-sense
detection for word embeddings by dimensionality reduction of sense pairs. We
propose a novel principal analysis method, termed Ex-RPCA, designed to detect
both pseudo multi senses and real multi senses. With Ex-RPCA, we empirically
show that pseudo multi senses are generated systematically in unsupervised
method. Moreover, the multi-sense word embeddings can by improved by a simple
linear transformation based on Ex-RPCA. Our improved word embedding outperform
the original one by 5.6 points on Stanford contextual word similarity (SCWS)
dataset. We hope our simple yet effective approach will help the linguistic
analysis of multi-sense word embeddings in the future.
| 2,018 | Computation and Language |
CAESAR: Context Awareness Enabled Summary-Attentive Reader | Comprehending meaning from natural language is a primary objective of Natural
Language Processing (NLP), and text comprehension is the cornerstone for
achieving this objective upon which all other problems like chat bots, language
translation and others can be achieved. We report a Summary-Attentive Reader we
designed to better emulate the human reading process, along with a
dictiontary-based solution regarding out-of-vocabulary (OOV) words in the data,
to generate answer based on machine comprehension of reading passages and
question from the SQuAD benchmark. Our implementation of these features with
two popular models (Match LSTM and Dynamic Coattention) was able to reach close
to matching the results obtained from humans.
| 2,018 | Computation and Language |
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual
Sentence Representations | Average word embeddings are a common baseline for more sophisticated sentence
embedding techniques. However, they typically fall short of the performances of
more complex models such as InferSent. Here, we generalize the concept of
average word embeddings to power mean word embeddings. We show that the
concatenation of different types of power mean word embeddings considerably
closes the gap to state-of-the-art methods monolingually and substantially
outperforms these more complex techniques cross-lingually. In addition, our
proposed method outperforms different recently proposed baselines such as SIF
and Sent2Vec by a solid margin, thus constituting a much harder-to-beat
monolingual baseline. Our data and code are publicly available.
| 2,018 | Computation and Language |
Query and Output: Generating Words by Querying Distributed Word
Representations for Paraphrase Generation | Most recent approaches use the sequence-to-sequence model for paraphrase
generation. The existing sequence-to-sequence model tends to memorize the words
and the patterns in the training dataset instead of learning the meaning of the
words. Therefore, the generated sentences are often grammatically correct but
semantically improper. In this work, we introduce a novel model based on the
encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our
proposed model generates the words by querying distributed word representations
(i.e. neural word embeddings), hoping to capturing the meaning of the according
words. Following previous work, we evaluate our model on two
paraphrase-oriented tasks, namely text simplification and short text
abstractive summarization. Experimental results show that our model outperforms
the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two
English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a
Chinese summarization dataset. Moreover, our model achieves state-of-the-art
performances on these three benchmark datasets.
| 2,018 | Computation and Language |
Automatic Translating between Ancient Chinese and Contemporary Chinese
with Limited Aligned Corpora | The Chinese language has evolved a lot during the long-term development.
Therefore, native speakers now have trouble in reading sentences written in
ancient Chinese. In this paper, we propose to build an end-to-end neural model
to automatically translate between ancient and contemporary Chinese. However,
the existing ancient-contemporary Chinese parallel corpora are not aligned at
the sentence level and sentence-aligned corpora are limited, which makes it
difficult to train the model. To build the sentence level parallel training
data for the model, we propose an unsupervised algorithm that constructs
sentence-aligned ancient-contemporary pairs by using the fact that the aligned
sentence pair shares many of the tokens. Based on the aligned corpus, we
propose an end-to-end neural model with copying mechanism and local attention
to translate between ancient and contemporary Chinese. Experiments show that
the proposed unsupervised algorithm achieves 99.4% F1 score for sentence
alignment, and the translation model achieves 26.95 BLEU from ancient to
contemporary, and 36.34 BLEU from contemporary to ancient.
| 2,022 | Computation and Language |
Calculated attributes of synonym sets | The goal of formalization, proposed in this paper, is to bring together, as
near as possible, the theoretic linguistic problem of synonym conception and
the computer linguistic methods based generally on empirical intuitive
unjustified factors. Using the word vector representation we have proposed the
geometric approach to mathematical modeling of synonym set (synset). The word
embedding is based on the neural networks (Skip-gram, CBOW), developed and
realized as word2vec program by T. Mikolov. The standard cosine similarity is
used as the distance between word-vectors. Several geometric characteristics of
the synset words are introduced: the interior of synset, the synset word rank
and centrality. These notions are intended to select the most significant
synset words, i.e. the words which senses are the nearest to the sense of a
synset. Some experiments with proposed notions, based on RusVectores resources,
are represented. A brief description of this work can be viewed in slides
https://goo.gl/K82Fei
| 2,018 | Computation and Language |
Neural Architectures for Open-Type Relation Argument Extraction | In this work, we introduce the task of Open-Type Relation Argument Extraction
(ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q
authored notable work with title X"), the model has to extract an argument of
non-standard entity type (entities that cannot be extracted by a standard named
entity tagger, e.g. X: the title of a book or a work of art) from the corpus. A
distantly supervised dataset based on WikiData relations is obtained and
released to address the task.
We develop and compare a wide range of neural models for this task yielding
large improvements over a strong baseline obtained with a neural question
answering system. The impact of different sentence encoding architectures and
answer extraction methods is systematically compared. An encoder based on gated
recurrent units combined with a conditional random fields tagger gives the best
results.
| 2,019 | Computation and Language |
Explain Yourself: A Natural Language Interface for Scrutable Autonomous
Robots | Autonomous systems in remote locations have a high degree of autonomy and
there is a need to explain what they are doing and why in order to increase
transparency and maintain trust. Here, we describe a natural language chat
interface that enables vehicle behaviour to be queried by the user. We obtain
an interpretable model of autonomy through having an expert 'speak out-loud'
and provide explanations during a mission. This approach is agnostic to the
type of autonomy model and as expert and operator are from the same user-group,
we predict that these explanations will align well with the operator's mental
model, increase transparency and assist with operator training.
| 2,018 | Computation and Language |
Self-Attention with Relative Position Representations | Relying entirely on an attention mechanism, the Transformer introduced by
Vaswani et al. (2017) achieves state-of-the-art results for machine
translation. In contrast to recurrent and convolutional neural networks, it
does not explicitly model relative or absolute position information in its
structure. Instead, it requires adding representations of absolute positions to
its inputs. In this work we present an alternative approach, extending the
self-attention mechanism to efficiently consider representations of the
relative positions, or distances between sequence elements. On the WMT 2014
English-to-German and English-to-French translation tasks, this approach yields
improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations,
respectively. Notably, we observe that combining relative and absolute position
representations yields no further improvement in translation quality. We
describe an efficient implementation of our method and cast it as an instance
of relation-aware self-attention mechanisms that can generalize to arbitrary
graph-labeled inputs.
| 2,018 | Computation and Language |
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction | Clinical notes often describe important aspects of a patient's stay and are
therefore critical to medical research. Clinical concept extraction (CCE) of
named entities - such as problems, tests, and treatments - aids in forming an
understanding of notes and provides a foundation for many downstream clinical
decision-making tasks. Historically, this task has been posed as a standard
named entity recognition (NER) sequence tagging problem, and solved with
feature-based methods using handengineered domain knowledge. Recent advances,
however, have demonstrated the efficacy of LSTM-based models for NER tasks,
including CCE. This work presents CliNER 2.0, a simple-to-install, open-source
tool for extracting concepts from clinical text. CliNER 2.0 uses a word- and
character- level LSTM model, and achieves state-of-the-art performance. For
ease of use, the tool also includes pre-trained models available for public
use.
| 2,018 | Computation and Language |
An End-to-End Goal-Oriented Dialog System with a Generative Natural
Language Response Generation | Recently advancements in deep learning allowed the development of end-to-end
trained goal-oriented dialog systems. Although these systems already achieve
good performance, some simplifications limit their usage in real-life
scenarios.
In this work, we address two of these limitations: ignoring positional
information and a fixed number of possible response candidates. We propose to
use positional encodings in the input to model the word order of the user
utterances. Furthermore, by using a feedforward neural network, we are able to
generate the output word by word and are no longer restricted to a fixed number
of possible response candidates. Using the positional encoding, we were able to
achieve better accuracies in the Dialog bAbI Tasks and using the feedforward
neural network for generating the response, we were able to save computation
time and space consumption.
| 2,018 | Computation and Language |
Annotation Artifacts in Natural Language Inference Data | Large-scale datasets for natural language inference are created by presenting
crowd workers with a sentence (premise), and asking them to generate three new
sentences (hypotheses) that it entails, contradicts, or is logically neutral
with respect to. We show that, in a significant portion of such data, this
protocol leaves clues that make it possible to identify the label by looking
only at the hypothesis, without observing the premise. Specifically, we show
that a simple text categorization model can correctly classify the hypothesis
alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams
et. al, 2017). Our analysis reveals that specific linguistic phenomena such as
negation and vagueness are highly correlated with certain inference classes.
Our findings suggest that the success of natural language inference models to
date has been overestimated, and that the task remains a hard open problem.
| 2,018 | Computation and Language |
Multimodal Emoji Prediction | Emojis are small images that are commonly included in social media text
messages. The combination of visual and textual content in the same message
builds up a modern way of communication, that automatic systems are not used to
deal with. In this paper we extend recent advances in emoji prediction by
putting forward a multimodal approach that is able to predict emojis in
Instagram posts. Instagram posts are composed of pictures together with texts
which sometimes include emojis. We show that these emojis can be predicted by
using the text, but also using the picture. Our main finding is that
incorporating the two synergistic modalities, in a combined model, improves
accuracy in an emoji prediction task. This result demonstrates that these two
modalities (text and images) encode different information on the use of emojis
and therefore can complement each other.
| 2,018 | Computation and Language |
Natural Language to Structured Query Generation via Meta-Learning | In conventional supervised training, a model is trained to fit all the
training examples. However, having a monolithic model may not always be the
best strategy, as examples could vary widely. In this work, we explore a
different learning protocol that treats each example as a unique pseudo-task,
by reducing the original learning problem to a few-shot meta-learning scenario
with the help of a domain-dependent relevance function. When evaluated on the
WikiSQL dataset, our approach leads to faster convergence and achieves
1.1%-5.4% absolute accuracy gains over the non-meta-learning counterparts.
| 2,018 | Computation and Language |
Extracting Domain Invariant Features by Unsupervised Learning for Robust
Automatic Speech Recognition | The performance of automatic speech recognition (ASR) systems can be
significantly compromised by previously unseen conditions, which is typically
due to a mismatch between training and testing distributions. In this paper, we
address robustness by studying domain invariant features, such that domain
information becomes transparent to ASR systems, resolving the mismatch problem.
Specifically, we investigate a recent model, called the Factorized Hierarchical
Variational Autoencoder (FHVAE). FHVAEs learn to factorize sequence-level and
segment-level attributes into different latent variables without supervision.
We argue that the set of latent variables that contain segment-level
information is our desired domain invariant feature for ASR. Experiments are
conducted on Aurora-4 and CHiME-4, which demonstrate 41% and 27% absolute word
error rate reductions respectively on mismatched domains.
| 2,018 | Computation and Language |
Generating Contradictory, Neutral, and Entailing Sentences | Learning distributed sentence representations remains an interesting problem
in the field of Natural Language Processing (NLP). We want to learn a model
that approximates the conditional latent space over the representations of a
logical antecedent of the given statement. In our paper, we propose an approach
to generating sentences, conditioned on an input sentence and a logical
inference label. We do this by modeling the different possibilities for the
output sentence as a distribution over the latent representation, which we
train using an adversarial objective. We evaluate the model using two
state-of-the-art models for the Recognizing Textual Entailment (RTE) task, and
measure the BLEU scores against the actual sentences as a probe for the
diversity of sentences produced by our model. The experiment results show that,
given our framework, we have clear ways to improve the quality and diversity of
generated sentences.
| 2,018 | Computation and Language |
Towards the Creation of a Large Corpus of Synthetically-Identified
Clinical Notes | Clinical notes often describe the most important aspects of a patient's
physiology and are therefore critical to medical research. However, these notes
are typically inaccessible to researchers without prior removal of sensitive
protected health information (PHI), a natural language processing (NLP) task
referred to as deidentification. Tools to automatically de-identify clinical
notes are needed but are difficult to create without access to those very same
notes containing PHI. This work presents a first step toward creating a large
synthetically-identified corpus of clinical notes and corresponding PHI
annotations in order to facilitate the development de-identification tools.
Further, one such tool is evaluated against this corpus in order to understand
the advantages and shortcomings of this approach.
| 2,018 | Computation and Language |
The emergent algebraic structure of RNNs and embeddings in NLP | We examine the algebraic and geometric properties of a uni-directional GRU
and word embeddings trained end-to-end on a text classification task. A
hyperparameter search over word embedding dimension, GRU hidden dimension, and
a linear combination of the GRU outputs is performed. We conclude that words
naturally embed themselves in a Lie group and that RNNs form a nonlinear
representation of the group. Appealing to these results, we propose a novel
class of recurrent-like neural networks and a word embedding scheme.
| 2,018 | Computation and Language |
An efficient framework for learning sentence representations | In this work we propose a simple and efficient framework for learning
sentence representations from unlabelled data. Drawing inspiration from the
distributional hypothesis and recent work on learning sentence representations,
we reformulate the problem of predicting the context in which a sentence
appears as a classification problem. Given a sentence and its context, a
classifier distinguishes context sentences from other contrastive sentences
based on their vector representations. This allows us to efficiently learn
different types of encoding functions, and we show that the model learns
high-quality sentence representations. We demonstrate that our sentence
representations outperform state-of-the-art unsupervised and supervised
representation learning methods on several downstream NLP tasks that involve
understanding sentence semantics while achieving an order of magnitude speedup
in training time.
| 2,018 | Computation and Language |
Translating Questions into Answers using DBPedia n-triples | In this paper we present a question answering system using a neural network
to interpret questions learned from the DBpedia repository. We train a
sequence-to-sequence neural network model with n-triples extracted from the
DBpedia Infobox Properties. Since these properties do not represent the natural
language, we further used question-answer dialogues from movie subtitles.
Although the automatic evaluation shows a low overlap of the generated answers
compared to the gold standard set, a manual inspection of the showed promising
outcomes from the experiment for further work.
| 2,018 | Computation and Language |
How Images Inspire Poems: Generating Classical Chinese Poetry from
Images with Memory Networks | With the recent advances of neural models and natural language processing,
automatic generation of classical Chinese poetry has drawn significant
attention due to its artistic and cultural value. Previous works mainly focus
on generating poetry given keywords or other text information, while visual
inspirations for poetry have been rarely explored. Generating poetry from
images is much more challenging than generating poetry from text, since images
contain very rich visual information which cannot be described completely using
several keywords, and a good poem should convey the image accurately. In this
paper, we propose a memory based neural model which exploits images to generate
poems. Specifically, an Encoder-Decoder model with a topic memory network is
proposed to generate classical Chinese poetry from images. To the best of our
knowledge, this is the first work attempting to generate classical Chinese
poetry from images with neural networks. A comprehensive experimental
investigation with both human evaluation and quantitative analysis demonstrates
that the proposed model can generate poems which convey images accurately.
| 2,018 | Computation and Language |
Fact Checking in Community Forums | Community Question Answering (cQA) forums are very popular nowadays, as they
represent effective means for communities around particular topics to share
information. Unfortunately, this information is not always factual. Thus, here
we explore a new dimension in the context of cQA, which has been ignored so
far: checking the veracity of answers to particular questions in cQA forums. As
this is a new problem, we create a specialized dataset for it. We further
propose a novel multi-faceted model, which captures information from the answer
content (what is said and how), from the author profile (who says it), from the
rest of the community forum (where it is said), and from external authoritative
sources of information (external support). Evaluation results show a MAP value
of 86.54, which is 21 points absolute above the baseline.
| 2,018 | Computation and Language |
Feudal Reinforcement Learning for Dialogue Management in Large Domains | Reinforcement learning (RL) is a promising approach to solve dialogue policy
optimisation. Traditional RL algorithms, however, fail to scale to large
domains due to the curse of dimensionality. We propose a novel Dialogue
Management architecture, based on Feudal RL, which decomposes the decision into
two steps; a first step where a master policy selects a subset of primitive
actions, and a second step where a primitive action is chosen from the selected
subset. The structural information included in the domain ontology is used to
abstract the dialogue state space, taking the decisions at each step using
different parts of the abstracted state. This, combined with an information
sharing mechanism between slots, increases the scalability to large domains. We
show that an implementation of this approach, based on Deep-Q Networks,
significantly outperforms previous state of the art in several dialogue domains
and environments, without the need of any additional reward signal.
| 2,018 | Computation and Language |
Learning Approximate Inference Networks for Structured Prediction | Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use
neural network architectures to define energy functions that can capture
arbitrary dependencies among parts of structured outputs. Prior work used
gradient descent for inference, relaxing the structured output to a set of
continuous variables and then optimizing the energy with respect to them. We
replace this use of gradient descent with a neural network trained to
approximate structured argmax inference. This "inference network" outputs
continuous values that we treat as the output structure. We develop
large-margin training criteria for joint training of the structured energy
function and inference network. On multi-label classification we report
speed-ups of 10-60x compared to (Belanger et al, 2017) while also improving
accuracy. For sequence labeling with simple structured energies, our approach
performs comparably to exact inference while being much faster at test time. We
then demonstrate improved accuracy by augmenting the energy with a "label
language model" that scores entire output label sequences, showing it can
improve handling of long-distance dependencies in part-of-speech tagging.
Finally, we show how inference networks can replace dynamic programming for
test-time inference in conditional random fields, suggestive for their general
use for fast inference in structured settings.
| 2,018 | Computation and Language |
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss | The task of Fine-grained Entity Type Classification (FETC) consists of
assigning types from a hierarchy to entity mentions in text. Existing methods
rely on distant supervision and are thus susceptible to noisy labels that can
be out-of-context or overly-specific for the training sentence. Previous
methods that attempt to address these issues do so with heuristics or with the
help of hand-crafted features. Instead, we propose an end-to-end solution with
a neural network model that uses a variant of cross- entropy loss function to
handle out-of-context labels, and hierarchical loss normalization to cope with
overly-specific ones. Also, previous work solve FETC a multi-label
classification followed by ad-hoc post-processing. In contrast, our solution is
more elegant: we use public word embeddings to train a single-label that
jointly learns representations for entity mentions and their context. We show
experimentally that our approach is robust against noise and consistently
outperforms the state-of-the-art on established benchmarks for the task.
| 2,018 | Computation and Language |
An Unsupervised Model with Attention Autoencoders for Question Retrieval | Question retrieval is a crucial subtask for community question answering.
Previous research focus on supervised models which depend heavily on training
data and manual feature engineering. In this paper, we propose a novel
unsupervised framework, namely reduced attentive matching network (RAMN), to
compute semantic matching between two questions. Our RAMN integrates together
the deep semantic representations, the shallow lexical mismatching information
and the initial rank produced by an external search engine. For the first time,
we propose attention autoencoders to generate semantic representations of
questions. In addition, we employ lexical mismatching to capture surface
matching between two questions, which is derived from the importance of each
word in a question. We conduct experiments on the open CQA datasets of
SemEval-2016 and SemEval-2017. The experimental results show that our
unsupervised model obtains comparable performance with the state-of-the-art
supervised methods in SemEval-2016 Task 3, and outperforms the best system in
SemEval-2017 Task 3 by a wide margin.
| 2,018 | Computation and Language |
The Importance of Being Recurrent for Modeling Hierarchical Structure | Recent work has shown that recurrent neural networks (RNNs) can implicitly
capture and exploit hierarchical information when trained to solve common
natural language processing tasks such as language modeling (Linzen et al.,
2016) and neural machine translation (Shi et al., 2016). In contrast, the
ability to model structured data with non-recurrent neural networks has
received little attention despite their success in many NLP tasks (Gehring et
al., 2017; Vaswani et al., 2017). In this work, we compare the two
architectures---recurrent versus non-recurrent---with respect to their ability
to model hierarchical structure and find that recurrency is indeed important
for this purpose.
| 2,018 | Computation and Language |
Hate Speech Detection: A Solved Problem? The Challenging Case of Long
Tail on Twitter | In recent years, the increasing propagation of hate speech on social media
and the urgent need for effective counter-measures have drawn significant
investment from governments, companies, and researchers. A large number of
methods have been developed for automated hate speech detection online. This
aims to classify textual content into non-hate or hate speech, in which case
the method may also identify the targeting characteristics (i.e., types of
hate, such as race, and religion) in the hate speech. However, we notice
significant difference between the performance of the two (i.e., non-hate v.s.
hate). In this work, we argue for a focus on the latter problem for practical
reasons. We show that it is a much more challenging task, as our analysis of
the language in the typical datasets shows that hate speech lacks unique,
discriminative features and therefore is found in the 'long tail' in a dataset
that is difficult to discover. We then propose Deep Neural Network structures
serving as feature extractors that are particularly effective for capturing the
semantics of hate speech. Our methods are evaluated on the largest collection
of hate speech datasets based on Twitter, and are shown to be able to
outperform the best performing method by up to 5 percentage points in
macro-average F1, or 8 percentage points in the more challenging case of
identifying hateful content.
| 2,018 | Computation and Language |
Automating Reading Comprehension by Generating Question and Answer Pairs | Neural network-based methods represent the state-of-the-art in question
generation from text. Existing work focuses on generating only questions from
text without concerning itself with answer generation. Moreover, our analysis
shows that handling rare words and generating the most appropriate question
given a candidate answer are still challenges facing existing approaches. We
present a novel two-stage process to generate question-answer pairs from the
text. For the first stage, we present alternatives for encoding the span of the
pivotal answer in the sentence using Pointer Networks. In our second stage, we
employ sequence to sequence models for question generation, enhanced with rich
linguistic features. Finally, global attention and answer encoding are used for
generating the question most relevant to the answer. We motivate and
linguistically analyze the role of each component in our framework and consider
compositions of these. This analysis is supported by extensive experimental
evaluations. Using standard evaluation metrics as well as human evaluations,
our experimental results validate the significant improvement in the quality of
questions generated by our framework over the state-of-the-art. The technique
presented here represents another step towards more automated reading
comprehension assessment. We also present a live system \footnote{Demo of the
system is available at
\url{https://www.cse.iitb.ac.in/~vishwajeet/autoqg.html}.} to demonstrate the
effectiveness of our approach.
| 2,018 | Computation and Language |
Syntax-Aware Language Modeling with Recurrent Neural Networks | Neural language models (LMs) are typically trained using only lexical
features, such as surface forms of words. In this paper, we argue this deprives
the LM of crucial syntactic signals that can be detected at high confidence
using existing parsers. We present a simple but highly effective approach for
training neural LMs using both lexical and syntactic information, and a novel
approach for applying such LMs to unparsed text using sequential Monte Carlo
sampling. In experiments on a range of corpora and corpus sizes, we show our
approach consistently outperforms standard lexical LMs in character-level
language modeling; on the other hand, for word-level models the models are on a
par with standard language models. These results indicate potential for
expanding LMs beyond lexical surface features to higher-level NLP features for
character-level models.
| 2,018 | Computation and Language |
Co-occurrence of the Benford-like and Zipf Laws Arising from the Texts
Representing Human and Artificial Languages | We demonstrate that large texts, representing human (English, Russian,
Ukrainian) and artificial (C++, Java) languages, display quantitative patterns
characterized by the Benford-like and Zipf laws. The frequency of a word
following the Zipf law is inversely proportional to its rank, whereas the total
numbers of a certain word appearing in the text generate the uneven
Benford-like distribution of leading numbers. Excluding the most popular words
essentially improves the correlation of actual textual data with the Zipfian
distribution, whereas the Benford distribution of leading numbers (arising from
the overall amount of a certain word) is insensitive to the same elimination
procedure. The calculated values of the moduli of slopes of double
logarithmical plots for artificial languages (C++, Java) are markedly larger
than those for human ones.
| 2,018 | Computation and Language |
IcoRating: A Deep-Learning System for Scam ICO Identification | Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP,
NEO) have been rapidly gaining ground in use, value, and understanding among
the public, bringing astonishing profits to investors. Unlike other money and
banking systems, most digital tokens do not require central authorities. Being
decentralized poses significant challenges for credit rating. Most ICOs are
currently not subject to government regulations, which makes a reliable credit
rating system for ICO projects necessary and urgent.
In this paper, we introduce IcoRating, the first learning--based
cryptocurrency rating system. We exploit natural-language processing techniques
to analyze various aspects of 2,251 digital currencies to date, such as white
paper content, founding teams, Github repositories, websites, etc. Supervised
learning models are used to correlate the life span and the price change of
cryptocurrencies with these features. For the best setting, the proposed system
is able to identify scam ICO projects with 0.83 precision.
We hope this work will help investors identify scam ICOs and attract more
efforts in automatically evaluating and analyzing ICO projects.
| 2,018 | Computation and Language |
We Built a Fake News & Click-bait Filter: What Happened Next Will Blow
Your Mind! | It is completely amazing! Fake news and click-baits have totally invaded the
cyber space. Let us face it: everybody hates them for three simple reasons.
Reason #2 will absolutely amaze you. What these can achieve at the time of
election will completely blow your mind! Now, we all agree, this cannot go on,
you know, somebody has to stop it. So, we did this research on fake
news/click-bait detection and trust us, it is totally great research, it really
is! Make no mistake. This is the best research ever! Seriously, come have a
look, we have it all: neural networks, attention mechanism, sentiment lexicons,
author profiling, you name it. Lexical features, semantic features, we
absolutely have it all. And we have totally tested it, trust us! We have
results, and numbers, really big numbers. The best numbers ever! Oh, and
analysis, absolutely top notch analysis. Interested? Come read the shocking
truth about fake news and click-bait in the Bulgarian cyber space. You won't
believe what we have found!
| 2,018 | Computation and Language |
Face2Text: Collecting an Annotated Image Description Corpus for the
Generation of Rich Face Descriptions | The past few years have witnessed renewed interest in NLP tasks at the
interface between vision and language. One intensively-studied problem is that
of automatically generating text from images. In this paper, we extend this
problem to the more specific domain of face description. Unlike scene
descriptions, face descriptions are more fine-grained and rely on attributes
extracted from the image, rather than objects and relations. Given that no data
exists for this task, we present an ongoing crowdsourcing study to collect a
corpus of descriptions of face images taken `in the wild'. To gain a better
understanding of the variation we find in face description and the possible
issues that this may raise, we also conducted an annotation study on a subset
of the corpus. Primarily, we found descriptions to refer to a mixture of
attributes, not only physical, but also emotional and inferential, which is
bound to create further challenges for current image-to-text methods.
| 2,021 | Computation and Language |
Language Identification of Bengali-English Code-Mixed data using
Character & Phonetic based LSTM Models | Language identification of social media text still remains a challenging task
due to properties like code-mixing and inconsistent phonetic transliterations.
In this paper, we present a supervised learning approach for language
identification at the word level of low resource Bengali-English code-mixed
data taken from social media. We employ two methods of word encoding, namely
character based and root phone based to train our deep LSTM models. Utilizing
these two models we created two ensemble models using stacking and threshold
technique which gave 91.78% and 92.35% accuracies respectively on our testing
data.
| 2,018 | Computation and Language |
Path of Vowel Raising in Chengdu Dialect of Mandarin | He and Rao (2013) reported a raising phenomenon of /a/ in /Xan/ (X being a
consonant or a vowel) in Chengdu dialect of Mandarin, i.e. /a/ is realized as
[epsilon] for young speakers but [ae] for older speakers, but they offered no
acoustic analysis. We designed an acoustic study that examined the realization
of /Xan/ in speakers of different age (old vs. young) and gender (male vs.
female) groups, where X represents three conditions: 1) unaspirated consonants:
C ([p], [t], [k]), 2) aspirated consonants: Ch ([ph], [th], [kh]), and 3) high
vowels: V ([i], [y], [u]). 17 native speakers were asked to read /Xan/
characters and the F1 values are extracted for comparison. Our results
confirmed the raising effect in He and Rao (2013), i.e., young speakers realize
/a/ as [epsilon] in /an/, whereas older speakers in the most part realize it as
[ae]. Also, female speakers raise more than male speakers within the same age
group. Interestingly, within the /Van/ condition, older speakers do raise /a/
in /ian/ and /yan/. We interpret this as /a/ first assimilates to its preceding
front high vowels /i/ and /y/ for older speakers, which then becomes
phonologized in younger speakers in all conditions, including /Chan/ and /Can/.
This shows a possible trajectory of the ongoing sound change in the Chengdu
dialect.
| 2,018 | Computation and Language |
Generating Bilingual Pragmatic Color References | Contextual influences on language often exhibit substantial cross-lingual
regularities; for example, we are more verbose in situations that require finer
distinctions. However, these regularities are sometimes obscured by semantic
and syntactic differences. Using a newly-collected dataset of color reference
games in Mandarin Chinese (which we release to the public), we confirm that a
variety of constructions display the same sensitivity to contextual difficulty
in Chinese and English. We then show that a neural speaker agent trained on
bilingual data with a simple multitask learning approach displays more
human-like patterns of context dependence and is more pragmatically informative
than its monolingual Chinese counterpart. Moreover, this is not at the expense
of language-specific semantic understanding: the resulting speaker model learns
the different basic color term systems of English and Chinese (with noteworthy
cross-lingual influences), and it can identify synonyms between the two
languages using vector analogy operations on its output layer, despite having
no exposure to parallel data.
| 2,018 | Computation and Language |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.