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Deep Cascade Multi-task Learning for Slot Filling in Online Shopping
Assistant | Slot filling is a critical task in natural language understanding (NLU) for
dialog systems. State-of-the-art approaches treat it as a sequence labeling
problem and adopt such models as BiLSTM-CRF. While these models work relatively
well on standard benchmark datasets, they face challenges in the context of
E-commerce where the slot labels are more informative and carry richer
expressions. In this work, inspired by the unique structure of E-commerce
knowledge base, we propose a novel multi-task model with cascade and residual
connections, which jointly learns segment tagging, named entity tagging and
slot filling. Experiments show the effectiveness of the proposed cascade and
residual structures. Our model has a 14.6% advantage in F1 score over the
strong baseline methods on a new Chinese E-commerce shopping assistant dataset,
while achieving competitive accuracies on a standard dataset. Furthermore,
online test deployed on such dominant E-commerce platform shows 130%
improvement on accuracy of understanding user utterances. Our model has already
gone into production in the E-commerce platform.
| 2,019 | Computation and Language |
Automatic Generation of Chinese Short Product Titles for Mobile Display | This paper studies the problem of automatically extracting a short title from
a manually written longer description of E-commerce products for display on
mobile devices. It is a new extractive summarization problem on short text
inputs, for which we propose a feature-enriched network model, combining three
different categories of features in parallel. Experimental results show that
our framework significantly outperforms several baselines by a substantial gain
of 4.5%. Moreover, we produce an extractive summarization dataset for
E-commerce short texts and will release it to the research community.
| 2,019 | Computation and Language |
Fine-Grained Attention Mechanism for Neural Machine Translation | Neural machine translation (NMT) has been a new paradigm in machine
translation, and the attention mechanism has become the dominant approach with
the state-of-the-art records in many language pairs. While there are variants
of the attention mechanism, all of them use only temporal attention where one
scalar value is assigned to one context vector corresponding to a source word.
In this paper, we propose a fine-grained (or 2D) attention mechanism where each
dimension of a context vector will receive a separate attention score. In
experiments with the task of En-De and En-Fi translation, the fine-grained
attention method improves the translation quality in terms of BLEU score. In
addition, our alignment analysis reveals how the fine-grained attention
mechanism exploits the internal structure of context vectors.
| 2,018 | Computation and Language |
Automatically augmenting an emotion dataset improves classification
using audio | In this work, we tackle a problem of speech emotion classification. One of
the issues in the area of affective computation is that the amount of annotated
data is very limited. On the other hand, the number of ways that the same
emotion can be expressed verbally is enormous due to variability between
speakers. This is one of the factors that limits performance and
generalization. We propose a simple method that extracts audio samples from
movies using textual sentiment analysis. As a result, it is possible to
automatically construct a larger dataset of audio samples with positive,
negative emotional and neutral speech. We show that pretraining recurrent
neural network on such a dataset yields better results on the challenging
EmotiW corpus. This experiment shows a potential benefit of combining textual
sentiment analysis with vocal information.
| 2,018 | Computation and Language |
Reusing Neural Speech Representations for Auditory Emotion Recognition | Acoustic emotion recognition aims to categorize the affective state of the
speaker and is still a difficult task for machine learning models. The
difficulties come from the scarcity of training data, general subjectivity in
emotion perception resulting in low annotator agreement, and the uncertainty
about which features are the most relevant and robust ones for classification.
In this paper, we will tackle the latter problem. Inspired by the recent
success of transfer learning methods we propose a set of architectures which
utilize neural representations inferred by training on large speech databases
for the acoustic emotion recognition task. Our experiments on the IEMOCAP
dataset show ~10% relative improvements in the accuracy and F1-score over the
baseline recurrent neural network which is trained end-to-end for emotion
recognition.
| 2,018 | Computation and Language |
GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural
Network Models for Tweet Emotion Intensity Detection | The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity
values of tweet messages. Given the text of a tweet and its emotion category
(anger, joy, fear, and sadness), the participants were asked to build a system
that assigns emotion intensity values. Emotion intensity estimation is a
challenging problem given the short length of the tweets, the noisy structure
of the text and the lack of annotated data. To solve this problem, we developed
an ensemble of two neural models, processing input on the character. and
word-level with a lexicon-driven system. The correlation scores across all four
emotions are averaged to determine the bottom-line competition metric, and our
system ranks place forth in full intensity range and third in 0.5-1 range of
intensity among 23 systems at the time of writing (June 2017).
| 2,020 | Computation and Language |
ESPnet: End-to-End Speech Processing Toolkit | This paper introduces a new open source platform for end-to-end speech
processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech
recognition (ASR), and adopts widely-used dynamic neural network toolkits,
Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the
Kaldi ASR toolkit style for data processing, feature extraction/format, and
recipes to provide a complete setup for speech recognition and other speech
processing experiments. This paper explains a major architecture of this
software platform, several important functionalities, which differentiate
ESPnet from other open source ASR toolkits, and experimental results with major
ASR benchmarks.
| 2,018 | Computation and Language |
Attentive Interaction Model: Modeling Changes in View in Argumentation | We present a neural architecture for modeling argumentative dialogue that
explicitly models the interplay between an Opinion Holder's (OH's) reasoning
and a challenger's argument, with the goal of predicting if the argument
successfully changes the OH's view. The model has two components: (1)
vulnerable region detection, an attention model that identifies parts of the
OH's reasoning that are amenable to change, and (2) interaction encoding, which
identifies the relationship between the content of the OH's reasoning and that
of the challenger's argument. Based on evaluation on discussions from the
Change My View forum on Reddit, the two components work together to predict an
OH's change in view, outperforming several baselines. A posthoc analysis
suggests that sentences picked out by the attention model are addressed more
frequently by successful arguments than by unsuccessful ones.
| 2,018 | Computation and Language |
Learning General Purpose Distributed Sentence Representations via Large
Scale Multi-task Learning | A lot of the recent success in natural language processing (NLP) has been
driven by distributed vector representations of words trained on large amounts
of text in an unsupervised manner. These representations are typically used as
general purpose features for words across a range of NLP problems. However,
extending this success to learning representations of sequences of words, such
as sentences, remains an open problem. Recent work has explored unsupervised as
well as supervised learning techniques with different training objectives to
learn general purpose fixed-length sentence representations. In this work, we
present a simple, effective multi-task learning framework for sentence
representations that combines the inductive biases of diverse training
objectives in a single model. We train this model on several data sources with
multiple training objectives on over 100 million sentences. Extensive
experiments demonstrate that sharing a single recurrent sentence encoder across
weakly related tasks leads to consistent improvements over previous methods. We
present substantial improvements in the context of transfer learning and
low-resource settings using our learned general-purpose representations.
| 2,018 | Computation and Language |
Towards Learning Transferable Conversational Skills using
Multi-dimensional Dialogue Modelling | Recent statistical approaches have improved the robustness and scalability of
spoken dialogue systems. However, despite recent progress in domain adaptation,
their reliance on in-domain data still limits their cross-domain scalability.
In this paper, we argue that this problem can be addressed by extending current
models to reflect and exploit the multi-dimensional nature of human dialogue.
We present our multi-dimensional, statistical dialogue management framework, in
which transferable conversational skills can be learnt by separating out
domain-independent dimensions of communication and using multi-agent
reinforcement learning. Our initial experiments with a simulated user show that
we can speed up the learning process by transferring learnt policies.
| 2,018 | Computation and Language |
Training Tips for the Transformer Model | This article describes our experiments in neural machine translation using
the recent Tensor2Tensor framework and the Transformer sequence-to-sequence
model (Vaswani et al., 2017). We examine some of the critical parameters that
affect the final translation quality, memory usage, training stability and
training time, concluding each experiment with a set of recommendations for
fellow researchers. In addition to confirming the general mantra "more data and
larger models", we address scaling to multiple GPUs and provide practical tips
for improved training regarding batch size, learning rate, warmup steps,
maximum sentence length and checkpoint averaging. We hope that our observations
will allow others to get better results given their particular hardware and
data constraints.
| 2,018 | Computation and Language |
Revisiting Skip-Gram Negative Sampling Model with Rectification | We revisit skip-gram negative sampling (SGNS), one of the most popular
neural-network based approaches to learning distributed word representation. We
first point out the ambiguity issue undermining the SGNS model, in the sense
that the word vectors can be entirely distorted without changing the objective
value. To resolve the issue, we investigate the intrinsic structures in
solution that a good word embedding model should deliver. Motivated by this, we
rectify the SGNS model with quadratic regularization, and show that this simple
modification suffices to structure the solution in the desired manner. A
theoretical justification is presented, which provides novel insights into
quadratic regularization . Preliminary experiments are also conducted on
Google's analytical reasoning task to support the modified SGNS model.
| 2,019 | Computation and Language |
Completely Unsupervised Phoneme Recognition by Adversarially Learning
Mapping Relationships from Audio Embeddings | Unsupervised discovery of acoustic tokens from audio corpora without
annotation and learning vector representations for these tokens have been
widely studied. Although these techniques have been shown successful in some
applications such as query-by-example Spoken Term Detection (STD), the lack of
mapping relationships between these discovered tokens and real phonemes have
limited the down-stream applications. This paper represents probably the first
attempt towards the goal of completely unsupervised phoneme recognition, or
mapping audio signals to phoneme sequences without phoneme-labeled audio data.
The basic idea is to cluster the embedded acoustic tokens and learn the mapping
between the cluster sequences and the unknown phoneme sequences with a
Generative Adversarial Network (GAN). An unsupervised phoneme recognition
accuracy of 36% was achieved in the preliminary experiments.
| 2,018 | Computation and Language |
Joint Learning of Interactive Spoken Content Retrieval and Trainable
User Simulator | User-machine interaction is crucial for information retrieval, especially for
spoken content retrieval, because spoken content is difficult to browse, and
speech recognition has a high degree of uncertainty. In interactive retrieval,
the machine takes different actions to interact with the user to obtain better
retrieval results; here it is critical to select the most efficient action. In
previous work, deep Q-learning techniques were proposed to train an interactive
retrieval system but rely on a hand-crafted user simulator; building a reliable
user simulator is difficult. In this paper, we further improve the interactive
spoken content retrieval framework by proposing a learnable user simulator
which is jointly trained with interactive retrieval system, making the
hand-crafted user simulator unnecessary. The experimental results show that the
learned simulated users not only achieve larger rewards than the hand-crafted
ones but act more like real users.
| 2,018 | Computation and Language |
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition
Errors on Listening Comprehension | Reading comprehension has been widely studied. One of the most representative
reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on
which machine is already comparable with human. On the other hand, accessing
large collections of multimedia or spoken content is much more difficult and
time-consuming than plain text content for humans. It's therefore highly
attractive to develop machines which can automatically understand spoken
content. In this paper, we propose a new listening comprehension task - Spoken
SQuAD. On the new task, we found that speech recognition errors have
catastrophic impact on machine comprehension, and several approaches are
proposed to mitigate the impact.
| 2,018 | Computation and Language |
Marian: Fast Neural Machine Translation in C++ | We present Marian, an efficient and self-contained Neural Machine Translation
framework with an integrated automatic differentiation engine based on dynamic
computation graphs. Marian is written entirely in C++. We describe the design
of the encoder-decoder framework and demonstrate that a research-friendly
toolkit can achieve high training and translation speed.
| 2,018 | Computation and Language |
Real Time Sentiment Change Detection of Twitter Data Streams | In the past few years, there has been a huge growth in Twitter sentiment
analysis having already provided a fair amount of research on sentiment
detection of public opinion among Twitter users. Given the fact that Twitter
messages are generated constantly with dizzying rates, a huge volume of
streaming data is created, thus there is an imperative need for accurate
methods for knowledge discovery and mining of this information. Although there
exists a plethora of twitter sentiment analysis methods in the recent
literature, the researchers have shifted to real-time sentiment identification
on twitter streaming data, as expected. A major challenge is to deal with the
Big Data challenges arising in Twitter streaming applications concerning both
Volume and Velocity. Under this perspective, in this paper, a methodological
approach based on open source tools is provided for real-time detection of
changes in sentiment that is ultra efficient with respect to both memory
consumption and computational cost. This is achieved by iteratively collecting
tweets in real time and discarding them immediately after their process. For
this purpose, we employ the Lexicon approach for sentiment characterizations,
while change detection is achieved through appropriate control charts that do
not require historical information. We believe that the proposed methodology
provides the trigger for a potential large-scale monitoring of threads in an
attempt to discover fake news spread or propaganda efforts in their early
stages. Our experimental real-time analysis based on a recent hashtag provides
evidence that the proposed approach can detect meaningful sentiment changes
across a hashtags lifetime.
| 2,019 | Computation and Language |
NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network
Model for Irony Detection in Twitter | This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony
detection in English tweets". We propose to use a simple neural network
architecture of Multilayer Perceptron with various types of input features
including: lexical, syntactic, semantic and polarity features. Our system
achieves very high performance in both subtasks of binary and multi-class irony
detection in tweets. In particular, we rank third using the accuracy metric and
fifth using the F1 metric. Our code is available at
https://github.com/NIHRIO/IronyDetectionInTwitter
| 2,018 | Computation and Language |
A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech
Domain Adaptation | Domain adaptation plays an important role for speech recognition models, in
particular, for domains that have low resources. We propose a novel generative
model based on cyclic-consistent generative adversarial network (CycleGAN) for
unsupervised non-parallel speech domain adaptation. The proposed model employs
multiple independent discriminators on the power spectrogram, each in charge of
different frequency bands. As a result we have 1) better discriminators that
focus on fine-grained details of the frequency features, and 2) a generator
that is capable of generating more realistic domain-adapted spectrogram. We
demonstrate the effectiveness of our method on speech recognition with gender
adaptation, where the model only has access to supervised data from one gender
during training, but is evaluated on the other at test time. Our model is able
to achieve an average of $7.41\%$ on phoneme error rate, and $11.10\%$ word
error rate relative performance improvement as compared to the baseline, on
TIMIT and WSJ dataset, respectively. Qualitatively, our model also generates
more natural sounding speech, when conditioned on data from the other domain.
| 2,018 | Computation and Language |
Investigating Capsule Networks with Dynamic Routing for Text
Classification | In this study, we explore capsule networks with dynamic routing for text
classification. We propose three strategies to stabilize the dynamic routing
process to alleviate the disturbance of some noise capsules which may contain
"background" information or have not been successfully trained. A series of
experiments are conducted with capsule networks on six text classification
benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets,
which shows the effectiveness of capsule networks for text classification. We
additionally show that capsule networks exhibit significant improvement when
transfer single-label to multi-label text classification over strong baseline
methods. To the best of our knowledge, this is the first work that capsule
networks have been empirically investigated for text modeling.
| 2,018 | Computation and Language |
A Systematic Review of Automated Grammar Checking in English Language | Grammar checking is the task of detection and correction of grammatical
errors in the text. English is the dominating language in the field of science
and technology. Therefore, the non-native English speakers must be able to use
correct English grammar while reading, writing or speaking. This generates the
need of automatic grammar checking tools. So far many approaches have been
proposed and implemented. But less efforts have been made in surveying the
literature in the past decade. The objective of this systematic review is to
examine the existing literature, highlighting the current issues and suggesting
the potential directions of future research. This systematic review is a result
of analysis of 12 primary studies obtained after designing a search strategy
for selecting papers found on the web. We also present a possible scheme for
the classification of grammar errors. Among the main observations, we found
that there is a lack of efficient and robust grammar checking tools for real
time applications. We present several useful illustrations- most prominent are
the schematic diagrams that we provide for each approach and a table that
summarizes these approaches along different dimensions such as target error
types, linguistic dataset used, strengths and limitations of the approach. This
facilitates better understandability, comparison and evaluation of previous
research.
| 2,018 | Computation and Language |
The Training of Neuromodels for Machine Comprehension of Text.
Brain2Text Algorithm | Nowadays, the Internet represents a vast informational space, growing
exponentially and the problem of search for relevant data becomes essential as
never before. The algorithm proposed in the article allows to perform natural
language queries on content of the document and get comprehensive meaningful
answers. The problem is partially solved for English as SQuAD contains enough
data to learn on, but there is no such dataset in Russian, so the methods used
by scientists now are not applicable to Russian. Brain2 framework allows to
cope with the problem - it stands out for its ability to be applied on small
datasets and does not require impressive computing power. The algorithm is
illustrated on Sberbank of Russia Strategy's text and assumes the use of a
neuromodel consisting of 65 mln synapses. The trained model is able to
construct word-by-word answers to questions based on a given text. The existing
limitations are its current inability to identify synonyms, pronoun relations
and allegories. Nevertheless, the results of conducted experiments showed high
capacity and generalisation ability of the suggested approach.
| 2,018 | Computation and Language |
Modeling Semantic Plausibility by Injecting World Knowledge | Distributional data tells us that a man can swallow candy, but not that a man
can swallow a paintball, since this is never attested. However both are
physically plausible events. This paper introduces the task of semantic
plausibility: recognizing plausible but possibly novel events. We present a new
crowdsourced dataset of semantic plausibility judgments of single events such
as "man swallow paintball". Simple models based on distributional
representations perform poorly on this task, despite doing well on selection
preference, but injecting manually elicited knowledge about entity properties
provides a substantial performance boost. Our error analysis shows that our new
dataset is a great testbed for semantic plausibility models: more sophisticated
knowledge representation and propagation could address many of the remaining
errors.
| 2,018 | Computation and Language |
Simple and Effective Semi-Supervised Question Answering | Recent success of deep learning models for the task of extractive Question
Answering (QA) is hinged on the availability of large annotated corpora.
However, large domain specific annotated corpora are limited and expensive to
construct. In this work, we envision a system where the end user specifies a
set of base documents and only a few labelled examples. Our system exploits the
document structure to create cloze-style questions from these base documents;
pre-trains a powerful neural network on the cloze style questions; and further
fine-tunes the model on the labeled examples. We evaluate our proposed system
across three diverse datasets from different domains, and find it to be highly
effective with very little labeled data. We attain more than 50% F1 score on
SQuAD and TriviaQA with less than a thousand labelled examples. We are also
releasing a set of 3.2M cloze-style questions for practitioners to use while
building QA systems.
| 2,018 | Computation and Language |
Automatic Normalization of Word Variations in Code-Mixed Social Media
Text | Social media platforms such as Twitter and Facebook are becoming popular in
multilingual societies. This trend induces portmanteau of South Asian languages
with English. The blend of multiple languages as code-mixed data has recently
become popular in research communities for various NLP tasks. Code-mixed data
consist of anomalies such as grammatical errors and spelling variations. In
this paper, we leverage the contextual property of words where the different
spelling variation of words share similar context in a large noisy social media
text. We capture different variations of words belonging to same context in an
unsupervised manner using distributed representations of words. Our experiments
reveal that preprocessing of the code-mixed dataset based on our approach
improves the performance in state-of-the-art part-of-speech tagging
(POS-tagging) and sentiment analysis tasks.
| 2,024 | Computation and Language |
Emotions are Universal: Learning Sentiment Based Representations of
Resource-Poor Languages using Siamese Networks | Machine learning approaches in sentiment analysis principally rely on the
abundance of resources. To limit this dependence, we propose a novel method
called Siamese Network Architecture for Sentiment Analysis (SNASA) to learn
representations of resource-poor languages by jointly training them with
resource-rich languages using a siamese network.
SNASA model consists of twin Bi-directional Long Short-Term Memory Recurrent
Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive
loss function, based on a similarity metric. The model learns the sentence
representations of resource-poor and resource-rich language in a common
sentiment space by using a similarity metric based on their individual
sentiments. The model, hence, projects sentences with similar sentiment closer
to each other and the sentences with different sentiment farther from each
other. Experiments on large-scale datasets of resource-rich languages - English
and Spanish and resource-poor languages - Hindi and Telugu reveal that SNASA
outperforms the state-of-the-art sentiment analysis approaches based on
distributional semantics, semantic rules, lexicon lists and deep neural network
representations without sh
| 2,024 | Computation and Language |
Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich
Languages | Code-mixed data is an important challenge of natural language processing
because its characteristics completely vary from the traditional structures of
standard languages.
In this paper, we propose a novel approach called Sentiment Analysis of
Code-Mixed Text (SACMT) to classify sentences into their corresponding
sentiment - positive, negative or neutral, using contrastive learning. We
utilize the shared parameters of siamese networks to map the sentences of
code-mixed and standard languages to a common sentiment space. Also, we
introduce a basic clustering based preprocessing method to capture variations
of code-mixed transliterated words. Our experiments reveal that SACMT
outperforms the state-of-the-art approaches in sentiment analysis for
code-mixed text by 7.6% in accuracy and 10.1% in F-score.
| 2,024 | Computation and Language |
Incorporating Word Embeddings into Open Directory Project based
Large-scale Classification | Recently, implicit representation models, such as embedding or deep learning,
have been successfully adopted to text classification task due to their
outstanding performance. However, these approaches are limited to small- or
moderate-scale text classification. Explicit representation models are often
used in a large-scale text classification, like the Open Directory Project
(ODP)-based text classification. However, the performance of these models is
limited to the associated knowledge bases. In this paper, we incorporate word
embeddings into the ODP-based large-scale classification. To this end, we first
generate category vectors, which represent the semantics of ODP categories by
jointly modeling word embeddings and the ODP-based text classification. We then
propose a novel semantic similarity measure, which utilizes the category and
word vectors obtained from the joint model and word embeddings, respectively.
The evaluation results clearly show the efficacy of our methodology in
large-scale text classification. The proposed scheme exhibits significant
improvements of 10% and 28% in terms of macro-averaging F1-score and precision
at k, respectively, over state-of-the-art techniques.
| 2,018 | Computation and Language |
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural
Networks for Multi-label Emotion Classification | In this paper, we propose an attention-based classifier that predicts
multiple emotions of a given sentence. Our model imitates human's two-step
procedure of sentence understanding and it can effectively represent and
classify sentences. With emoji-to-meaning preprocessing and extra lexicon
utilization, we further improve the model performance. We train and evaluate
our model with data provided by SemEval-2018 task 1-5, each sentence of which
has several labels among 11 given sentiments. Our model achieves 5-th/1-th rank
in English/Spanish respectively.
| 2,018 | Computation and Language |
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of
Yor\`ub\'a Language Text | Yor\`ub\'a is a widely spoken West African language with a writing system
rich in tonal and orthographic diacritics. With very few exceptions, diacritics
are omitted from electronic texts, due to limited device and application
support. Diacritics provide morphological information, are crucial for lexical
disambiguation, pronunciation and are vital for any Yor\`ub\'a text-to-speech
(TTS), automatic speech recognition (ASR) and natural language processing (NLP)
tasks. Reframing Automatic Diacritic Restoration (ADR) as a machine translation
task, we experiment with two different attentive Sequence-to-Sequence neural
models to process undiacritized text. On our evaluation dataset, this approach
produces diacritization error rates of less than 5%. We have released
pre-trained models, datasets and source-code as an open-source project to
advance efforts on Yor\`ub\'a language technology.
| 2,018 | Computation and Language |
Bi-Directional Block Self-Attention for Fast and Memory-Efficient
Sequence Modeling | Recurrent neural networks (RNN), convolutional neural networks (CNN) and
self-attention networks (SAN) are commonly used to produce context-aware
representations. RNN can capture long-range dependency but is hard to
parallelize and not time-efficient. CNN focuses on local dependency but does
not perform well on some tasks. SAN can model both such dependencies via highly
parallelizable computation, but memory requirement grows rapidly in line with
sequence length. In this paper, we propose a model, called "bi-directional
block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding.
It requires as little memory as RNN but with all the merits of SAN. Bi-BloSAN
splits the entire sequence into blocks, and applies an intra-block SAN to each
block for modeling local context, then applies an inter-block SAN to the
outputs for all blocks to capture long-range dependency. Thus, each SAN only
needs to process a short sequence, and only a small amount of memory is
required. Additionally, we use feature-level attention to handle the variation
of contexts around the same word, and use forward/backward masks to encode
temporal order information. On nine benchmark datasets for different NLP tasks,
Bi-BloSAN achieves or improves upon state-of-the-art accuracy, and shows better
efficiency-memory trade-off than existing RNN/CNN/SAN.
| 2,018 | Computation and Language |
In-depth Question classification using Convolutional Neural Networks | Convolutional neural networks for computer vision are fairly intuitive. In a
typical CNN used in image classification, the first layers learn edges, and the
following layers learn some filters that can identify an object. But CNNs for
Natural Language Processing are not used often and are not completely
intuitive. We have a good idea about what the convolution filters learn for the
task of text classification, and to that, we propose a neural network structure
that will be able to give good results in less time. We will be using
convolutional neural networks to predict the primary or broader topic of a
question, and then use separate networks for each of these predicted topics to
accurately classify their sub-topics.
| 2,018 | Computation and Language |
360{\deg} Stance Detection | The proliferation of fake news and filter bubbles makes it increasingly
difficult to form an unbiased, balanced opinion towards a topic. To ameliorate
this, we propose 360{\deg} Stance Detection, a tool that aggregates news with
multiple perspectives on a topic. It presents them on a spectrum ranging from
support to opposition, enabling the user to base their opinion on multiple
pieces of diverse evidence.
| 2,018 | Computation and Language |
A Language for Function Signature Representations | Recent work by (Richardson and Kuhn, 2017a,b; Richardson et al., 2018) looks
at semantic parser induction and question answering in the domain of source
code libraries and APIs. In this brief note, we formalize the representations
being learned in these studies and introduce a simple domain specific language
and a systematic translation from this language to first-order logic. By
recasting the target representations in terms of classical logic, we aim to
broaden the applicability of existing code datasets for investigating more
complex natural language understanding and reasoning problems in the software
domain.
| 2,018 | Computation and Language |
CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble
of Deep and Shallow Learning to predict the Quality of Product Titles | We present an approach where two different models (Deep and Shallow) are
trained separately on the data and a weighted average of the outputs is taken
as the final result. For the Deep approach, we use different combinations of
models like Convolution Neural Network, pretrained word2vec embeddings and
LSTMs to get representations which are then used to train a Deep Neural
Network. For Clarity prediction, we also use an Attentive Pooling approach for
the pooling operation so as to be aware of the Title-Category pair. For the
shallow approach, we use boosting technique LightGBM on features generated
using title and categories. We find that an ensemble of these approaches does a
better job than using them alone suggesting that the results of the deep and
shallow approach are highly complementary
| 2,018 | Computation and Language |
Multi-lingual neural title generation for e-Commerce browse pages | To provide better access of the inventory to buyers and better search engine
optimization, e-Commerce websites are automatically generating millions of
easily searchable browse pages. A browse page consists of a set of slot
name/value pairs within a given category, grouping multiple items which share
some characteristics. These browse pages require a title describing the content
of the page. Since the number of browse pages are huge, manual creation of
these titles is infeasible. Previous statistical and neural approaches depend
heavily on the availability of large amounts of data in a language. In this
research, we apply sequence-to-sequence models to generate titles for high- &
low-resourced languages by leveraging transfer learning. We train these models
on multi-lingual data, thereby creating one joint model which can generate
titles in various different languages. Performance of the title generation
system is evaluated on three different languages; English, German, and French,
with a particular focus on low-resourced French language.
| 2,018 | Computation and Language |
Socioeconomic Dependencies of Linguistic Patterns in Twitter: A
Multivariate Analysis | Our usage of language is not solely reliant on cognition but is arguably
determined by myriad external factors leading to a global variability of
linguistic patterns. This issue, which lies at the core of sociolinguistics and
is backed by many small-scale studies on face-to-face communication, is
addressed here by constructing a dataset combining the largest French Twitter
corpus to date with detailed socioeconomic maps obtained from national census
in France. We show how key linguistic variables measured in individual Twitter
streams depend on factors like socioeconomic status, location, time, and the
social network of individuals. We found that (i) people of higher socioeconomic
status, active to a greater degree during the daytime, use a more standard
language; (ii) the southern part of the country is more prone to use more
standard language than the northern one, while locally the used variety or
dialect is determined by the spatial distribution of socioeconomic status; and
(iii) individuals connected in the social network are closer linguistically
than disconnected ones, even after the effects of status homophily have been
removed. Our results inform sociolinguistic theory and may inspire novel
learning methods for the inference of socioeconomic status of people from the
way they tweet.
| 2,018 | Computation and Language |
Clinical Concept Embeddings Learned from Massive Sources of Multimodal
Medical Data | Word embeddings are a popular approach to unsupervised learning of word
relationships that are widely used in natural language processing. In this
article, we present a new set of embeddings for medical concepts learned using
an extremely large collection of multimodal medical data. Leaning on recent
theoretical insights, we demonstrate how an insurance claims database of 60
million members, a collection of 20 million clinical notes, and 1.7 million
full text biomedical journal articles can be combined to embed concepts into a
common space, resulting in the largest ever set of embeddings for 108,477
medical concepts. To evaluate our approach, we present a new benchmark
methodology based on statistical power specifically designed to test embeddings
of medical concepts. Our approach, called cui2vec, attains state-of-the-art
performance relative to previous methods in most instances. Finally, we provide
a downloadable set of pre-trained embeddings for other researchers to use, as
well as an online tool for interactive exploration of the cui2vec embeddings
| 2,019 | Computation and Language |
Domain Adaptation for Statistical Machine Translation | Statistical machine translation (SMT) systems perform poorly when it is
applied to new target domains. Our goal is to explore domain adaptation
approaches and techniques for improving the translation quality of
domain-specific SMT systems. However, translating texts from a specific domain
(e.g., medicine) is full of challenges. The first challenge is ambiguity. Words
or phrases contain different meanings in different contexts. The second one is
language style due to the fact that texts from different genres are always
presented in different syntax, length and structural organization. The third
one is the out-of-vocabulary words (OOVs) problem. In-domain training data are
often scarce with low terminology coverage. In this thesis, we explore the
state-of-the-art domain adaptation approaches and propose effective solutions
to address those problems.
| 2,018 | Computation and Language |
Chinese-Portuguese Machine Translation: A Study on Building Parallel
Corpora from Comparable Texts | Although there are increasing and significant ties between China and
Portuguese-speaking countries, there is not much parallel corpora in the
Chinese-Portuguese language pair. Both languages are very populous, with 1.2
billion native Chinese speakers and 279 million native Portuguese speakers, the
language pair, however, could be considered as low-resource in terms of
available parallel corpora. In this paper, we describe our methods to curate
Chinese-Portuguese parallel corpora and evaluate their quality. We extracted
bilingual data from Macao government websites and proposed a hierarchical
strategy to build a large parallel corpus. Experiments are conducted on
existing and our corpora using both Phrased-Based Machine Translation (PBMT)
and the state-of-the-art Neural Machine Translation (NMT) models. The results
of this work can be used as a benchmark for future Chinese-Portuguese MT
systems. The approach we used in this paper also shows a good example on how to
boost performance of MT systems for low-resource language pairs.
| 2,018 | Computation and Language |
Not just about size - A Study on the Role of Distributed Word
Representations in the Analysis of Scientific Publications | The emergence of knowledge graphs in the scholarly communication domain and
recent advances in artificial intelligence and natural language processing
bring us closer to a scenario where intelligent systems can assist scientists
over a range of knowledge-intensive tasks. In this paper we present
experimental results about the generation of word embeddings from scholarly
publications for the intelligent processing of scientific texts extracted from
SciGraph. We compare the performance of domain-specific embeddings with
existing pre-trained vectors generated from very large and general purpose
corpora. Our results suggest that there is a trade-off between corpus
specificity and volume. Embeddings from domain-specific scientific corpora
effectively capture the semantics of the domain. On the other hand, obtaining
comparable results through general corpora can also be achieved, but only in
the presence of very large corpora of well formed text. Furthermore, We also
show that the degree of overlapping between knowledge areas is directly related
to the performance of embeddings in domain evaluation tasks.
| 2,018 | Computation and Language |
Word Segmentation as Graph Partition | We propose a new approach to the Chinese word segmentation problem that
considers the sentence as an undirected graph, whose nodes are the characters.
One can use various techniques to compute the edge weights that measure the
connection strength between characters. Spectral graph partition algorithms are
used to group the characters and achieve word segmentation. We follow the graph
partition approach and design several unsupervised algorithms, and we show
their inspiring segmentation results on two corpora: (1) electronic health
records in Chinese, and (2) benchmark data from the Second International
Chinese Word Segmentation Bakeoff.
| 2,018 | Computation and Language |
Contrastive Learning of Emoji-based Representations for Resource-Poor
Languages | The introduction of emojis (or emoticons) in social media platforms has given
the users an increased potential for expression. We propose a novel method
called Classification of Emojis using Siamese Network Architecture (CESNA) to
learn emoji-based representations of resource-poor languages by jointly
training them with resource-rich languages using a siamese network.
CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent
Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive
loss function based on a similarity metric. The model learns the
representations of resource-poor and resource-rich language in a common emoji
space by using a similarity metric based on the emojis present in sentences
from both languages. The model, hence, projects sentences with similar emojis
closer to each other and the sentences with different emojis farther from one
another. Experiments on large-scale Twitter datasets of resource-rich languages
- English and Spanish and resource-poor languages - Hindi and Telugu reveal
that CESNA outperforms the state-of-the-art emoji prediction approaches based
on distributional semantics, semantic rules, lexicon lists and deep neural
network representations without shared parameters.
| 2,024 | Computation and Language |
Automated Classification of Text Sentiment | The ability to identify sentiment in text, referred to as sentiment analysis,
is one which is natural to adult humans. This task is, however, not one which a
computer can perform by default. Identifying sentiments in an automated,
algorithmic manner will be a useful capability for business and research in
their search to understand what consumers think about their products or
services and to understand human sociology. Here we propose two new Genetic
Algorithms (GAs) for the task of automated text sentiment analysis. The GAs
learn whether words occurring in a text corpus are either sentiment or
amplifier words, and their corresponding magnitude. Sentiment words, such as
'horrible', add linearly to the final sentiment. Amplifier words in contrast,
which are typically adjectives/adverbs like 'very', multiply the sentiment of
the following word. This increases, decreases or negates the sentiment of the
following word. The sentiment of the full text is then the sum of these terms.
This approach grows both a sentiment and amplifier dictionary which can be
reused for other purposes and fed into other machine learning algorithms. We
report the results of multiple experiments conducted on large Amazon data sets.
The results reveal that our proposed approach was able to outperform several
public and/or commercial sentiment analysis algorithms.
| 2,018 | Computation and Language |
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and
Convolutional Neural Networks for Relation Classification and Extraction | Reliably detecting relevant relations between entities in unstructured text
is a valuable resource for knowledge extraction, which is why it has awaken
significant interest in the field of Natural Language Processing. In this
paper, we present a system for relation classification and extraction based on
an ensemble of convolutional and recurrent neural networks that ranked first in
3 out of the 4 subtasks at SemEval 2018 Task 7. We provide detailed
explanations and grounds for the design choices behind the most relevant
features and analyze their importance.
| 2,018 | Computation and Language |
Few-Shot Text Classification with Pre-Trained Word Embeddings and a
Human in the Loop | Most of the literature around text classification treats it as a supervised
learning problem: given a corpus of labeled documents, train a classifier such
that it can accurately predict the classes of unseen documents. In industry,
however, it is not uncommon for a business to have entire corpora of documents
where few or none have been classified, or where existing classifications have
become meaningless. With web content, for example, poor taxonomy management can
result in labels being applied indiscriminately, making filtering by these
labels unhelpful. Our work aims to make it possible to classify an entire
corpus of unlabeled documents using a human-in-the-loop approach, where the
content owner manually classifies just one or two documents per category and
the rest can be automatically classified. This "few-shot" learning approach
requires rich representations of the documents such that those that have been
manually labeled can be treated as prototypes, and automatic classification of
the rest is a simple case of measuring the distance to prototypes. This
approach uses pre-trained word embeddings, where documents are represented
using a simple weighted average of constituent word embeddings. We have tested
the accuracy of the approach on existing labeled datasets and provide the
results here. We have also made code available for reproducing the results we
got on the 20 Newsgroups dataset.
| 2,018 | Computation and Language |
Expressive Speech Synthesis via Modeling Expressions with Variational
Autoencoder | Recent advances in neural autoregressive models have improve the performance
of speech synthesis (SS). However, as they lack the ability to model global
characteristics of speech (such as speaker individualities or speaking styles),
particularly when these characteristics have not been labeled, making neural
autoregressive SS systems more expressive is still an open issue. In this
paper, we propose to combine VoiceLoop, an autoregressive SS model, with
Variational Autoencoder (VAE). This approach, unlike traditional autoregressive
SS systems, uses VAE to model the global characteristics explicitly, enabling
the expressiveness of the synthesized speech to be controlled in an
unsupervised manner. Experiments using the VCTK and Blizzard2012 datasets show
the VAE helps VoiceLoop to generate higher quality speech and to control the
expressions in its synthesized speech by incorporating global characteristics
into the speech generating process.
| 2,019 | Computation and Language |
Enrichment of OntoSenseNet: Adding a Sense-annotated Telugu lexicon | The paper describes the enrichment of OntoSenseNet - a verb-centric lexical
resource for Indian Languages. This resource contains a newly developed
Telugu-Telugu dictionary. It is important because native speakers can better
annotate the senses when both the word and its meaning are in Telugu. Hence
efforts are made to develop a soft copy of Telugu dictionary. Our resource also
has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs
and 1673 adjectives. Annotations are done by native speakers according to
defined annotation guidelines. In this paper, we provide an overview of the
annotation procedure and present the validation of our resource through
inter-annotator agreement. Concepts of sense-class and sense-type are
discussed. Additionally, we discuss the potential of lexical sense-annotated
corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is
crowd-sourced for annotation of individual words in synsets and is compared
with the developed sense-annotated lexicon (OntoSenseNet) to examine the
improvement. Also, we present a special categorization (spatio-temporal
classification) of adjectives.
| 2,018 | Computation and Language |
Sequence Training of DNN Acoustic Models With Natural Gradient | Deep Neural Network (DNN) acoustic models often use discriminative sequence
training that optimises an objective function that better approximates the word
error rate (WER) than frame-based training. Sequence training is normally
implemented using Stochastic Gradient Descent (SGD) or Hessian Free (HF)
training. This paper proposes an alternative batch style optimisation framework
that employs a Natural Gradient (NG) approach to traverse through the parameter
space. By correcting the gradient according to the local curvature of the
KL-divergence, the NG optimisation process converges more quickly than HF.
Furthermore, the proposed NG approach can be applied to any sequence
discriminative training criterion. The efficacy of the NG method is shown using
experiments on a Multi-Genre Broadcast (MGB) transcription task that
demonstrates both the computational efficiency and the accuracy of the
resulting DNN models.
| 2,018 | Computation and Language |
Chart Parsing Multimodal Grammars | The short note describes the chart parser for multimodal type-logical
grammars which has been developed in conjunction with the type-logical treebank
for French. The chart parser presents an incomplete but fast implementation of
proof search for multimodal type-logical grammars using the "deductive parsing"
framework. Proofs found can be transformed to natural deduction proofs.
| 2,018 | Computation and Language |
Neural models of factuality | We present two neural models for event factuality prediction, which yield
significant performance gains over previous models on three event factuality
datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion
of the It Happened portion of the Universal Decompositional Semantics dataset,
yielding the largest event factuality dataset to date. We report model results
on this extended factuality dataset as well.
| 2,018 | Computation and Language |
Scalable Sentiment for Sequence-to-sequence Chatbot Response with
Performance Analysis | Conventional seq2seq chatbot models only try to find the sentences with the
highest probabilities conditioned on the input sequences, without considering
the sentiment of the output sentences. Some research works trying to modify the
sentiment of the output sequences were reported. In this paper, we propose five
models to scale or adjust the sentiment of the chatbot response: persona-based
model, reinforcement learning, plug and play model, sentiment transformation
network and cycleGAN, all based on the conventional seq2seq model. We also
develop two evaluation metrics to estimate if the responses are reasonable
given the input. These metrics together with other two popularly used metrics
were used to analyze the performance of the five proposed models on different
aspects, and reinforcement learning and cycleGAN were shown to be very
attractive. The evaluation metrics were also found to be well correlated with
human evaluation.
| 2,018 | Computation and Language |
Evaluating historical text normalization systems: How well do they
generalize? | We highlight several issues in the evaluation of historical text
normalization systems that make it hard to tell how well these systems would
actually work in practice---i.e., for new datasets or languages; in comparison
to more na\"ive systems; or as a preprocessing step for downstream NLP tools.
We illustrate these issues and exemplify our proposed evaluation practices by
comparing two neural models against a na\"ive baseline system. We show that the
neural models generalize well to unseen words in tests on five languages;
nevertheless, they provide no clear benefit over the na\"ive baseline for
downstream POS tagging of an English historical collection. We conclude that
future work should include more rigorous evaluation, including both intrinsic
and extrinsic measures where possible.
| 2,018 | Computation and Language |
Guiding Neural Machine Translation with Retrieved Translation Pieces | One of the difficulties of neural machine translation (NMT) is the recall and
appropriate translation of low-frequency words or phrases. In this paper, we
propose a simple, fast, and effective method for recalling previously seen
translation examples and incorporating them into the NMT decoding process.
Specifically, for an input sentence, we use a search engine to retrieve
sentence pairs whose source sides are similar with the input sentence, and then
collect $n$-grams that are both in the retrieved target sentences and aligned
with words that match in the source sentences, which we call "translation
pieces". We compute pseudo-probabilities for each retrieved sentence based on
similarities between the input sentence and the retrieved source sentences, and
use these to weight the retrieved translation pieces. Finally, an existing NMT
model is used to translate the input sentence, with an additional bonus given
to outputs that contain the collected translation pieces. We show our method
improves NMT translation results up to 6 BLEU points on three narrow domain
translation tasks where repetitiveness of the target sentences is particularly
salient. It also causes little increase in the translation time, and compares
favorably to another alternative retrieval-based method with respect to
accuracy, speed, and simplicity of implementation.
| 2,018 | Computation and Language |
Simple Models for Word Formation in English Slang | We propose generative models for three types of extra-grammatical word
formation phenomena abounding in English slang: Blends, Clippings, and
Reduplicatives. Adopting a data-driven approach coupled with linguistic
knowledge, we propose simple models with state of the art performance on human
annotated gold standard datasets. Overall, our models reveal insights into the
generative processes of word formation in slang -- insights which are
increasingly relevant in the context of the rising prevalence of slang and
non-standard varieties on the Internet.
| 2,018 | Computation and Language |
Vision as an Interlingua: Learning Multilingual Semantic Embeddings of
Untranscribed Speech | In this paper, we explore the learning of neural network embeddings for
natural images and speech waveforms describing the content of those images.
These embeddings are learned directly from the waveforms without the use of
linguistic transcriptions or conventional speech recognition technology. While
prior work has investigated this setting in the monolingual case using English
speech data, this work represents the first effort to apply these techniques to
languages beyond English. Using spoken captions collected in English and Hindi,
we show that the same model architecture can be successfully applied to both
languages. Further, we demonstrate that training a multilingual model
simultaneously on both languages offers improved performance over the
monolingual models. Finally, we show that these models are capable of
performing semantic cross-lingual speech-to-speech retrieval.
| 2,018 | Computation and Language |
Leveraging Intra-User and Inter-User Representation Learning for
Automated Hate Speech Detection | Hate speech detection is a critical, yet challenging problem in Natural
Language Processing (NLP). Despite the existence of numerous studies dedicated
to the development of NLP hate speech detection approaches, the accuracy is
still poor. The central problem is that social media posts are short and noisy,
and most existing hate speech detection solutions take each post as an isolated
input instance, which is likely to yield high false positive and negative
rates. In this paper, we radically improve automated hate speech detection by
presenting a novel model that leverages intra-user and inter-user
representation learning for robust hate speech detection on Twitter. In
addition to the target Tweet, we collect and analyze the user's historical
posts to model intra-user Tweet representations. To suppress the noise in a
single Tweet, we also model the similar Tweets posted by all other users with
reinforced inter-user representation learning techniques. Experimentally, we
show that leveraging these two representations can significantly improve the
f-score of a strong bidirectional LSTM baseline model by 10.1%.
| 2,018 | Computation and Language |
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition | Describes an audio dataset of spoken words designed to help train and
evaluate keyword spotting systems. Discusses why this task is an interesting
challenge, and why it requires a specialized dataset that is different from
conventional datasets used for automatic speech recognition of full sentences.
Suggests a methodology for reproducible and comparable accuracy metrics for
this task. Describes how the data was collected and verified, what it contains,
previous versions and properties. Concludes by reporting baseline results of
models trained on this dataset.
| 2,018 | Computation and Language |
A GPU-based WFST Decoder with Exact Lattice Generation | We describe initial work on an extension of the Kaldi toolkit that supports
weighted finite-state transducer (WFST) decoding on Graphics Processing Units
(GPUs). We implement token recombination as an atomic GPU operation in order to
fully parallelize the Viterbi beam search, and propose a dynamic load balancing
strategy for more efficient token passing scheduling among GPU threads. We also
redesign the exact lattice generation and lattice pruning algorithms for better
utilization of the GPUs. Experiments on the Switchboard corpus show that the
proposed method achieves identical 1-best results and lattice quality in
recognition and confidence measure tasks, while running 3 to 15 times faster
than the single process Kaldi decoder. The above results are reported on
different GPU architectures. Additionally we obtain a 46-fold speedup with
sequence parallelism and multi-process service (MPS) in GPU.
| 2,018 | Computation and Language |
Efficient Graph-based Word Sense Induction by Distributional Inclusion
Vector Embeddings | Word sense induction (WSI), which addresses polysemy by unsupervised
discovery of multiple word senses, resolves ambiguities for downstream NLP
tasks and also makes word representations more interpretable. This paper
proposes an accurate and efficient graph-based method for WSI that builds a
global non-negative vector embedding basis (which are interpretable like
topics) and clusters the basis indexes in the ego network of each polysemous
word. By adopting distributional inclusion vector embeddings as our basis
formation model, we avoid the expensive step of nearest neighbor search that
plagues other graph-based methods without sacrificing the quality of sense
clusters. Experiments on three datasets show that our proposed method produces
similar or better sense clusters and embeddings compared with previous
state-of-the-art methods while being significantly more efficient.
| 2,018 | Computation and Language |
Question Answering over Freebase via Attentive RNN with Similarity
Matrix based CNN | With the rapid growth of knowledge bases (KBs), question answering over
knowledge base, a.k.a. KBQA has drawn huge attention in recent years. Most of
the existing KBQA methods follow so called encoder-compare framework. They map
the question and the KB facts to a common embedding space, in which the
similarity between the question vector and the fact vectors can be conveniently
computed. This, however, inevitably loses original words interaction
information. To preserve more original information, we propose an attentive
recurrent neural network with similarity matrix based convolutional neural
network (AR-SMCNN) model, which is able to capture comprehensive hierarchical
information utilizing the advantages of both RNN and CNN. We use RNN to capture
semantic-level correlation by its sequential modeling nature, and use an
attention mechanism to keep track of the entities and relations simultaneously.
Meanwhile, we use a similarity matrix based CNN with two-directions pooling to
extract literal-level words interaction matching utilizing CNNs strength of
modeling spatial correlation among data. Moreover, we have developed a new
heuristic extension method for entity detection, which significantly decreases
the effect of noise. Our method has outperformed the state-of-the-arts on
SimpleQuestion benchmark in both accuracy and efficiency.
| 2,018 | Computation and Language |
A Hierarchical Latent Structure for Variational Conversation Modeling | Variational autoencoders (VAE) combined with hierarchical RNNs have emerged
as a powerful framework for conversation modeling. However, they suffer from
the notorious degeneration problem, where the decoders learn to ignore latent
variables and reduce to vanilla RNNs. We empirically show that this degeneracy
occurs mostly due to two reasons. First, the expressive power of hierarchical
RNN decoders is often high enough to model the data using only its decoding
distributions without relying on the latent variables. Second, the conditional
VAE structure whose generation process is conditioned on a context, makes the
range of training targets very sparse; that is, the RNN decoders can easily
overfit to the training data ignoring the latent variables. To solve the
degeneration problem, we propose a novel model named Variational Hierarchical
Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical
structure of latent variables, and (2) exploiting an utterance drop
regularization. With evaluations on two datasets of Cornell Movie Dialog and
Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent
variables and outperforms state-of-the-art models for conversation generation.
Moreover, it can perform several new utterance control tasks, thanks to its
hierarchical latent structure.
| 2,018 | Computation and Language |
Who framed Roger Reindeer? De-censorship of Facebook posts by snippet
classification | This paper considers online news censorship and it concentrates on censorship
of identities. Obfuscating identities may occur for disparate reasons, from
military to judiciary ones. In the majority of cases, this happens to protect
individuals from being identified and persecuted by hostile people. However,
being the collaborative web characterised by a redundancy of information, it is
not unusual that the same fact is reported by multiple sources, which may not
apply the same restriction policies in terms of censorship. Also, the proven
aptitude of social network users to disclose personal information leads to the
phenomenon that comments to news can reveal the data withheld in the news
itself. This gives us a mean to figure out who the subject of the censored news
is. We propose an adaptation of a text analysis approach to unveil censored
identities. The approach is tested on a synthesised scenario, which however
resembles a real use case. Leveraging a text analysis based on a context
classifier trained over snippets from posts and comments of Facebook pages, we
achieve promising results. Despite the quite constrained settings in which we
operate -- such as considering only snippets of very short length -- our system
successfully detects the censored name, choosing among 10 different candidate
names, in more than 50\% of the investigated cases. This outperforms the
results of two reference baselines. The findings reported in this paper, other
than being supported by a thorough experimental methodology and interesting on
their own, also pave the way for further investigation on the insidious issues
of censorship on the web.
| 2,018 | Computation and Language |
Mining Social Media for Newsgathering: A Review | Social media is becoming an increasingly important data source for learning
about breaking news and for following the latest developments of ongoing news.
This is in part possible thanks to the existence of mobile devices, which
allows anyone with access to the Internet to post updates from anywhere,
leading in turn to a growing presence of citizen journalism. Consequently,
social media has become a go-to resource for journalists during the process of
newsgathering. Use of social media for newsgathering is however challenging,
and suitable tools are needed in order to facilitate access to useful
information for reporting. In this paper, we provide an overview of research in
data mining and natural language processing for mining social media for
newsgathering. We discuss five different areas that researchers have worked on
to mitigate the challenges inherent to social media newsgathering: news
discovery, curation of news, validation and verification of content,
newsgathering dashboards, and other tasks. We outline the progress made so far
in the field, summarise the current challenges as well as discuss future
directions in the use of computational journalism to assist with social media
newsgathering. This review is relevant to computer scientists researching news
in social media as well as for interdisciplinary researchers interested in the
intersection of computer science and journalism.
| 2,019 | Computation and Language |
Deep Learning for Digital Text Analytics: Sentiment Analysis | In today's scenario, imagining a world without negativity is something very
unrealistic, as bad NEWS spreads more virally than good ones. Though it seems
impractical in real life, this could be implemented by building a system using
Machine Learning and Natural Language Processing techniques in identifying the
news datum with negative shade and filter them by taking only the news with
positive shade (good news) to the end user. In this work, around two lakhs
datum have been trained and tested using a combination of rule-based and data
driven approaches. VADER along with a filtration method has been used as an
annotating tool followed by statistical Machine Learning approach that have
used Document Term Matrix (representation) and Support Vector Machine
(classification). Deep Learning algorithms then came into picture to make this
system reliable (Doc2Vec) which finally ended up with Convolutional Neural
Network(CNN) that yielded better results than the other experimented modules.
It showed up a training accuracy of 96%, while a test accuracy of (internal and
external news datum) above 85% was obtained.
| 2,018 | Computation and Language |
Achieving Fluency and Coherency in Task-oriented Dialog | We consider real world task-oriented dialog settings, where agents need to
generate both fluent natural language responses and correct external actions
like database queries and updates. We demonstrate that, when applied to
customer support chat transcripts, Sequence to Sequence (Seq2Seq) models often
generate short, incoherent and ungrammatical natural language responses that
are dominated by words that occur with high frequency in the training data.
These phenomena do not arise in synthetic datasets such as bAbI, where we show
Seq2Seq models are nearly perfect. We develop techniques to learn embeddings
that succinctly capture relevant information from the dialog history, and
demonstrate that nearest neighbor based approaches in this learned neural
embedding space generate more fluent responses. However, we see that these
methods are not able to accurately predict when to execute an external action.
We show how to combine nearest neighbor and Seq2Seq methods in a hybrid model,
where nearest neighbor is used to generate fluent responses and Seq2Seq type
models ensure dialog coherency and generate accurate external actions. We show
that this approach is well suited for customer support scenarios, where agents'
responses are typically script-driven, and correct external actions are
critically important. The hybrid model on the customer support data achieves a
78% relative improvement in fluency scores, and a 130% improvement in accuracy
of external calls.
| 2,018 | Computation and Language |
Reference-less Measure of Faithfulness for Grammatical Error Correction | We propose USim, a semantic measure for Grammatical Error Correction (GEC)
that measures the semantic faithfulness of the output to the source, thereby
complementing existing reference-less measures (RLMs) for measuring the
output's grammaticality. USim operates by comparing the semantic symbolic
structure of the source and the correction, without relying on manually-curated
references. Our experiments establish the validity of USim, by showing that (1)
semantic annotation can be consistently applied to ungrammatical text; (2)
valid corrections obtain a high USim similarity score to the source; and (3)
invalid corrections obtain a lower score.
| 2,018 | Computation and Language |
Generating Clues for Gender based Occupation De-biasing in Text | Vast availability of text data has enabled widespread training and use of AI
systems that not only learn and predict attributes from the text but also
generate text automatically. However, these AI models also learn gender, racial
and ethnic biases present in the training data. In this paper, we present the
first system that discovers the possibility that a given text portrays a gender
stereotype associated with an occupation. If the possibility exists, the system
offers counter-evidences of opposite gender also being associated with the same
occupation in the context of user-provided geography and timespan. The system
thus enables text de-biasing by assisting a human-in-the-loop. The system can
not only act as a text pre-processor before training any AI model but also help
human story writers write stories free of occupation-level gender bias in the
geographical and temporal context of their choice.
| 2,018 | Computation and Language |
Generating Multilingual Parallel Corpus Using Subtitles | Neural Machine Translation with its significant results, still has a great
problem: lack or absence of parallel corpus for many languages. This article
suggests a method for generating considerable amount of parallel corpus for any
language pairs, extracted from open source materials existing on the Internet.
Parallel corpus contents will be derived from video subtitles. It needs a set
of video titles, with some attributes like release date, rating, duration and
etc. Process of finding and downloading subtitle pairs for desired language
pairs is automated by using a crawler. Finally sentence pairs will be extracted
from synchronous dialogues in subtitles. The main problem of this method is
unsynchronized subtitle pairs. Therefore subtitles will be verified before
downloading. If two subtitle were not synchronized, then another subtitle of
that video will be processed till it finds the matching subtitle. Using this
approach gives ability to make context based parallel corpus through filtering
videos by genre. Context based corpus can be used in complex translators which
decode sentences by different networks after determining contents subject.
Languages have many differences in their formal and informal styles, including
words and syntax. Other advantage of this method is to make corpus of informal
style of languages. Because most of movies dialogues are parts of a
conversation. So they had informal style. This feature of generated corpus can
be used in real-time translators to have more accurate conversation
translations.
| 2,018 | Computation and Language |
Sentiment Transfer using Seq2Seq Adversarial Autoencoders | Expressing in language is subjective. Everyone has a different style of
reading and writing, apparently it all boil downs to the way their mind
understands things (in a specific format). Language style transfer is a way to
preserve the meaning of a text and change the way it is expressed. Progress in
language style transfer is lagged behind other domains, such as computer
vision, mainly because of the lack of parallel data, use cases, and reliable
evaluation metrics. In response to the challenge of lacking parallel data, we
explore learning style transfer from non-parallel data. We propose a model
combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The
key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks.
Considering the problem of evaluating style transfer tasks, we frame the
problem as sentiment transfer and evaluation using a sentiment classifier to
calculate how many sentiments was the model able to transfer. We report our
results on several kinds of models.
| 2,018 | Computation and Language |
ISIS at its apogee: the Arabic discourse on Twitter and what we can
learn from that about ISIS support and Foreign Fighters | We analyze 26.2 million comments published in Arabic language on Twitter,
from July 2014 to January 2015, when ISIS' strength reached its peak and the
group was prominently expanding the territorial area under its control. By
doing that, we are able to measure the share of support and aversion toward the
Islamic State within the online Arab communities. We then investigate two
specific topics. First, by exploiting the time-granularity of the tweets, we
link the opinions with daily events to understand the main determinants of the
changing trend in support toward ISIS. Second, by taking advantage of the
geographical locations of tweets, we explore the relationship between online
opinions across countries and the number of foreign fighters joining ISIS.
| 2,018 | Computation and Language |
Multi-Task Learning for Argumentation Mining in Low-Resource Settings | We investigate whether and where multi-task learning (MTL) can improve
performance on NLP problems related to argumentation mining (AM), in particular
argument component identification. Our results show that MTL performs
particularly well (and better than single-task learning) when little training
data is available for the main task, a common scenario in AM. Our findings
challenge previous assumptions that conceptualizations across AM datasets are
divergent and that MTL is difficult for semantic or higher-level tasks.
| 2,018 | Computation and Language |
Natural Language Statistical Features of LSTM-generated Texts | Long Short-Term Memory (LSTM) networks have recently shown remarkable
performance in several tasks dealing with natural language generation, such as
image captioning or poetry composition. Yet, only few works have analyzed text
generated by LSTMs in order to quantitatively evaluate to which extent such
artificial texts resemble those generated by humans. We compared the
statistical structure of LSTM-generated language to that of written natural
language, and to those produced by Markov models of various orders. In
particular, we characterized the statistical structure of language by assessing
word-frequency statistics, long-range correlations, and entropy measures. Our
main finding is that while both LSTM and Markov-generated texts can exhibit
features similar to real ones in their word-frequency statistics and entropy
measures, LSTM-texts are shown to reproduce long-range correlations at scales
comparable to those found in natural language. Moreover, for LSTM networks a
temperature-like parameter controlling the generation process shows an optimal
value---for which the produced texts are closest to real language---consistent
across all the different statistical features investigated.
| 2,019 | Computation and Language |
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation | Supervised training of abstractive language generation models results in
learning conditional probabilities over language sequences based on the
supervised training signal. When the training signal contains a variety of
writing styles, such models may end up learning an 'average' style that is
directly influenced by the training data make-up and cannot be controlled by
the needs of an application. We describe a family of model architectures
capable of capturing both generic language characteristics via shared model
parameters, as well as particular style characteristics via private model
parameters. Such models are able to generate language according to a specific
learned style, while still taking advantage of their power to model generic
language phenomena. Furthermore, we describe an extension that uses a mixture
of output distributions from all learned styles to perform on-the fly style
adaptation based on the textual input alone. Experimentally, we find that the
proposed models consistently outperform models that encapsulate single-style or
average-style language generation capabilities.
| 2,018 | Computation and Language |
Predicting Twitter User Socioeconomic Attributes with Network and
Language Information | Inferring socioeconomic attributes of social media users such as occupation
and income is an important problem in computational social science. Automated
inference of such characteristics has applications in personalised recommender
systems, targeted computational advertising and online political campaigning.
While previous work has shown that language features can reliably predict
socioeconomic attributes on Twitter, employing information coming from users'
social networks has not yet been explored for such complex user
characteristics. In this paper, we describe a method for predicting the
occupational class and the income of Twitter users given information extracted
from their extended networks by learning a low-dimensional vector
representation of users, i.e. graph embeddings. We use this representation to
train predictive models for occupational class and income. Results on two
publicly available datasets show that our method consistently outperforms the
state-of-the-art methods in both tasks. We also obtain further significant
improvements when we combine graph embeddings with textual features,
demonstrating that social network and language information are complementary.
| 2,018 | Computation and Language |
Evaluating Word Embedding Hyper-Parameters for Similarity and Analogy
Tasks | The versatility of word embeddings for various applications is attracting
researchers from various fields. However, the impact of hyper-parameters when
training embedding model is often poorly understood. How much do
hyper-parameters such as vector dimensions and corpus size affect the quality
of embeddings, and how do these results translate to downstream applications?
Using standard embedding evaluation metrics and datasets, we conduct a study to
empirically measure the impact of these hyper-parameters.
| 2,018 | Computation and Language |
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical
Abbreviation Expansion | In the medical domain, identifying and expanding abbreviations in clinical
texts is a vital task for both better human and machine understanding. It is a
challenging task because many abbreviations are ambiguous especially for
intensive care medicine texts, in which phrase abbreviations are frequently
used. Besides the fact that there is no universal dictionary of clinical
abbreviations and no universal rules for abbreviation writing, such texts are
difficult to acquire, expensive to annotate and even sometimes, confusing to
domain experts. This paper proposes a novel and effective approach - exploiting
task-oriented resources to learn word embeddings for expanding abbreviations in
clinical notes. We achieved 82.27% accuracy, close to expert human performance.
| 2,018 | Computation and Language |
English Out-of-Vocabulary Lexical Evaluation Task | Unlike previous unknown nouns tagging task, this is the first attempt to
focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require
any prior knowledge. The OOV words are words that only appear in test samples.
The goal of tasks is to provide solutions for OOV lexical classification and
prediction. The tasks require annotators to conclude the attributes of the OOV
words based on their related contexts. Then, we utilize unsupervised word
embedding methods such as Word2Vec and Word2GM to perform the baseline
experiments on the categorical classification task and OOV words attribute
prediction tasks.
| 2,019 | Computation and Language |
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social
Media | While social media empowers freedom of expression and individual voices, it
also enables anti-social behavior, online harassment, cyberbullying, and hate
speech. In this paper, we deepen our understanding of online hate speech by
focusing on a largely neglected but crucial aspect of hate speech -- its
target: either "directed" towards a specific person or entity, or "generalized"
towards a group of people sharing a common protected characteristic. We perform
the first linguistic and psycholinguistic analysis of these two forms of hate
speech and reveal the presence of interesting markers that distinguish these
types of hate speech. Our analysis reveals that Directed hate speech, in
addition to being more personal and directed, is more informal, angrier, and
often explicitly attacks the target (via name calling) with fewer analytic
words and more words suggesting authority and influence. Generalized hate
speech, on the other hand, is dominated by religious hate, is characterized by
the use of lethal words such as murder, exterminate, and kill; and quantity
words such as million and many. Altogether, our work provides a data-driven
analysis of the nuances of online-hate speech that enables not only a deepened
understanding of hate speech and its social implications but also its
detection.
| 2,018 | Computation and Language |
Training a Ranking Function for Open-Domain Question Answering | In recent years, there have been amazing advances in deep learning methods
for machine reading. In machine reading, the machine reader has to extract the
answer from the given ground truth paragraph. Recently, the state-of-the-art
machine reading models achieve human level performance in SQuAD which is a
reading comprehension-style question answering (QA) task. The success of
machine reading has inspired researchers to combine information retrieval with
machine reading to tackle open-domain QA. However, these systems perform poorly
compared to reading comprehension-style QA because it is difficult to retrieve
the pieces of paragraphs that contain the answer to the question. In this
study, we propose two neural network rankers that assign scores to different
passages based on their likelihood of containing the answer to a given
question. Additionally, we analyze the relative importance of semantic
similarity and word level relevance matching in open-domain QA.
| 2,018 | Computation and Language |
A Capsule Network-based Embedding Model for Search Personalization | Search personalization aims to tailor search results to each specific user
based on the user's personal interests and preferences (i.e., the user
profile). Recent research approaches to search personalization by modelling the
potential 3-way relationship between the submitted query, the user and the
search results (i.e., documents). That relationship is then used to personalize
the search results to that user. In this paper, we introduce a novel embedding
model based on capsule network, which recently is a breakthrough in deep
learning, to model the 3-way relationships for search personalization. In the
model, each user (submitted query or returned document) is embedded by a vector
in the same vector space. The 3-way relationship is described as a triple of
(query, user, document) which is then modeled as a 3-column matrix containing
the three embedding vectors. After that, the 3-column matrix is fed into a deep
learning architecture to re-rank the search results returned by a basis ranker.
Experimental results on query logs from a commercial web search engine show
that our model achieves better performances than the basis ranker as well as
strong search personalization baselines.
| 2,019 | Computation and Language |
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention
Mechanism for Sentiment Classification | This paper describes the participation of Amobee in the shared sentiment
analysis task at SemEval 2018. We participated in all the English sub-tasks and
the Spanish valence tasks. Our system consists of three parts: training
task-specific word embeddings, training a model consisting of
gated-recurrent-units (GRU) with a convolution neural network (CNN) attention
mechanism and training stacking-based ensembles for each of the sub-tasks. Our
algorithm reached 3rd and 1st places in the valence ordinal classification
sub-tasks in English and Spanish, respectively.
| 2,019 | Computation and Language |
EventKG: A Multilingual Event-Centric Temporal Knowledge Graph | One of the key requirements to facilitate semantic analytics of information
regarding contemporary and historical events on the Web, in the news and in
social media is the availability of reference knowledge repositories containing
comprehensive representations of events and temporal relations. Existing
knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata,
focus mostly on entity-centric information and are insufficient in terms of
their coverage and completeness with respect to events and temporal relations.
EventKG presented in this paper is a multilingual event-centric temporal
knowledge graph that addresses this gap. EventKG incorporates over 690 thousand
contemporary and historical events and over 2.3 million temporal relations
extracted from several large-scale knowledge graphs and semi-structured sources
and makes them available through a canonical representation.
| 2,018 | Computation and Language |
A Survey on Neural Network-Based Summarization Methods | Automatic text summarization, the automated process of shortening a text
while reserving the main ideas of the document(s), is a critical research area
in natural language processing. The aim of this literature review is to survey
the recent work on neural-based models in automatic text summarization. We
examine in detail ten state-of-the-art neural-based summarizers: five
abstractive models and five extractive models. In addition, we discuss the
related techniques that can be applied to the summarization tasks and present
promising paths for future research in neural-based summarization.
| 2,018 | Computation and Language |
Predicting Good Configurations for GitHub and Stack Overflow Topic
Models | Software repositories contain large amounts of textual data, ranging from
source code comments and issue descriptions to questions, answers, and comments
on Stack Overflow. To make sense of this textual data, topic modelling is
frequently used as a text-mining tool for the discovery of hidden semantic
structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used
topic model that aims to explain the structure of a corpus by grouping texts.
LDA requires multiple parameters to work well, and there are only rough and
sometimes conflicting guidelines available on how these parameters should be
set. In this paper, we contribute (i) a broad study of parameters to arrive at
good local optima for GitHub and Stack Overflow text corpora, (ii) an
a-posteriori characterisation of text corpora related to eight programming
languages, and (iii) an analysis of corpus feature importance via per-corpus
LDA configuration. We find that (1) popular rules of thumb for topic modelling
parameter configuration are not applicable to the corpora used in our
experiments, (2) corpora sampled from GitHub and Stack Overflow have different
characteristics and require different configurations to achieve good model fit,
and (3) we can predict good configurations for unseen corpora reliably. These
findings support researchers and practitioners in efficiently determining
suitable configurations for topic modelling when analysing textual data
contained in software repositories.
| 2,019 | Computation and Language |
An Ontology-Based Dialogue Management System for Banking and Finance
Dialogue Systems | Keeping the dialogue state in dialogue systems is a notoriously difficult
task. We introduce an ontology-based dialogue manage(OntoDM), a dialogue
manager that keeps the state of the conversation, provides a basis for anaphora
resolution and drives the conversation via domain ontologies. The banking and
finance area promises great potential for disambiguating the context via a rich
set of products and specificity of proper nouns, named entities and verbs. We
used ontologies both as a knowledge base and a basis for the dialogue manager;
the knowledge base component and dialogue manager components coalesce in a
sense. Domain knowledge is used to track Entities of Interest, i.e. nodes
(classes) of the ontology which happen to be products and services. In this way
we also introduced conversation memory and attention in a sense. We finely
blended linguistic methods, domain-driven keyword ranking and domain ontologies
to create ways of domain-driven conversation. Proposed framework is used in our
in-house German language banking and finance chatbots. General challenges of
German language processing and finance-banking domain chatbot language models
and lexicons are also introduced. This work is still in progress, hence no
success metrics have been introduced yet.
| 2,018 | Computation and Language |
Incorporating Dictionaries into Deep Neural Networks for the Chinese
Clinical Named Entity Recognition | Clinical Named Entity Recognition (CNER) aims to identify and classify
clinical terms such as diseases, symptoms, treatments, exams, and body parts in
electronic health records, which is a fundamental and crucial task for clinical
and translational research. In recent years, deep neural networks have achieved
significant success in named entity recognition and many other Natural Language
Processing (NLP) tasks. Most of these algorithms are trained end to end, and
can automatically learn features from large scale labeled datasets. However,
these data-driven methods typically lack the capability of processing rare or
unseen entities. Previous statistical methods and feature engineering practice
have demonstrated that human knowledge can provide valuable information for
handling rare and unseen cases. In this paper, we address the problem by
incorporating dictionaries into deep neural networks for the Chinese CNER task.
Two different architectures that extend the Bi-directional Long Short-Term
Memory (Bi-LSTM) neural network and five different feature representation
schemes are proposed to handle the task. Computational results on the CCKS-2017
Task 2 benchmark dataset show that the proposed method achieves the highly
competitive performance compared with the state-of-the-art deep learning
methods.
| 2,018 | Computation and Language |
Pieces of Eight: 8-bit Neural Machine Translation | Neural machine translation has achieved levels of fluency and adequacy that
would have been surprising a short time ago. Output quality is extremely
relevant for industry purposes, however it is equally important to produce
results in the shortest time possible, mainly for latency-sensitive
applications and to control cloud hosting costs. In this paper we show the
effectiveness of translating with 8-bit quantization for models that have been
trained using 32-bit floating point values. Results show that 8-bit translation
makes a non-negligible impact in terms of speed with no degradation in accuracy
and adequacy.
| 2,018 | Computation and Language |
S\'i o no, qu\`e penses? Catalonian Independence and Linguistic Identity
on Social Media | Political identity is often manifested in language variation, but the
relationship between the two is still relatively unexplored from a quantitative
perspective. This study examines the use of Catalan, a language local to the
semi-autonomous region of Catalonia in Spain, on Twitter in discourse related
to the 2017 independence referendum. We corroborate prior findings that
pro-independence tweets are more likely to include the local language than
anti-independence tweets. We also find that Catalan is used more often in
referendum-related discourse than in other contexts, contrary to prior findings
on language variation. This suggests a strong role for the Catalan language in
the expression of Catalonian political identity.
| 2,018 | Computation and Language |
Automatic Language Identification System for Hindi and Magahi | Language identification has become a prerequisite for all kinds of automated
text processing systems. In this paper, we present a rule-based language
identifier tool for two closely related Indo-Aryan languages: Hindi and Magahi.
This system has currently achieved an accuracy of approx 86.34%. We hope to
improve this in the future. Automatic identification of languages will be
significant in the accuracy of output of Web Crawlers.
| 2,018 | Computation and Language |
Developing Far-Field Speaker System Via Teacher-Student Learning | In this study, we develop the keyword spotting (KWS) and acoustic model (AM)
components in a far-field speaker system. Specifically, we use teacher-student
(T/S) learning to adapt a close-talk well-trained production AM to far-field by
using parallel close-talk and simulated far-field data. We also use T/S
learning to compress a large-size KWS model into a small-size one to fit the
device computational cost. Without the need of transcription, T/S learning well
utilizes untranscribed data to boost the model performance in both the AM
adaptation and KWS model compression. We further optimize the models with
sequence discriminative training and live data to reach the best performance of
systems. The adapted AM improved from the baseline by 72.60% and 57.16%
relative word error rate reduction on play-back and live test data,
respectively. The final KWS model size was reduced by 27 times from a
large-size KWS model without losing accuracy.
| 2,018 | Computation and Language |
"With 1 follower I must be AWESOME :P". Exploring the role of irony
markers in irony recognition | Conversations in social media often contain the use of irony or sarcasm, when
the users say the opposite of what they really mean. Irony markers are the
meta-communicative clues that inform the reader that an utterance is ironic. We
propose a thorough analysis of theoretically grounded irony markers in two
social media platforms: $Twitter$ and $Reddit$. Classification and frequency
analysis show that for $Twitter$, typographic markers such as emoticons and
emojis are the most discriminative markers to recognize ironic utterances,
while for $Reddit$ the morphological markers (e.g., interjections, tag
questions) are the most discriminative.
| 2,018 | Computation and Language |
ClassiNet -- Predicting Missing Features for Short-Text Classification | The fundamental problem in short-text classification is \emph{feature
sparseness} -- the lack of feature overlap between a trained model and a test
instance to be classified. We propose \emph{ClassiNet} -- a network of
classifiers trained for predicting missing features in a given instance, to
overcome the feature sparseness problem. Using a set of unlabeled training
instances, we first learn binary classifiers as feature predictors for
predicting whether a particular feature occurs in a given instance. Next, each
feature predictor is represented as a vertex $v_i$ in the ClassiNet where a
one-to-one correspondence exists between feature predictors and vertices. The
weight of the directed edge $e_{ij}$ connecting a vertex $v_i$ to a vertex
$v_j$ represents the conditional probability that given $v_i$ exists in an
instance, $v_j$ also exists in the same instance. We show that ClassiNets
generalize word co-occurrence graphs by considering implicit co-occurrences
between features. We extract numerous features from the trained ClassiNet to
overcome feature sparseness. In particular, for a given instance $\vec{x}$, we
find similar features from ClassiNet that did not appear in $\vec{x}$, and
append those features in the representation of $\vec{x}$. Moreover, we propose
a method based on graph propagation to find features that are indirectly
related to a given short-text. We evaluate ClassiNets on several benchmark
datasets for short-text classification. Our experimental results show that by
using ClassiNet, we can statistically significantly improve the accuracy in
short-text classification tasks, without having to use any external resources
such as thesauri for finding related features.
| 2,018 | Computation and Language |
Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by
Averaging Source Word Embeddings | Creating accurate meta-embeddings from pre-trained source embeddings has
received attention lately. Methods based on global and locally-linear
transformation and concatenation have shown to produce accurate
meta-embeddings. In this paper, we show that the arithmetic mean of two
distinct word embedding sets yields a performant meta-embedding that is
comparable or better than more complex meta-embedding learning methods. The
result seems counter-intuitive given that vector spaces in different source
embeddings are not comparable and cannot be simply averaged. We give insight
into why averaging can still produce accurate meta-embedding despite the
incomparability of the source vector spaces.
| 2,018 | Computation and Language |
Predicting Cyber Events by Leveraging Hacker Sentiment | Recent high-profile cyber attacks exemplify why organizations need better
cyber defenses. Cyber threats are hard to accurately predict because attackers
usually try to mask their traces. However, they often discuss exploits and
techniques on hacking forums. The community behavior of the hackers may provide
insights into groups' collective malicious activity. We propose a novel
approach to predict cyber events using sentiment analysis. We test our approach
using cyber attack data from 2 major business organizations. We consider 3
types of events: malicious software installation, malicious destination visits,
and malicious emails that surpassed the target organizations' defenses. We
construct predictive signals by applying sentiment analysis on hacker forum
posts to better understand hacker behavior. We analyze over 400K posts
generated between January 2016 and January 2018 on over 100 hacking forums both
on surface and Dark Web. We find that some forums have significantly more
predictive power than others. Sentiment-based models that leverage specific
forums can outperform state-of-the-art deep learning and time-series models on
forecasting cyber attacks weeks ahead of the events.
| 2,018 | Computation and Language |
The EcoLexicon Semantic Sketch Grammar: from Knowledge Patterns to Word
Sketches | Many projects have applied knowledge patterns (KPs) to the retrieval of
specialized information. Yet terminologists still rely on manual analysis of
concordance lines to extract semantic information, since there are no
user-friendly publicly available applications enabling them to find knowledge
rich contexts (KRCs). To fill this void, we have created the KP-based
EcoLexicon Semantic SketchGrammar (ESSG) in the well-known corpus query system
Sketch Engine. For the first time, the ESSG is now publicly available inSketch
Engine to query the EcoLexicon English Corpus. Additionally, reusing the ESSG
in any English corpus uploaded by the user enables Sketch Engine to extract
KRCs codifying generic-specific, part-whole, location, cause and function
relations, because most of the KPs are domain-independent. The information is
displayed in the form of summary lists (word sketches) containing the pairs of
terms linked by a given semantic relation. This paper describes the process of
building a KP-based sketch grammar with special focus on the last stage,
namely, the evaluation with refinement purposes. We conducted an initial
shallow precision and recall evaluation of the 64 English sketch grammar rules
created so far for hyponymy, meronymy and causality. Precision was measured
based on a random sample of concordances extracted from each word sketch type.
Recall was assessed based on a random sample of concordances where known term
pairs are found. The results are necessary for the improvement and refinement
of the ESSG. The noise of false positives helped to further specify the rules,
whereas the silence of false negatives allows us to find useful new patterns.
| 2,018 | Computation and Language |
Introducing two Vietnamese Datasets for Evaluating Semantic Models of
(Dis-)Similarity and Relatedness | We present two novel datasets for the low-resource language Vietnamese to
assess models of semantic similarity: ViCon comprises pairs of synonyms and
antonyms across word classes, thus offering data to distinguish between
similarity and dissimilarity. ViSim-400 provides degrees of similarity across
five semantic relations, as rated by human judges. The two datasets are
verified through standard co-occurrence and neural network models, showing
results comparable to the respective English datasets.
| 2,018 | Computation and Language |
Higher-order Coreference Resolution with Coarse-to-fine Inference | We introduce a fully differentiable approximation to higher-order inference
for coreference resolution. Our approach uses the antecedent distribution from
a span-ranking architecture as an attention mechanism to iteratively refine
span representations. This enables the model to softly consider multiple hops
in the predicted clusters. To alleviate the computational cost of this
iterative process, we introduce a coarse-to-fine approach that incorporates a
less accurate but more efficient bilinear factor, enabling more aggressive
pruning without hurting accuracy. Compared to the existing state-of-the-art
span-ranking approach, our model significantly improves accuracy on the English
OntoNotes benchmark, while being far more computationally efficient.
| 2,018 | Computation and Language |
Context and Humor: Understanding Amul advertisements of India | Contextual knowledge is the most important element in understanding language.
By contextual knowledge we mean both general knowledge and discourse knowledge
i.e. knowledge of the situational context, background knowledge and the
co-textual context [10]. In this paper, we will discuss the importance of
contextual knowledge in understanding the humor present in the cartoon based
Amul advertisements in India.In the process, we will analyze these
advertisements and also see if humor is an effective tool for advertising and
thereby, for marketing.These bilingual advertisements also expect the audience
to have the appropriate linguistic knowledge which includes knowledge of
English and Hindi vocabulary, morphology and syntax. Different techniques like
punning, portmanteaus and parodies of popular proverbs, expressions, acronyms,
famous dialogues, songs etc are employed to convey the message in a humorous
way. The present study will concentrate on these linguistic cues and the
required context for understanding wit and humor.
| 2,018 | Computation and Language |
GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation
Classification | SemEval 2018 Task 7 focuses on relation ex- traction and classification in
scientific literature. In this work, we present our tree-based LSTM network for
this shared task. Our approach placed 9th (of 28) for subtask 1.1 (relation
classification), and 5th (of 20) for subtask 1.2 (relation classification with
noisy entities). We also provide an ablation study of features included as
input to the network.
| 2,018 | Computation and Language |
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