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Non-Parametric Adaptation for Neural Machine Translation | Neural Networks trained with gradient descent are known to be susceptible to
catastrophic forgetting caused by parameter shift during the training process.
In the context of Neural Machine Translation (NMT) this results in poor
performance on heterogeneous datasets and on sub-tasks like rare phrase
translation. On the other hand, non-parametric approaches are immune to
forgetting, perfectly complementing the generalization ability of NMT. However,
attempts to combine non-parametric or retrieval based approaches with NMT have
only been successful on narrow domains, possibly due to over-reliance on
sentence level retrieval. We propose a novel n-gram level retrieval approach
that relies on local phrase level similarities, allowing us to retrieve
neighbors that are useful for translation even when overall sentence similarity
is low. We complement this with an expressive neural network, allowing our
model to extract information from the noisy retrieved context. We evaluate our
semi-parametric NMT approach on a heterogeneous dataset composed of WMT, IWSLT,
JRC-Acquis and OpenSubtitles, and demonstrate gains on all 4 evaluation sets.
The semi-parametric nature of our approach opens the door for non-parametric
domain adaptation, demonstrating strong inference-time adaptation performance
on new domains without the need for any parameter updates.
| 2,019 | Computation and Language |
Massively Multilingual Neural Machine Translation | Multilingual neural machine translation (NMT) enables training a single model
that supports translation from multiple source languages into multiple target
languages. In this paper, we push the limits of multilingual NMT in terms of
number of languages being used. We perform extensive experiments in training
massively multilingual NMT models, translating up to 102 languages to and from
English within a single model. We explore different setups for training such
models and analyze the trade-offs between translation quality and various
modeling decisions. We report results on the publicly available TED talks
multilingual corpus where we show that massively multilingual many-to-many
models are effective in low resource settings, outperforming the previous
state-of-the-art while supporting up to 59 languages. Our experiments on a
large-scale dataset with 102 languages to and from English and up to one
million examples per direction also show promising results, surpassing strong
bilingual baselines and encouraging future work on massively multilingual NMT.
| 2,019 | Computation and Language |
Improving Grounded Natural Language Understanding through Human-Robot
Dialog | Natural language understanding for robotics can require substantial domain-
and platform-specific engineering. For example, for mobile robots to
pick-and-place objects in an environment to satisfy human commands, we can
specify the language humans use to issue such commands, and connect concept
words like red can to physical object properties. One way to alleviate this
engineering for a new domain is to enable robots in human environments to adapt
dynamically---continually learning new language constructions and perceptual
concepts. In this work, we present an end-to-end pipeline for translating
natural language commands to discrete robot actions, and use clarification
dialogs to jointly improve language parsing and concept grounding. We train and
evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we
transfer the learned agent to a physical robot platform to demonstrate it in
the real world.
| 2,019 | Computation and Language |
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented
Architecture with Unlabeled Data | Neural machine translation systems have become state-of-the-art approaches
for Grammatical Error Correction (GEC) task. In this paper, we propose a
copy-augmented architecture for the GEC task by copying the unchanged words
from the source sentence to the target sentence. Since the GEC suffers from not
having enough labeled training data to achieve high accuracy. We pre-train the
copy-augmented architecture with a denoising auto-encoder using the unlabeled
One Billion Benchmark and make comparisons between the fully pre-trained model
and a partially pre-trained model. It is the first time copying words from the
source context and fully pre-training a sequence to sequence model are
experimented on the GEC task. Moreover, We add token-level and sentence-level
multi-task learning for the GEC task. The evaluation results on the CoNLL-2014
test set show that our approach outperforms all recently published
state-of-the-art results by a large margin. The code and pre-trained models are
released at https://github.com/zhawe01/fairseq-gec.
| 2,019 | Computation and Language |
Chinese-Japanese Unsupervised Neural Machine Translation Using
Sub-character Level Information | Unsupervised neural machine translation (UNMT) requires only monolingual data
of similar language pairs during training and can produce bi-directional
translation models with relatively good performance on alphabetic languages
(Lample et al., 2018). However, no research has been done to logographic
language pairs. This study focuses on Chinese-Japanese UNMT trained by data
containing sub-character (ideograph or stroke) level information which is
decomposed from character level data. BLEU scores of both character and
sub-character level systems were compared against each other and the results
showed that despite the effectiveness of UNMT on character level data,
sub-character level data could further enhance the performance, in which the
stroke level system outperformed the ideograph level system.
| 2,019 | Computation and Language |
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning
Over Paragraphs | Reading comprehension has recently seen rapid progress, with systems matching
humans on the most popular datasets for the task. However, a large body of work
has highlighted the brittleness of these systems, showing that there is much
work left to be done. We introduce a new English reading comprehension
benchmark, DROP, which requires Discrete Reasoning Over the content of
Paragraphs. In this crowdsourced, adversarially-created, 96k-question
benchmark, a system must resolve references in a question, perhaps to multiple
input positions, and perform discrete operations over them (such as addition,
counting, or sorting). These operations require a much more comprehensive
understanding of the content of paragraphs than what was necessary for prior
datasets. We apply state-of-the-art methods from both the reading comprehension
and semantic parsing literature on this dataset and show that the best systems
only achieve 32.7% F1 on our generalized accuracy metric, while expert human
performance is 96.0%. We additionally present a new model that combines reading
comprehension methods with simple numerical reasoning to achieve 47.0% F1.
| 2,019 | Computation and Language |
Open Information Extraction from Question-Answer Pairs | Open Information Extraction (OpenIE) extracts meaningful structured tuples
from free-form text. Most previous work on OpenIE considers extracting data
from one sentence at a time. We describe NeurON, a system for extracting tuples
from question-answer pairs. Since real questions and answers often contain
precisely the information that users care about, such information is
particularly desirable to extend a knowledge base with.
NeurON addresses several challenges. First, an answer text is often hard to
understand without knowing the question, and second, relevant information can
span multiple sentences. To address these, NeurON formulates extraction as a
multi-source sequence-to-sequence learning task, wherein it combines
distributed representations of a question and an answer to generate knowledge
facts. We describe experiments on two real-world datasets that demonstrate that
NeurON can find a significant number of new and interesting facts to extend a
knowledge base compared to state-of-the-art OpenIE methods.
| 2,019 | Computation and Language |
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition
from YouTube Videos | In this paper, we describe KT-Speech-Crawler: an approach for automatic
dataset construction for speech recognition by crawling YouTube videos. We
outline several filtering and post-processing steps, which extract samples that
can be used for training end-to-end neural speech recognition systems. In our
experiments, we demonstrate that a single-core version of the crawler can
obtain around 150 hours of transcribed speech within a day, containing an
estimated 3.5% word error rate in the transcriptions. Automatically collected
samples contain reading and spontaneous speech recorded in various conditions
including background noise and music, distant microphone recordings, and a
variety of accents and reverberation. When training a deep neural network on
speech recognition, we observed around 40\% word error rate reduction on the
Wall Street Journal dataset by integrating 200 hours of the collected samples
into the training set. The demo (http://emnlp-demo.lakomkin.me/) and the
crawler code (https://github.com/EgorLakomkin/KTSpeechCrawler) are publicly
available.
| 2,019 | Computation and Language |
Using natural language processing techniques to extract information on
the properties and functionalities of energetic materials from large text
corpora | The number of scientific journal articles and reports being published about
energetic materials every year is growing exponentially, and therefore
extracting relevant information and actionable insights from the latest
research is becoming a considerable challenge. In this work we explore how
techniques from natural language processing and machine learning can be used to
automatically extract chemical insights from large collections of documents. We
first describe how to download and process documents from a variety of sources
- journal articles, conference proceedings (including NTREM), the US Patent &
Trademark Office, and the Defense Technical Information Center archive on
archive.org. We present a custom NLP pipeline which uses open source NLP tools
to identify the names of chemical compounds and relates them to function words
("underwater", "rocket", "pyrotechnic") and property words ("elastomer",
"non-toxic"). After explaining how word embeddings work we compare the utility
of two popular word embeddings - word2vec and GloVe. Chemical-chemical and
chemical-application relationships are obtained by doing computations with word
vectors. We show that word embeddings capture latent information about
energetic materials, so that related materials appear close together in the
word embedding space.
| 2,019 | Computation and Language |
Towards NLP with Deep Learning: Convolutional Neural Networks and
Recurrent Neural Networks for Offensive Language Identification in Social
Media | This short paper presents the design decisions taken and challenges
encountered in completing SemEval Task 6, which poses the problem of
identifying and categorizing offensive language in tweets. Our proposed
solutions explore Deep Learning techniques, Linear Support Vector
classification and Random Forests to identify offensive tweets, to classify
offenses as targeted or untargeted and eventually to identify the target
subject type.
| 2,019 | Computation and Language |
Predicting and interpreting embeddings for out of vocabulary words in
downstream tasks | We propose a novel way to handle out of vocabulary (OOV) words in downstream
natural language processing (NLP) tasks. We implement a network that predicts
useful embeddings for OOV words based on their morphology and on the context in
which they appear. Our model also incorporates an attention mechanism
indicating the focus allocated to the left context words, the right context
words or the word's characters, hence making the prediction more interpretable.
The model is a ``drop-in'' module that is jointly trained with the downstream
task's neural network, thus producing embeddings specialized for the task at
hand. When the task is mostly syntactical, we observe that our model aims most
of its attention on surface form characters. On the other hand, for tasks more
semantical, the network allocates more attention to the surrounding words. In
all our tests, the module helps the network to achieve better performances in
comparison to the use of simple random embeddings.
| 2,018 | Computation and Language |
Predicting Algorithm Classes for Programming Word Problems | We introduce the task of algorithm class prediction for programming word
problems. A programming word problem is a problem written in natural language,
which can be solved using an algorithm or a program. We define classes of
various programming word problems which correspond to the class of algorithms
required to solve the problem. We present four new datasets for this task, two
multiclass datasets with 550 and 1159 problems each and two multilabel datasets
having 3737 and 3960 problems each. We pose the problem as a text
classification problem and train neural network and non-neural network-based
models on this task. Our best performing classifier gets an accuracy of 62.7
percent for the multiclass case on the five class classification dataset,
Codeforces Multiclass-5 (CFMC5). We also do some human-level analysis and
compare human performance with that of our text classification models. Our best
classifier has an accuracy only 9 percent lower than that of a human on this
task. To the best of our knowledge, these are the first reported results on
such a task. We make our code and datasets publicly available.
| 2,019 | Computation and Language |
Detecting dementia in Mandarin Chinese using transfer learning from a
parallel corpus | Machine learning has shown promise for automatic detection of Alzheimer's
disease (AD) through speech; however, efforts are hampered by a scarcity of
data, especially in languages other than English. We propose a method to learn
a correspondence between independently engineered lexicosyntactic features in
two languages, using a large parallel corpus of out-of-domain movie dialogue
data. We apply it to dementia detection in Mandarin Chinese, and demonstrate
that our method outperforms both unilingual and machine translation-based
baselines. This appears to be the first study that transfers feature domains in
detecting cognitive decline.
| 2,019 | Computation and Language |
Structural Supervision Improves Learning of Non-Local Grammatical
Dependencies | State-of-the-art LSTM language models trained on large corpora learn
sequential contingencies in impressive detail and have been shown to acquire a
number of non-local grammatical dependencies with some success. Here we
investigate whether supervision with hierarchical structure enhances learning
of a range of grammatical dependencies, a question that has previously been
addressed only for subject-verb agreement. Using controlled experimental
methods from psycholinguistics, we compare the performance of word-based LSTM
models versus two models that represent hierarchical structure and deploy it in
left-to-right processing: Recurrent Neural Network Grammars (RNNGs) (Dyer et
al., 2016) and a incrementalized version of the Parsing-as-Language-Modeling
configuration from Chariak et al., (2016). Models are tested on a diverse range
of configurations for two classes of non-local grammatical dependencies in
English---Negative Polarity licensing and Filler--Gap Dependencies. Using the
same training data across models, we find that structurally-supervised models
outperform the LSTM, with the RNNG demonstrating best results on both types of
grammatical dependencies and even learning many of the Island Constraints on
the filler--gap dependency. Structural supervision thus provides data
efficiency advantages over purely string-based training of neural language
models in acquiring human-like generalizations about non-local grammatical
dependencies.
| 2,019 | Computation and Language |
SECNLP: A Survey of Embeddings in Clinical Natural Language Processing | Traditional representations like Bag of words are high dimensional, sparse
and ignore the order as well as syntactic and semantic information. Distributed
vector representations or embeddings map variable length text to dense fixed
length vectors as well as capture the prior knowledge which can transferred to
downstream tasks. Even though embedding has become de facto standard for
representations in deep learning based NLP tasks in both general and clinical
domains, there is no survey paper which presents a detailed review of
embeddings in Clinical Natural Language Processing. In this survey paper, we
discuss various medical corpora and their characteristics, medical codes and
present a brief overview as well as comparison of popular embeddings models. We
classify clinical embeddings into nine types and discuss each embedding type in
detail. We discuss various evaluation methods followed by possible solutions to
various challenges in clinical embeddings. Finally, we conclude with some of
the future directions which will advance the research in clinical embeddings.
| 2,020 | Computation and Language |
From Knowledge Map to Mind Map: Artificial Imagination | Imagination is one of the most important factors which makes an artistic
painting unique and impressive. With the rapid development of Artificial
Intelligence, more and more researchers try to create painting with AI
technology automatically. However, lacking of imagination is still a main
problem for AI painting. In this paper, we propose a novel approach to inject
rich imagination into a special painting art Mind Map creation. We firstly
consider lexical and phonological similarities of seed word, then learn and
inherit original painting style of the author, and finally apply Dadaism and
impossibility of improvisation principles into painting process. We also design
several metrics for imagination evaluation. Experimental results show that our
proposed method can increase imagination of painting and also improve its
overall quality.
| 2,019 | Computation and Language |
Using Word Embeddings for Visual Data Exploration with Ontodia and
Wikidata | One of the big challenges in Linked Data consumption is to create visual and
natural language interfaces to the data usable for non-technical users. Ontodia
provides support for diagrammatic data exploration, showcased in this
publication in combination with the Wikidata dataset. We present improvements
to the natural language interface regarding exploring and querying Linked Data
entities. The method uses models of distributional semantics to find and rank
entity properties related to user input in Ontodia. Various word embedding
types and model settings are evaluated, and the results show that user
experience in visual data exploration benefits from the proposed approach.
| 2,019 | Computation and Language |
Relation Extraction Datasets in the Digital Humanities Domain and their
Evaluation with Word Embeddings | In this research, we manually create high-quality datasets in the digital
humanities domain for the evaluation of language models, specifically word
embedding models. The first step comprises the creation of unigram and n-gram
datasets for two fantasy novel book series for two task types each, analogy and
doesn't-match. This is followed by the training of models on the two book
series with various popular word embedding model types such as word2vec, GloVe,
fastText, or LexVec. Finally, we evaluate the suitability of word embedding
models for such specific relation extraction tasks in a situation of comparably
small corpus sizes. In the evaluations, we also investigate and analyze
particular aspects such as the impact of corpus term frequencies and task
difficulty on accuracy. The datasets, and the underlying system and word
embedding models are available on github and can be easily extended with new
datasets and tasks, be used to reproduce the presented results, or be
transferred to other domains.
| 2,023 | Computation and Language |
Polylingual Wordnet | Princeton WordNet is one of the most important resources for natural language
processing, but is only available for English. While it has been translated
using the expand approach to many other languages, this is an expensive manual
process. Therefore it would be beneficial to have a high-quality automatic
translation approach that would support NLP techniques, which rely on WordNet
in new languages. The translation of wordnets is fundamentally complex because
of the need to translate all senses of a word including low frequency senses,
which is very challenging for current machine translation approaches. For this
reason we leverage existing translations of WordNet in other languages to
identify contextual information for wordnet senses from a large set of generic
parallel corpora. We evaluate our approach using 10 translated wordnets for
European languages. Our experiment shows a significant improvement over
translation without any contextual information. Furthermore, we evaluate how
the choice of pivot languages affects performance of multilingual word sense
disambiguation.
| 2,019 | Computation and Language |
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings | Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite
recent progress in neural-based CWS. The limited amount of annotated data in
the target domain has been the key obstacle to a satisfactory performance. In
this paper, we propose a semi-supervised word-based approach to improving
cross-domain CWS given a baseline segmenter. Particularly, our model only
deploys word embeddings trained on raw text in the target domain, discarding
complex hand-crafted features and domain-specific dictionaries. Innovative
subsampling and negative sampling methods are proposed to derive word
embeddings optimized for CWS. We conduct experiments on five datasets in
special domains, covering domains in novels, medicine, and patent. Results show
that our model can obviously improve cross-domain CWS, especially in the
segmentation of domain-specific noun entities. The word F-measure increases by
over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and
unsupervised cross-domain CWS approaches with a large margin. We make our code
and data available on Github.
| 2,019 | Computation and Language |
Mining Dual Emotion for Fake News Detection | Emotion plays an important role in detecting fake news online. When
leveraging emotional signals, the existing methods focus on exploiting the
emotions of news contents that conveyed by the publishers (i.e., publisher
emotion). However, fake news often evokes high-arousal or activating emotions
of people, so the emotions of news comments aroused in the crowd (i.e., social
emotion) should not be ignored. Furthermore, it remains to be explored whether
there exists a relationship between publisher emotion and social emotion (i.e.,
dual emotion), and how the dual emotion appears in fake news. In this paper, we
verify that dual emotion is distinctive between fake and real news and propose
Dual Emotion Features to represent dual emotion and the relationship between
them for fake news detection. Further, we exhibit that our proposed features
can be easily plugged into existing fake news detectors as an enhancement.
Extensive experiments on three real-world datasets (one in English and the
others in Chinese) show that our proposed feature set: 1) outperforms the
state-of-the-art task-related emotional features; 2) can be well compatible
with existing fake news detectors and effectively improve the performance of
detecting fake news.
| 2,021 | Computation and Language |
Language and Dialect Identification of Cuneiform Texts | This article introduces a corpus of cuneiform texts from which the dataset
for the use of the Cuneiform Language Identification (CLI) 2019 shared task was
derived as well as some preliminary language identification experiments
conducted using that corpus. We also describe the CLI dataset and how it was
derived from the corpus. In addition, we provide some baseline language
identification results using the CLI dataset. To the best of our knowledge, the
experiments detailed here are the first time automatic language identification
methods have been used on cuneiform data.
| 2,019 | Computation and Language |
Negative Training for Neural Dialogue Response Generation | Although deep learning models have brought tremendous advancements to the
field of open-domain dialogue response generation, recent research results have
revealed that the trained models have undesirable generation behaviors, such as
malicious responses and generic (boring) responses. In this work, we propose a
framework named "Negative Training" to minimize such behaviors. Given a trained
model, the framework will first find generated samples that exhibit the
undesirable behavior, and then use them to feed negative training signals for
fine-tuning the model. Our experiments show that negative training can
significantly reduce the hit rate of malicious responses, or discourage
frequent responses and improve response diversity.
| 2,020 | Computation and Language |
Persona-Aware Tips Generation | Tips, as a compacted and concise form of reviews, were paid less attention by
researchers. In this paper, we investigate the task of tips generation by
considering the `persona' information which captures the intrinsic language
style of the users or the different characteristics of the product items. In
order to exploit the persona information, we propose a framework based on
adversarial variational auto-encoders (aVAE) for persona modeling from the
historical tips and reviews of users and items. The latent variables from aVAE
are regarded as persona embeddings. Besides representing persona using the
latent embeddings, we design a persona memory for storing the persona related
words for users and items. Pointer Network is used to retrieve persona wordings
from the memory when generating tips. Moreover, the persona embeddings are used
as latent factors by a rating prediction component to predict the sentiment of
a user over an item. Finally, the persona embeddings and the sentiment
information are incorporated into a recurrent neural networks based tips
generation component. Extensive experimental results are reported and discussed
to elaborate the peculiarities of our framework.
| 2,019 | Computation and Language |
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class
Distribution Mismatch in Conversational Classification | We present several techniques to tackle the mismatch in class distributions
between training and test data in the Contextual Emotion Detection task of
SemEval 2019, by extending the existing methods for class imbalance problem.
Reducing the distance between the distribution of prediction and ground truth,
they consistently show positive effects on the performance. Also we propose a
novel neural architecture which utilizes representation of overall context as
well as of each utterance. The combination of the methods and the models
achieved micro F1 score of about 0.766 on the final evaluation.
| 2,019 | Computation and Language |
Bidirectional Attentive Memory Networks for Question Answering over
Knowledge Bases | When answering natural language questions over knowledge bases (KBs),
different question components and KB aspects play different roles. However,
most existing embedding-based methods for knowledge base question answering
(KBQA) ignore the subtle inter-relationships between the question and the KB
(e.g., entity types, relation paths and context). In this work, we propose to
directly model the two-way flow of interactions between the questions and the
KB via a novel Bidirectional Attentive Memory Network, called BAMnet. Requiring
no external resources and only very few hand-crafted features, on the
WebQuestions benchmark, our method significantly outperforms existing
information-retrieval based methods, and remains competitive with
(hand-crafted) semantic parsing based methods. Also, since we use attention
mechanisms, our method offers better interpretability compared to other
baselines.
| 2,019 | Computation and Language |
Dixit: Interactive Visual Storytelling via Term Manipulation | In this paper, we introduce Dixit, an interactive visual storytelling system
that the user interacts with iteratively to compose a short story for a photo
sequence. The user initiates the process by uploading a sequence of photos.
Dixit first extracts text terms from each photo which describe the objects
(e.g., boy, bike) or actions (e.g., sleep) in the photo, and then allows the
user to add new terms or remove existing terms. Dixit then generates a short
story based on these terms. Behind the scenes, Dixit uses an LSTM-based model
trained on image caption data and FrameNet to distill terms from each image and
utilizes a transformer decoder to compose a context-coherent story. Users
change images or terms iteratively with Dixit to create the most ideal story.
Dixit also allows users to manually edit and rate stories. The proposed
procedure opens up possibilities for interpretable and controllable visual
storytelling, allowing users to understand the story formation rationale and to
intervene in the generation process.
| 2,019 | Computation and Language |
KBQA: Learning Question Answering over QA Corpora and Knowledge Bases | Question answering (QA) has become a popular way for humans to access
billion-scale knowledge bases. Unlike web search, QA over a knowledge base
gives out accurate and concise results, provided that natural language
questions can be understood and mapped precisely to structured queries over the
knowledge base. The challenge, however, is that a human can ask one question in
many different ways. Previous approaches have natural limits due to their
representations: rule based approaches only understand a small set of "canned"
questions, while keyword based or synonym based approaches cannot fully
understand the questions. In this paper, we design a new kind of question
representation: templates, over a billion scale knowledge base and a million
scale QA corpora. For example, for questions about a city's population, we
learn templates such as What's the population of $city?, How many people are
there in $city?. We learned 27 million templates for 2782 intents. Based on
these templates, our QA system KBQA effectively supports binary factoid
questions, as well as complex questions which are composed of a series of
binary factoid questions. Furthermore, we expand predicates in RDF knowledge
base, which boosts the coverage of knowledge base by 57 times. Our QA system
beats all other state-of-art works on both effectiveness and efficiency over
QALD benchmarks.
| 2,017 | Computation and Language |
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language
Navigation | We present the Frontier Aware Search with backTracking (FAST) Navigator, a
general framework for action decoding, that achieves state-of-the-art results
on the Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson
et. al. (2018). Given a natural language instruction and photo-realistic image
views of a previously unseen environment, the agent was tasked with navigating
from source to target location as quickly as possible. While all current
approaches make local action decisions or score entire trajectories using beam
search, ours balances local and global signals when exploring an unobserved
environment. Importantly, this lets us act greedily but use global signals to
backtrack when necessary. Applying FAST framework to existing state-of-the-art
models achieved a 17% relative gain, an absolute 6% gain on Success rate
weighted by Path Length (SPL).
| 2,019 | Computation and Language |
Sentence Embedding Alignment for Lifelong Relation Extraction | Conventional approaches to relation extraction usually require a fixed set of
pre-defined relations. Such requirement is hard to meet in many real
applications, especially when new data and relations are emerging incessantly
and it is computationally expensive to store all data and re-train the whole
model every time new data and relations come in. We formulate such a
challenging problem as lifelong relation extraction and investigate
memory-efficient incremental learning methods without catastrophically
forgetting knowledge learned from previous tasks. We first investigate a
modified version of the stochastic gradient methods with a replay memory, which
surprisingly outperforms recent state-of-the-art lifelong learning methods. We
further propose to improve this approach to alleviate the forgetting problem by
anchoring the sentence embedding space. Specifically, we utilize an explicit
alignment model to mitigate the sentence embedding distortion of the learned
model when training on new data and new relations. Experiment results on
multiple benchmarks show that our proposed method significantly outperforms the
state-of-the-art lifelong learning approaches.
| 2,019 | Computation and Language |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained
Entity Typing | Existing entity typing systems usually exploit the type hierarchy provided by
knowledge base (KB) schema to model label correlations and thus improve the
overall performance. Such techniques, however, are not directly applicable to
more open and practical scenarios where the type set is not restricted by KB
schema and includes a vast number of free-form types. To model the underly-ing
label correlations without access to manually annotated label structures, we
introduce a novel label-relational inductive bias, represented by a graph
propagation layer that effectively encodes both global label co-occurrence
statistics and word-level similarities.On a large dataset with over 10,000
free-form types, the graph-enhanced model equipped with an attention-based
matching module is able to achieve a much higher recall score while maintaining
a high-level precision. Specifically, it achieves a 15.3% relative F1
improvement and also less inconsistency in the outputs. We further show that a
simple modification of our proposed graph layer can also improve the
performance on a conventional and widely-tested dataset that only includes
KB-schema types.
| 2,019 | Computation and Language |
A Character-Level Approach to the Text Normalization Problem Based on a
New Causal Encoder | Text normalization is a ubiquitous process that appears as the first step of
many Natural Language Processing problems. However, previous Deep Learning
approaches have suffered from so-called silly errors, which are undetectable on
unsupervised frameworks, making those models unsuitable for deployment. In this
work, we make use of an attention-based encoder-decoder architecture that
overcomes these undetectable errors by using a fine-grained character-level
approach rather than a word-level one. Furthermore, our new general-purpose
encoder based on causal convolutions, called Causal Feature Extractor (CFE), is
introduced and compared to other common encoders. The experimental results show
the feasibility of this encoder, which leverages the attention mechanisms the
most and obtains better results in terms of accuracy, number of parameters and
convergence time. While our method results in a slightly worse initial accuracy
(92.74%), errors can be automatically detected and, thus, more readily solved,
obtaining a more robust model for deployment. Furthermore, there is still
plenty of room for future improvements that will push even further these
advantages.
| 2,019 | Computation and Language |
Multi-Instance Learning for End-to-End Knowledge Base Question Answering | End-to-end training has been a popular approach for knowledge base question
answering (KBQA). However, real world applications often contain answers of
varied quality for users' questions. It is not appropriate to treat all
available answers of a user question equally.
This paper proposes a novel approach based on multiple instance learning to
address the problem of noisy answers by exploring consensus among answers to
the same question in training end-to-end KBQA models. In particular, the QA
pairs are organized into bags with dynamic instance selection and different
options of instance weighting. Curriculum learning is utilized to select
instance bags during training. On the public CQA dataset, the new method
significantly improves both entity accuracy and the Rouge-L score over a
state-of-the-art end-to-end KBQA baseline.
| 2,019 | Computation and Language |
Creation and Evaluation of Datasets for Distributional Semantics Tasks
in the Digital Humanities Domain | Word embeddings are already well studied in the general domain, usually
trained on large text corpora, and have been evaluated for example on word
similarity and analogy tasks, but also as an input to downstream NLP processes.
In contrast, in this work we explore the suitability of word embedding
technologies in the specialized digital humanities domain. After training
embedding models of various types on two popular fantasy novel book series, we
evaluate their performance on two task types: term analogies, and word
intrusion. To this end, we manually construct test datasets with domain
experts. Among the contributions are the evaluation of various word embedding
techniques on the different task types, with the findings that even embeddings
trained on small corpora perform well for example on the word intrusion task.
Furthermore, we provide extensive and high-quality datasets in digital
humanities for further investigation, as well as the implementation to easily
reproduce or extend the experiments.
| 2,023 | Computation and Language |
Arabic natural language processing: An overview | Arabic is recognised as the 4th most used language of the Internet. Arabic
has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic
(MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or
in Roman script (Arabizi), which corresponds to Arabic written with Latin
letters, numerals and punctuation. Due to the complexity of this language and
the number of corresponding challenges for NLP, many surveys have been
conducted, in order to synthesise the work done on Arabic. However these
surveys principally focus on two varieties of Arabic (MSA and AD, written in
Arabic letters only), they are slightly old (no such survey since 2015) and
therefore do not cover recent resources and tools. To bridge the gap, we
propose a survey focusing on 90 recent research papers (74% of which were
published after 2015). Our study presents and classifies the work done on the
three varieties of Arabic, by concentrating on both Arabic and Arabizi, and
associates each work to its publicly available resources whenever available.
| 2,019 | Computation and Language |
Predicting Research Trends From Arxiv | We perform trend detection on two datasets of Arxiv papers, derived from its
machine learning (cs.LG) and natural language processing (cs.CL) categories.
Our approach is bottom-up: we first rank papers by their normalized citation
counts, then group top-ranked papers into different categories based on the
tasks that they pursue and the methods they use. We then analyze these
resulting topics. We find that the dominating paradigm in cs.CL revolves around
natural language generation problems and those in cs.LG revolve around
reinforcement learning and adversarial principles. By extrapolation, we predict
that these topics will remain lead problems/approaches in their fields in the
short- and mid-term.
| 2,019 | Computation and Language |
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and
Language Models | The goal of this paper is to simulate the benefits of jointly applying active
learning (AL) and semi-supervised training (SST) in a new speech recognition
application. Our data selection approach relies on confidence filtering, and
its impact on both the acoustic and language models (AM and LM) is studied.
While AL is known to be beneficial to AM training, we show that it also carries
out substantial improvements to the LM when combined with SST. Sophisticated
confidence models, on the other hand, did not prove to yield any data selection
gain. Our results indicate that, while SST is crucial at the beginning of the
labeling process, its gains degrade rapidly as AL is set in place. The final
simulation reports that AL allows a transcription cost reduction of about 70%
over random selection. Alternatively, for a fixed transcription budget, the
proposed approach improves the word error rate by about 12.5% relative.
| 2,016 | Computation and Language |
Small-world networks for summarization of biomedical articles | In recent years, many methods have been developed to identify important
portions of text documents. Summarization tools can utilize these methods to
extract summaries from large volumes of textual information. However, to
identify concepts representing central ideas within a text document and to
extract the most informative sentences that best convey those concepts still
remain two crucial tasks in summarization methods. In this paper, we introduce
a graph-based method to address these two challenges in the context of
biomedical text summarization. We show that how a summarizer can discover
meaningful concepts within a biomedical text document using the Helmholtz
principle. The summarizer considers the meaningful concepts as the main topics
and constructs a graph based on the topics that the sentences share. The
summarizer can produce an informative summary by extracting those sentences
having higher values of the degree. We assess the performance of our method for
summarization of biomedical articles using the Recall-Oriented Understudy for
Gisting Evaluation (ROUGE) toolkit. The results show that the degree can be a
useful centrality measure to identify important sentences in this type of
graph-based modelling. Our method can improve the performance of biomedical
text summarization compared to some state-of-the-art and publicly available
summarizers. Combining a concept-based modelling strategy and a graph-based
approach to sentence extraction, our summarizer can produce summaries with the
highest scores of informativeness among the comparison methods. This research
work can be regarded as a start point to the study of small-world networks in
summarization of biomedical texts.
| 2,019 | Computation and Language |
Neural Language Modeling with Visual Features | Multimodal language models attempt to incorporate non-linguistic features for
the language modeling task. In this work, we extend a standard recurrent neural
network (RNN) language model with features derived from videos. We train our
models on data that is two orders-of-magnitude bigger than datasets used in
prior work. We perform a thorough exploration of model architectures for
combining visual and text features. Our experiments on two corpora (YouCookII
and 20bn-something-something-v2) show that the best performing architecture
consists of middle fusion of visual and text features, yielding over 25%
relative improvement in perplexity. We report analysis that provides insights
into why our multimodal language model improves upon a standard RNN language
model.
| 2,019 | Computation and Language |
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on UCCA parsing in English, German
and French, and discuss the participating systems and results. UCCA is a
cross-linguistically applicable framework for semantic representation, which
builds on extensive typological work and supports rapid annotation. UCCA poses
a challenge for existing parsing techniques, as it exhibits reentrancy
(resulting in DAG structures), discontinuous structures and non-terminal nodes
corresponding to complex semantic units. The shared task has yielded
improvements over the state-of-the-art baseline in all languages and settings.
Full results can be found in the task's website
\url{https://competitions.codalab.org/competitions/19160}.
| 2,020 | Computation and Language |
Option Comparison Network for Multiple-choice Reading Comprehension | Multiple-choice reading comprehension (MCRC) is the task of selecting the
correct answer from multiple options given a question and an article. Existing
MCRC models typically either read each option independently or compute a
fixed-length representation for each option before comparing them. However,
humans typically compare the options at multiple-granularity level before
reading the article in detail to make reasoning more efficient. Mimicking
humans, we propose an option comparison network (OCN) for MCRC which compares
options at word-level to better identify their correlations to help reasoning.
Specially, each option is encoded into a vector sequence using a skimmer to
retain fine-grained information as much as possible. An attention mechanism is
leveraged to compare these sequences vector-by-vector to identify more subtle
correlations between options, which is potentially valuable for reasoning.
Experimental results on the human English exam MCRC dataset RACE show that our
model outperforms existing methods significantly. Moreover, it is also the
first model that surpasses Amazon Mechanical Turker performance on the whole
dataset.
| 2,019 | Computation and Language |
Learning to Speak and Act in a Fantasy Text Adventure Game | We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully.
| 2,019 | Computation and Language |
Context-Aware Cross-Lingual Mapping | Cross-lingual word vectors are typically obtained by fitting an orthogonal
matrix that maps the entries of a bilingual dictionary from a source to a
target vector space. Word vectors, however, are most commonly used for sentence
or document-level representations that are calculated as the weighted average
of word embeddings. In this paper, we propose an alternative to word-level
mapping that better reflects sentence-level cross-lingual similarity. We
incorporate context in the transformation matrix by directly mapping the
averaged embeddings of aligned sentences in a parallel corpus. We also
implement cross-lingual mapping of deep contextualized word embeddings using
parallel sentences with word alignments. In our experiments, both approaches
resulted in cross-lingual sentence embeddings that outperformed
context-independent word mapping in sentence translation retrieval.
Furthermore, the sentence-level transformation could be used for word-level
mapping without loss in word translation quality.
| 2,019 | Computation and Language |
Neural Language Models as Psycholinguistic Subjects: Representations of
Syntactic State | We deploy the methods of controlled psycholinguistic experimentation to shed
light on the extent to which the behavior of neural network language models
reflects incremental representations of syntactic state. To do so, we examine
model behavior on artificial sentences containing a variety of syntactically
complex structures. We test four models: two publicly available LSTM sequence
models of English (Jozefowicz et al., 2016; Gulordava et al., 2018) trained on
large datasets; an RNNG (Dyer et al., 2016) trained on a small, parsed dataset;
and an LSTM trained on the same small corpus as the RNNG. We find evidence that
the LSTMs trained on large datasets represent syntactic state over large spans
of text in a way that is comparable to the RNNG, while the LSTM trained on the
small dataset does not or does so only weakly.
| 2,019 | Computation and Language |
Towards Time-Aware Distant Supervision for Relation Extraction | Distant supervision for relation extraction heavily suffers from the wrong
labeling problem. To alleviate this issue in news data with the timestamp, we
take a new factor time into consideration and propose a novel time-aware
distant supervision framework (Time-DS). Time-DS is composed of a time series
instance-popularity and two strategies. Instance-popularity is to encode the
strong relevance of time and true relation mention. Therefore,
instance-popularity would be an effective clue to reduce the noises generated
through distant supervision labeling. The two strategies, i.e., hard filter and
curriculum learning are both ways to implement instance-popularity for better
relation extraction in the manner of Time-DS. The curriculum learning is a more
sophisticated and flexible way to exploit instance-popularity to eliminate the
bad effects of noises, thus get better relation extraction performance.
Experiments on our collected multi-source news corpus show that Time-DS
achieves significant improvements for relation extraction.
| 2,019 | Computation and Language |
Filling Gender & Number Gaps in Neural Machine Translation with
Black-box Context Injection | When translating from a language that does not morphologically mark
information such as gender and number into a language that does, translation
systems must "guess" this missing information, often leading to incorrect
translations in the given context. We propose a black-box approach for
injecting the missing information to a pre-trained neural machine translation
system, allowing to control the morphological variations in the generated
translations without changing the underlying model or training data. We
evaluate our method on an English to Hebrew translation task, and show that it
is effective in injecting the gender and number information and that supplying
the correct information improves the translation accuracy in up to 2.3 BLEU on
a female-speaker test set for a state-of-the-art online black-box system.
Finally, we perform a fine-grained syntactic analysis of the generated
translations that shows the effectiveness of our method.
| 2,019 | Computation and Language |
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient
Conversations on Symptom Monitoring | Data for human-human spoken dialogues for research and development are
currently very limited in quantity, variety, and sources; such data are even
scarcer in healthcare. In this work, we investigate fast prototyping of a
dialogue comprehension system by leveraging on minimal nurse-to-patient
conversations. We propose a framework inspired by nurse-initiated clinical
symptom monitoring conversations to construct a simulated human-human dialogue
dataset, embodying linguistic characteristics of spoken interactions like
thinking aloud, self-contradiction, and topic drift. We then adopt an
established bidirectional attention pointer network on this simulated dataset,
achieving more than 80% F1 score on a held-out test set from real-world
nurse-to-patient conversations. The ability to automatically comprehend
conversations in the healthcare domain by exploiting only limited data has
implications for improving clinical workflows through red flag symptom
detection and triaging capabilities. We demonstrate the feasibility for
efficient and effective extraction, retrieval and comprehension of symptom
checking information discussed in multi-turn human-human spoken conversations.
| 2,019 | Computation and Language |
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases
in Word Embeddings But do not Remove Them | Word embeddings are widely used in NLP for a vast range of tasks. It was
shown that word embeddings derived from text corpora reflect gender biases in
society. This phenomenon is pervasive and consistent across different word
embedding models, causing serious concern. Several recent works tackle this
problem, and propose methods for significantly reducing this gender bias in
word embeddings, demonstrating convincing results. However, we argue that this
removal is superficial. While the bias is indeed substantially reduced
according to the provided bias definition, the actual effect is mostly hiding
the bias, not removing it. The gender bias information is still reflected in
the distances between "gender-neutralized" words in the debiased embeddings,
and can be recovered from them. We present a series of experiments to support
this claim, for two debiasing methods. We conclude that existing bias removal
techniques are insufficient, and should not be trusted for providing
gender-neutral modeling.
| 2,019 | Computation and Language |
Named Entity Recognition for Electronic Health Records: A Comparison of
Rule-based and Machine Learning Approaches | This work investigates multiple approaches to Named Entity Recognition (NER)
for text in Electronic Health Record (EHR) data. In particular, we look into
the application of (i) rule-based, (ii) deep learning and (iii) transfer
learning systems for the task of NER on brain imaging reports with a focus on
records from patients with stroke. We explore the strengths and weaknesses of
each approach, develop rules and train on a common dataset, and evaluate each
system's performance on common test sets of Scottish radiology reports from two
sources (brain imaging reports in ESS -- Edinburgh Stroke Study data collected
by NHS Lothian as well as radiology reports created in NHS Tayside). Our
comparison shows that a hand-crafted system is the most accurate way to
automatically label EHR, but machine learning approaches can provide a feasible
alternative where resources for a manual system are not readily available.
| 2,019 | Computation and Language |
Efficiently Reusing Natural Language Processing Models for
Phenotype-Mention Identification in Free-text Electronic Medical Records:
Methodology Study | Background: Many efforts have been put into the use of automated approaches,
such as natural language processing (NLP), to mine or extract data from
free-text medical records to construct comprehensive patient profiles for
delivering better health-care. Reusing NLP models in new settings, however,
remains cumbersome - requiring validation and/or retraining on new data
iteratively to achieve convergent results.
Objective: The aim of this work is to minimize the effort involved in reusing
NLP models on free-text medical records.
Methods: We formally define and analyse the model adaptation problem in
phenotype-mention identification tasks. We identify "duplicate waste" and
"imbalance waste", which collectively impede efficient model reuse. We propose
a phenotype embedding based approach to minimize these sources of waste without
the need for labelled data from new settings.
Results: We conduct experiments on data from a large mental health registry
to reuse NLP models in four phenotype-mention identification tasks. The
proposed approach can choose the best model for a new task, identifying up to
76% (duplicate waste), i.e. phenotype mentions without the need for validation
and model retraining, and with very good performance (93-97% accuracy). It can
also provide guidance for validating and retraining the selected model for
novel language patterns in new tasks, saving around 80% (imbalance waste), i.e.
the effort required in "blind" model-adaptation approaches.
Conclusions: Adapting pre-trained NLP models for new tasks can be more
efficient and effective if the language pattern landscapes of old settings and
new settings can be made explicit and comparable. Our experiments show that the
phenotype-mention embedding approach is an effective way to model language
patterns for phenotype-mention identification tasks and that its use can guide
efficient NLP model reuse.
| 2,019 | Computation and Language |
An Innovative Word Encoding Method For Text Classification Using
Convolutional Neural Network | Text classification plays a vital role today especially with the intensive
use of social networking media. Recently, different architectures of
convolutional neural networks have been used for text classification in which
one-hot vector, and word embedding methods are commonly used. This paper
presents a new language independent word encoding method for text
classification. The proposed model converts raw text data to low-level feature
dimension with minimal or no preprocessing steps by using a new approach called
binary unique number of word "BUNOW". BUNOW allows each unique word to have an
integer ID in a dictionary that is represented as a k-dimensional vector of its
binary equivalent. The output vector of this encoding is fed into a
convolutional neural network (CNN) model for classification. Moreover, the
proposed model reduces the neural network parameters, allows faster computation
with few network layers, where a word is atomic representation the document as
in word level, and decrease memory consumption for character level
representation. The provided CNN model is able to work with other languages or
multi-lingual text without the need for any changes in the encoding method. The
model outperforms the character level and very deep character level CNNs models
in terms of accuracy, network parameters, and memory consumption; the results
show total classification accuracy 91.99% and error 8.01% using AG's News
dataset compared to the state of art methods that have total classification
accuracy 91.45% and error 8.55%, in addition to the reduction in input feature
vector and neural network parameters by 62% and 34%, respectively.
| 2,019 | Computation and Language |
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree
Parsing | This paper describes a simple UCCA semantic graph parsing approach. The key
idea is to convert a UCCA semantic graph into a constituent tree, in which
extra labels are deliberately designed to mark remote edges and discontinuous
nodes for future recovery. In this way, we can make use of existing syntactic
parsing techniques. Based on the data statistics, we recover discontinuous
nodes directly according to the output labels of the constituent parser and use
a biaffine classification model to recover the more complex remote edges. The
classification model and the constituent parser are simultaneously trained
under the multi-task learning framework. We use the multilingual BERT as extra
features in the open tracks. Our system ranks the first place in the six
English/German closed/open tracks among seven participating systems. For the
seventh cross-lingual track, where there is little training data for French, we
propose a language embedding approach to utilize English and German training
data, and our result ranks the second place.
| 2,019 | Computation and Language |
Partially Shuffling the Training Data to Improve Language Models | Although SGD requires shuffling the training data between epochs, currently
none of the word-level language modeling systems do this. Naively shuffling all
sentences in the training data would not permit the model to learn
inter-sentence dependencies. Here we present a method that partially shuffles
the training data between epochs. This method makes each batch random, while
keeping most sentence ordering intact. It achieves new state of the art results
on word-level language modeling on both the Penn Treebank and WikiText-2
datasets.
| 2,019 | Computation and Language |
Toward Fast and Accurate Neural Chinese Word Segmentation with
Multi-Criteria Learning | The ambiguous annotation criteria lead to divergence of Chinese Word
Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese
word segmentation aims to capture various annotation criteria among datasets
and leverage their common underlying knowledge. In this paper, we propose a
domain adaptive segmenter to exploit diverse criteria of various datasets. Our
model is based on Bidirectional Encoder Representations from Transformers
(BERT), which is responsible for introducing open-domain knowledge. Private and
shared projection layers are proposed to capture domain-specific knowledge and
common knowledge, respectively. We also optimize computational efficiency via
distillation, quantization, and compiler optimization. Experiments show that
our segmenter outperforms the previous state of the art (SOTA) models on 10 CWS
datasets with superior efficiency.
| 2,020 | Computation and Language |
ETNLP: a visual-aided systematic approach to select pre-trained
embeddings for a downstream task | Given many recent advanced embedding models, selecting pre-trained word
embedding (a.k.a., word representation) models best fit for a specific
downstream task is non-trivial. In this paper, we propose a systematic
approach, called ETNLP, for extracting, evaluating, and visualizing multiple
sets of pre-trained word embeddings to determine which embeddings should be
used in a downstream task. For extraction, we provide a method to extract
subsets of the embeddings to be used in the downstream task. For evaluation, we
analyse the quality of pre-trained embeddings using an input word analogy list.
Finally, we visualize the word representations in the embedding space to
explore the embedded words interactively.
We demonstrate the effectiveness of the proposed approach on our pre-trained
word embedding models in Vietnamese to select which models are suitable for a
named entity recognition (NER) task. Specifically, we create a large Vietnamese
word analogy list to evaluate and select the pre-trained embedding models for
the task. We then utilize the selected embeddings for the NER task and achieve
the new state-of-the-art results on the task benchmark dataset. We also apply
the approach to another downstream task of privacy-guaranteed embedding
selection, and show that it helps users quickly select the most suitable
embeddings. In addition, we create an open-source system using the proposed
systematic approach to facilitate similar studies on other NLP tasks. The
source code and data are available at https://github.com/vietnlp/etnlp.
| 2,019 | Computation and Language |
The Truth and Nothing but the Truth: Multimodal Analysis for Deception
Detection | We propose a data-driven method for automatic deception detection in
real-life trial data using visual and verbal cues. Using OpenFace with facial
action unit recognition, we analyze the movement of facial features of the
witness when posed with questions and the acoustic patterns using OpenSmile. We
then perform a lexical analysis on the spoken words, emphasizing the use of
pauses and utterance breaks, feeding that to a Support Vector Machine to test
deceit or truth prediction. We then try out a method to incorporate
utterance-based fusion of visual and lexical analysis, using string based
matching.
| 2,019 | Computation and Language |
Practical Semantic Parsing for Spoken Language Understanding | Executable semantic parsing is the task of converting natural language
utterances into logical forms that can be directly used as queries to get a
response. We build a transfer learning framework for executable semantic
parsing. We show that the framework is effective for Question Answering (Q&A)
as well as for Spoken Language Understanding (SLU). We further investigate the
case where a parser on a new domain can be learned by exploiting data on other
domains, either via multi-task learning between the target domain and an
auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on
the target domain. With either flavor of transfer learning, we are able to
improve performance on most domains; we experiment with public data sets such
as Overnight and NLmaps as well as with commercial SLU data. The experiments
carried out on data sets that are different in nature show how executable
semantic parsing can unify different areas of NLP such as Q&A and SLU.
| 2,019 | Computation and Language |
Context-Aware Learning for Neural Machine Translation | Interest in larger-context neural machine translation, including
document-level and multi-modal translation, has been growing. Multiple works
have proposed new network architectures or evaluation schemes, but potentially
helpful context is still sometimes ignored by larger-context translation
models. In this paper, we propose a novel learning algorithm that explicitly
encourages a neural translation model to take into account additional context
using a multilevel pair-wise ranking loss. We evaluate the proposed learning
algorithm with a transformer-based larger-context translation system on
document-level translation. By comparing performance using actual and random
contexts, we show that a model trained with the proposed algorithm is more
sensitive to the additional context.
| 2,019 | Computation and Language |
Syllable-based Neural Named Entity Recognition for Myanmar Language | Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar
natural language processing research work. In this work, NER for Myanmar
language is treated as a sequence tagging problem and the effectiveness of deep
neural networks on NER for Myanmar language has been investigated. Experiments
are performed by applying deep neural network architectures on syllable level
Myanmar contexts. Very first manually annotated NER corpus for Myanmar language
is also constructed and proposed. In developing our in-house NER corpus,
sentences from online news website and also sentences supported from
ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian
Language Treebank (ALT) project under ASEAN IVO. This paper contributes the
first evaluation of neural network models on NER task for Myanmar language. The
experimental results show that those neural sequence models can produce
promising results compared to the baseline CRF model. Among those neural
architectures, bidirectional LSTM network added CRF layer above gives the
highest F-score value. This work also aims to discover the effectiveness of
neural network approaches to Myanmar textual processing as well as to promote
further researches on this understudied language.
| 2,019 | Computation and Language |
Few-Shot and Zero-Shot Learning for Historical Text Normalization | Historical text normalization often relies on small training datasets. Recent
work has shown that multi-task learning can lead to significant improvements by
exploiting synergies with related datasets, but there has been no systematic
study of different multi-task learning architectures. This paper evaluates
63~multi-task learning configurations for sequence-to-sequence-based historical
text normalization across ten datasets from eight languages, using
autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary
tasks. We observe consistent, significant improvements across languages when
training data for the target task is limited, but minimal or no improvements
when training data is abundant. We also show that zero-shot learning
outperforms the simple, but relatively strong, identity baseline.
| 2,019 | Computation and Language |
Character Eyes: Seeing Language through Character-Level Taggers | Character-level models have been used extensively in recent years in NLP
tasks as both supplements and replacements for closed-vocabulary token-level
word representations. In one popular architecture, character-level LSTMs are
used to feed token representations into a sequence tagger predicting
token-level annotations such as part-of-speech (POS) tags. In this work, we
examine the behavior of POS taggers across languages from the perspective of
individual hidden units within the character LSTM. We aggregate the behavior of
these units into language-level metrics which quantify the challenges that
taggers face on languages with different morphological properties, and identify
links between synthesis and affixation preference and emergent behavior of the
hidden tagger layer. In a comparative experiment, we show how modifying the
balance between forward and backward hidden units affects model arrangement and
performance in these types of languages.
| 2,019 | Computation and Language |
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation | We present a novel approach to dialogue state tracking and referring
expression resolution tasks. Successful contextual understanding of multi-turn
spoken dialogues requires resolving referring expressions across turns and
tracking the entities relevant to the conversation across turns. Tracking
conversational state is particularly challenging in a multi-domain scenario
when there exist multiple spoken language understanding (SLU) sub-systems, and
each SLU sub-system operates on its domain-specific meaning representation.
While previous approaches have addressed the disparate schema issue by learning
candidate transformations of the meaning representation, in this paper, we
instead model the reference resolution as a dialogue context-aware user query
reformulation task -- the dialog state is serialized to a sequence of natural
language tokens representing the conversation. We develop our model for query
reformulation using a pointer-generator network and a novel multi-task learning
setup. In our experiments, we show a significant improvement in absolute F1 on
an internal as well as a, soon to be released, public benchmark respectively.
| 2,019 | Computation and Language |
Topological Analysis of Syntactic Structures | We use the persistent homology method of topological data analysis and
dimensional analysis techniques to study data of syntactic structures of world
languages. We analyze relations between syntactic parameters in terms of
dimensionality, of hierarchical clustering structures, and of non-trivial
loops. We show there are relations that hold across language families and
additional relations that are family-specific. We then analyze the trees
describing the merging structure of persistent connected components for
languages in different language families and we show that they partly correlate
to historical phylogenetic trees but with significant differences. We also show
the existence of interesting non-trivial persistent first homology groups in
various language families. We give examples where explicit generators for the
persistent first homology can be identified, some of which appear to correspond
to homoplasy phenomena, while others may have an explanation in terms of
historical linguistics, corresponding to known cases of syntactic borrowing
across different language subfamilies.
| 2,019 | Computation and Language |
"Hang in There": Lexical and Visual Analysis to Identify Posts
Warranting Empathetic Responses | In the past few years, social media has risen as a platform where people
express and share personal incidences about abuse, violence and mental health
issues. There is a need to pinpoint such posts and learn the kind of response
expected. For this purpose, we understand the sentiment that a personal story
elicits on different posts present on different social media sites, on the
topics of abuse or mental health. In this paper, we propose a method supported
by hand-crafted features to judge if the post requires an empathetic response.
The model is trained upon posts from various web-pages and corresponding
comments, on both the captions and the images. We were able to obtain 80%
accuracy in tagging posts requiring empathetic responses.
| 2,019 | Computation and Language |
Bootstrapping Method for Developing Part-of-Speech Tagged Corpus in Low
Resource Languages Tagset - A Focus on an African Igbo | Most languages, especially in Africa, have fewer or no established
part-of-speech (POS) tagged corpus. However, POS tagged corpus is essential for
natural language processing (NLP) to support advanced researches such as
machine translation, speech recognition, etc. Even in cases where there is no
POS tagged corpus, there are some languages for which parallel texts are
available online. The task of POS tagging a new language corpus with a new
tagset usually face a bootstrapping problem at the initial stages of the
annotation process. The unavailability of automatic taggers to help the human
annotator makes the annotation process to appear infeasible to quickly produce
adequate amounts of POS tagged corpus for advanced NLP research and training
the taggers. In this paper, we demonstrate the efficacy of a POS annotation
method that employed the services of two automatic approaches to assist POS
tagged corpus creation for a novel language in NLP. The two approaches are
cross-lingual and monolingual POS tags projection. We used cross-lingual to
automatically create an initial 'errorful' tagged corpus for a target language
via word-alignment. The resources for creating this are derived from a source
language rich in NLP resources. A monolingual method is applied to clean the
induce noise via an alignment process and to transform the source language tags
to the target language tags. We used English and Igbo as our case study. This
is possible because there are parallel texts that exist between English and
Igbo, and the source language English has available NLP resources. The results
of the experiment show a steady improvement in accuracy and rate of tags
transformation with score ranges of 6.13% to 83.79% and 8.67% to 98.37%
respectively. The rate of tags transformation evaluates the rate at which
source language tags are translated to target language tags.
| 2,019 | Computation and Language |
Syntax-aware Neural Semantic Role Labeling with Supertags | We introduce a new syntax-aware model for dependency-based semantic role
labeling that outperforms syntax-agnostic models for English and Spanish. We
use a BiLSTM to tag the text with supertags extracted from dependency parses,
and we feed these supertags, along with words and parts of speech, into a deep
highway BiLSTM for semantic role labeling. Our model combines the strengths of
earlier models that performed SRL on the basis of a full dependency parse with
more recent models that use no syntactic information at all. Our local and
non-ensemble model achieves state-of-the-art performance on the CoNLL 09
English and Spanish datasets. SRL models benefit from syntactic information,
and we show that supertagging is a simple, powerful, and robust way to
incorporate syntax into a neural SRL system.
| 2,019 | Computation and Language |
End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model | Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based
end-to-end models are widely used in speech recognition due to its simplicity
in training and efficiency in decoding. In conventional LSTM-CTC based models,
a bottleneck projection matrix maps the hidden feature vectors obtained from
LSTM to softmax output layer. In this paper, we propose to use a high rank
projection layer to replace the projection matrix. The output from the high
rank projection layer is a weighted combination of vectors that are projected
from the hidden feature vectors via different projection matrices and
non-linear activation function. The high rank projection layer is able to
improve the expressiveness of LSTM-CTC models. The experimental results show
that on Wall Street Journal (WSJ) corpus and LibriSpeech data set, the proposed
method achieves 4%-6% relative word error rate (WER) reduction over the
baseline CTC system. They outperform other published CTC based end-to-end (E2E)
models under the condition that no external data or data augmentation is
applied. Code has been made available at https://github.com/mobvoi/lstm_ctc.
| 2,019 | Computation and Language |
Offensive Language Analysis using Deep Learning Architecture | SemEval-2019 Task 6 (Zampieri et al., 2019b) requires us to identify and
categorise offensive language in social media. In this paper we will describe
the process we took to tackle this challenge. Our process is heavily inspired
by Sosa (2017) where he proposed CNN-LSTM and LSTM-CNN models to conduct
twitter sentiment analysis. We decided to follow his approach as well as
further his work by testing out different variations of RNN models with CNN.
Specifically, we have divided the challenge into two parts: data processing and
sampling and choosing the optimal deep learning architecture. In preprocessing,
we experimented with two techniques, SMOTE and Class Weights to counter the
imbalance between classes. Once we are happy with the quality of our input
data, we proceed to choosing the optimal deep learning architecture for this
task. Given the quality and quantity of data we have been given, we found that
the addition of CNN layer provides very little to no additional improvement to
our model's performance and sometimes even lead to a decrease in our F1-score.
In the end, the deep learning architecture that gives us the highest macro
F1-score is a simple BiLSTM-CNN.
| 2,019 | Computation and Language |
Sub-event detection from Twitter streams as a sequence labeling problem | This paper introduces improved methods for sub-event detection in social
media streams, by applying neural sequence models not only on the level of
individual posts, but also directly on the stream level. Current approaches to
identify sub-events within a given event, such as a goal during a soccer match,
essentially do not exploit the sequential nature of social media streams. We
address this shortcoming by framing the sub-event detection problem in social
media streams as a sequence labeling task and adopt a neural sequence
architecture that explicitly accounts for the chronological order of posts.
Specifically, we (i) establish a neural baseline that outperforms a graph-based
state-of-the-art method for binary sub-event detection (2.7% micro-F1
improvement), as well as (ii) demonstrate superiority of a recurrent neural
network model on the posts sequence level for labeled sub-events (2.4%
bin-level F1 improvement over non-sequential models).
| 2,019 | Computation and Language |
Market Trend Prediction using Sentiment Analysis: Lessons Learned and
Paths Forward | Financial market forecasting is one of the most attractive practical
applications of sentiment analysis. In this paper, we investigate the potential
of using sentiment \emph{attitudes} (positive vs negative) and also sentiment
\emph{emotions} (joy, sadness, etc.) extracted from financial news or tweets to
help predict stock price movements. Our extensive experiments using the
\emph{Granger-causality} test have revealed that (i) in general sentiment
attitudes do not seem to Granger-cause stock price changes; and (ii) while on
some specific occasions sentiment emotions do seem to Granger-cause stock price
changes, the exhibited pattern is not universal and must be looked at on a case
by case basis. Furthermore, it has been observed that at least for certain
stocks, integrating sentiment emotions as additional features into the machine
learning based market trend prediction model could improve its accuracy.
| 2,019 | Computation and Language |
Overview of the Ugglan Entity Discovery and Linking System | Ugglan is a system designed to discover named entities and link them to
unique identifiers in a knowledge base. It is based on a combination of a name
and nominal dictionary derived from Wikipedia and Wikidata, a named entity
recognition module (NER) using fixed ordinally-forgetting encoding (FOFE)
trained on the TAC EDL data from 2014-2016, a candidate generation module from
the Wikipedia link graph across multiple editions, a PageRank link and
cooccurrence graph disambiguator, and finally a reranker trained on the TAC EDL
2015-2016 data.
| 2,017 | Computation and Language |
Adversarial attacks against Fact Extraction and VERification | This paper describes a baseline for the second iteration of the Fact
Extraction and VERification shared task (FEVER2.0) which explores the
resilience of systems through adversarial evaluation. We present a collection
of simple adversarial attacks against systems that participated in the first
FEVER shared task. FEVER modeled the assessment of truthfulness of written
claims as a joint information retrieval and natural language inference task
using evidence from Wikipedia. A large number of participants made use of deep
neural networks in their submissions to the shared task. The extent as to
whether such models understand language has been the subject of a number of
recent investigations and discussion in literature. In this paper, we present a
simple method of generating entailment-preserving and entailment-altering
perturbations of instances by common patterns within the training data. We find
that a number of systems are greatly affected with absolute losses in
classification accuracy of up to $29\%$ on the newly perturbed instances. Using
these newly generated instances, we construct a sample submission for the
FEVER2.0 shared task. Addressing these types of attacks will aid in building
more robust fact-checking models, as well as suggest directions to expand the
datasets.
| 2,019 | Computation and Language |
Benchmarking Natural Language Understanding Services for building
Conversational Agents | We have recently seen the emergence of several publicly available Natural
Language Understanding (NLU) toolkits, which map user utterances to structured,
but more abstract, Dialogue Act (DA) or Intent specifications, while making
this process accessible to the lay developer. In this paper, we present the
first wide coverage evaluation and comparison of some of the most popular NLU
services, on a large, multi-domain (21 domains) dataset of 25K user utterances
that we have collected and annotated with Intent and Entity Type specifications
and which will be released as part of this submission. The results show that on
Intent classification Watson significantly outperforms the other platforms,
namely, Dialogflow, LUIS and Rasa; though these also perform well.
Interestingly, on Entity Type recognition, Watson performs significantly worse
due to its low Precision. Again, Dialogflow, LUIS and Rasa perform well on this
task.
| 2,019 | Computation and Language |
GASC: Genre-Aware Semantic Change for Ancient Greek | Word meaning changes over time, depending on linguistic and extra-linguistic
factors. Associating a word's correct meaning in its historical context is a
central challenge in diachronic research, and is relevant to a range of NLP
tasks, including information retrieval and semantic search in historical texts.
Bayesian models for semantic change have emerged as a powerful tool to address
this challenge, providing explicit and interpretable representations of
semantic change phenomena. However, while corpora typically come with rich
metadata, existing models are limited by their inability to exploit contextual
information (such as text genre) beyond the document time-stamp. This is
particularly critical in the case of ancient languages, where lack of data and
long diachronic span make it harder to draw a clear distinction between
polysemy (the fact that a word has several senses) and semantic change (the
process of acquiring, losing, or changing senses), and current systems perform
poorly on these languages. We develop GASC, a dynamic semantic change model
that leverages categorical metadata about the texts' genre to boost inference
and uncover the evolution of meanings in Ancient Greek corpora. In a new
evaluation framework, our model achieves improved predictive performance
compared to the state of the art.
| 2,020 | Computation and Language |
Low-Resource Syntactic Transfer with Unsupervised Source Reordering | We describe a cross-lingual transfer method for dependency parsing that takes
into account the problem of word order differences between source and target
languages. Our model only relies on the Bible, a considerably smaller parallel
data than the commonly used parallel data in transfer methods. We use the
concatenation of projected trees from the Bible corpus, and the gold-standard
treebanks in multiple source languages along with cross-lingual word
representations. We demonstrate that reordering the source treebanks before
training on them for a target language improves the accuracy of languages
outside the European language family. Our experiments on 68 treebanks (38
languages) in the Universal Dependencies corpus achieve a high accuracy for all
languages. Among them, our experiments on 16 treebanks of 12 non-European
languages achieve an average UAS absolute improvement of 3.3% over a
state-of-the-art method.
| 2,019 | Computation and Language |
Consistent Dialogue Generation with Self-supervised Feature Learning | Generating responses that are consistent with the dialogue context is one of
the central challenges in building engaging conversational agents. We
demonstrate that neural conversation models can be geared towards generating
consistent responses by maintaining certain features related to topics and
personas throughout the conversation. Past work has required external
supervision that exploits features such as user identities that are often
unavailable. In our approach, topic and persona feature extractors are trained
using a contrastive training scheme that utilizes the natural structure of
dialogue data. We further adopt a feature disentangling loss which, paired with
controllable response generation techniques, allows us to promote or demote
certain learned topics and persona features. Evaluation results demonstrate the
model's ability to capture meaningful topics and persona features. The
incorporation of the learned features brings significant improvement in terms
of the quality of generated responses on two dialogue datasets.
| 2,021 | Computation and Language |
Survey of Text-based Epidemic Intelligence: A Computational Linguistic
Perspective | Epidemic intelligence deals with the detection of disease outbreaks using
formal (such as hospital records) and informal sources (such as user-generated
text on the web) of information. In this survey, we discuss approaches for
epidemic intelligence that use textual datasets, referring to it as `text-based
epidemic intelligence'. We view past work in terms of two broad categories:
health mention classification (selecting relevant text from a large volume) and
health event detection (predicting epidemic events from a collection of
relevant text). The focus of our discussion is the underlying computational
linguistic techniques in the two categories. The survey also provides details
of the state-of-the-art in annotation techniques, resources and evaluation
strategies for epidemic intelligence.
| 2,019 | Computation and Language |
Deep Patent Landscaping Model Using Transformer and Graph Embedding | Patent landscaping is a method used for searching related patents during a
research and development (R&D) project. To avoid the risk of patent
infringement and to follow current trends in technology, patent landscaping is
a crucial task required during the early stages of an R&D project. As the
process of patent landscaping requires advanced resources and can be tedious,
the demand for automated patent landscaping has been gradually increasing.
However, a shortage of well-defined benchmark datasets and comparable models
makes it difficult to find related research studies. In this paper, we propose
an automated patent landscaping model based on deep learning. To analyze the
text of patents, the proposed model uses a modified transformer structure. To
analyze the metadata of patents, we propose a graph embedding method that uses
a diffusion graph called Diff2Vec. Furthermore, we introduce four benchmark
datasets for comparing related research studies in patent landscaping. The
datasets are produced by querying Google BigQuery, based on a search formula
from a Korean patent attorney. The obtained results indicate that the proposed
model and datasets can attain state-of-the-art performance, as compared with
current patent landscaping models.
| 2,019 | Computation and Language |
MirrorGAN: Learning Text-to-image Generation by Redescription | Generating an image from a given text description has two goals: visual
realism and semantic consistency. Although significant progress has been made
in generating high-quality and visually realistic images using generative
adversarial networks, guaranteeing semantic consistency between the text
description and visual content remains very challenging. In this paper, we
address this problem by proposing a novel global-local attentive and
semantic-preserving text-to-image-to-text framework called MirrorGAN. MirrorGAN
exploits the idea of learning text-to-image generation by redescription and
consists of three modules: a semantic text embedding module (STEM), a
global-local collaborative attentive module for cascaded image generation
(GLAM), and a semantic text regeneration and alignment module (STREAM). STEM
generates word- and sentence-level embeddings. GLAM has a cascaded architecture
for generating target images from coarse to fine scales, leveraging both local
word attention and global sentence attention to progressively enhance the
diversity and semantic consistency of the generated images. STREAM seeks to
regenerate the text description from the generated image, which semantically
aligns with the given text description. Thorough experiments on two public
benchmark datasets demonstrate the superiority of MirrorGAN over other
representative state-of-the-art methods.
| 2,019 | Computation and Language |
Interactive Concept Mining on Personal Data -- Bootstrapping Semantic
Services | Semantic services (e.g. Semantic Desktops) are still afflicted by a cold
start problem: in the beginning, the user's personal information sphere, i.e.
files, mails, bookmarks, etc., is not represented by the system. Information
extraction tools used to kick-start the system typically create 1:1
representations of the different information items. Higher level concepts, for
example found in file names, mail subjects or in the content body of these
items, are not extracted. Leaving these concepts out may lead to
underperformance, having to many of them (e.g. by making every found term a
concept) will clutter the arising knowledge graph with non-helpful relations.
In this paper, we present an interactive concept mining approach proposing
concept candidates gathered by exploiting given schemata of usual personal
information management applications and analysing the personal information
sphere using various metrics. To heed the subjective view of the user, a
graphical user interface allows to easily rank and give feedback on proposed
concept candidates, thus keeping only those actually considered relevant. A
prototypical implementation demonstrates major steps of our approach.
| 2,019 | Computation and Language |
Absit invidia verbo: Comparing Deep Learning methods for offensive
language | This document describes our approach to building an Offensive Language
Classifier. More specifically, the OffensEval 2019 competition required us to
build three classifiers with slightly different goals:
- Offensive language identification: would classify a tweet as offensive or
not.
- Automatic categorization of offense types: would recognize if the target of
the offense was an individual or not.
- Offense target identification: would identify the target of the offense
between an individual, group or other.
In this report, we will discuss the different architectures, algorithms and
pre-processing strategies we tried, together with a detailed description of the
designs of our final classifiers and the reasons we choose them over others.
We evaluated our classifiers on the official test set provided for the
OffenseEval 2019 competition, obtaining a macro-averaged F1-score of 0.7189 for
Task A, 0.6708 on Task B and 0.5442 on Task C.
| 2,019 | Computation and Language |
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse
Tasks | While most previous work has focused on different pretraining objectives and
architectures for transfer learning, we ask how to best adapt the pretrained
model to a given target task. We focus on the two most common forms of
adaptation, feature extraction (where the pretrained weights are frozen), and
directly fine-tuning the pretrained model. Our empirical results across diverse
NLP tasks with two state-of-the-art models show that the relative performance
of fine-tuning vs. feature extraction depends on the similarity of the
pretraining and target tasks. We explore possible explanations for this finding
and provide a set of adaptation guidelines for the NLP practitioner.
| 2,019 | Computation and Language |
Formality Style Transfer with Hybrid Textual Annotations | Formality style transformation is the task of modifying the formality of a
given sentence without changing its content. Its challenge is the lack of
large-scale sentence-aligned parallel data. In this paper, we propose an
omnivorous model that takes parallel data and formality-classified data jointly
to alleviate the data sparsity issue. We empirically demonstrate the
effectiveness of our approach by achieving the state-of-art performance on a
recently proposed benchmark dataset of formality transfer. Furthermore, our
model can be readily adapted to other unsupervised text style transfer tasks
like unsupervised sentiment transfer and achieve competitive results on three
widely recognized benchmarks.
| 2,019 | Computation and Language |
Studying the Inductive Biases of RNNs with Synthetic Variations of
Natural Languages | How do typological properties such as word order and morphological case
marking affect the ability of neural sequence models to acquire the syntax of a
language? Cross-linguistic comparisons of RNNs' syntactic performance (e.g., on
subject-verb agreement prediction) are complicated by the fact that any two
languages differ in multiple typological properties, as well as by differences
in training corpus. We propose a paradigm that addresses these issues: we
create synthetic versions of English, which differ from English in one or more
typological parameters, and generate corpora for those languages based on a
parsed English corpus. We report a series of experiments in which RNNs were
trained to predict agreement features for verbs in each of those synthetic
languages. Among other findings, (1) performance was higher in
subject-verb-object order (as in English) than in subject-object-verb order (as
in Japanese), suggesting that RNNs have a recency bias; (2) predicting
agreement with both subject and object (polypersonal agreement) improves over
predicting each separately, suggesting that underlying syntactic knowledge
transfers across the two tasks; and (3) overt morphological case makes
agreement prediction significantly easier, regardless of word order.
| 2,019 | Computation and Language |
Automatic assessment of spoken language proficiency of non-native
children | This paper describes technology developed to automatically grade Italian
students (ages 9-16) on their English and German spoken language proficiency.
The students' spoken answers are first transcribed by an automatic speech
recognition (ASR) system and then scored using a feedforward neural network
(NN) that processes features extracted from the automatic transcriptions.
In-domain acoustic models, employing deep neural networks (DNNs), are derived
by adapting the parameters of an original out of domain DNN.
| 2,019 | Computation and Language |
A Context-Aware Citation Recommendation Model with BERT and Graph
Convolutional Networks | With the tremendous growth in the number of scientific papers being
published, searching for references while writing a scientific paper is a
time-consuming process. A technique that could add a reference citation at the
appropriate place in a sentence will be beneficial. In this perspective,
context-aware citation recommendation has been researched upon for around two
decades. Many researchers have utilized the text data called the context
sentence, which surrounds the citation tag, and the metadata of the target
paper to find the appropriate cited research. However, the lack of
well-organized benchmarking datasets and no model that can attain high
performance has made the research difficult.
In this paper, we propose a deep learning based model and well-organized
dataset for context-aware paper citation recommendation. Our model comprises a
document encoder and a context encoder, which uses Graph Convolutional Networks
(GCN) layer and Bidirectional Encoder Representations from Transformers (BERT),
which is a pre-trained model of textual data. By modifying the related PeerRead
dataset, we propose a new dataset called FullTextPeerRead containing context
sentences to cited references and paper metadata. To the best of our knowledge,
This dataset is the first well-organized dataset for context-aware paper
recommendation. The results indicate that the proposed model with the proposed
datasets can attain state-of-the-art performance and achieve a more than 28%
improvement in mean average precision (MAP) and recall@k.
| 2,019 | Computation and Language |
Content Differences in Syntactic and Semantic Representations | Syntactic analysis plays an important role in semantic parsing, but the
nature of this role remains a topic of ongoing debate. The debate has been
constrained by the scarcity of empirical comparative studies between syntactic
and semantic schemes, which hinders the development of parsing methods informed
by the details of target schemes and constructions. We target this gap, and
take Universal Dependencies (UD) and UCCA as a test case. After abstracting
away from differences of convention or formalism, we find that most content
divergences can be ascribed to: (1) UCCA's distinction between a Scene and a
non-Scene; (2) UCCA's distinction between primary relations, secondary ones and
participants; (3) different treatment of multi-word expressions, and (4)
different treatment of inter-clause linkage. We further discuss the long tail
of cases where the two schemes take markedly different approaches. Finally, we
show that the proposed comparison methodology can be used for fine-grained
evaluation of UCCA parsing, highlighting both challenges and potential sources
for improvement. The substantial differences between the schemes suggest that
semantic parsers are likely to benefit downstream text understanding
applications beyond their syntactic counterparts.
| 2,019 | Computation and Language |
Matching Entities Across Different Knowledge Graphs with Graph
Embeddings | This paper explores the problem of matching entities across different
knowledge graphs. Given a query entity in one knowledge graph, we wish to find
the corresponding real-world entity in another knowledge graph. We formalize
this problem and present two large-scale datasets for this task based on
exiting cross-ontology links between DBpedia and Wikidata, focused on several
hundred thousand ambiguous entities. Using a classification-based approach, we
find that a simple multi-layered perceptron based on representations derived
from RDF2Vec graph embeddings of entities in each knowledge graph is sufficient
to achieve high accuracy, with only small amounts of training data. The
contributions of our work are datasets for examining this problem and strong
baselines on which future work can be based.
| 2,019 | Computation and Language |
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence
Models | Adversarial examples --- perturbations to the input of a model that elicit
large changes in the output --- have been shown to be an effective way of
assessing the robustness of sequence-to-sequence (seq2seq) models. However,
these perturbations only indicate weaknesses in the model if they do not change
the input so significantly that it legitimately results in changes in the
expected output. This fact has largely been ignored in the evaluations of the
growing body of related literature. Using the example of untargeted attacks on
machine translation (MT), we propose a new evaluation framework for adversarial
attacks on seq2seq models that takes the semantic equivalence of the pre- and
post-perturbation input into account. Using this framework, we demonstrate that
existing methods may not preserve meaning in general, breaking the
aforementioned assumption that source side perturbations should not result in
changes in the expected output. We further use this framework to demonstrate
that adding additional constraints on attacks allows for adversarial
perturbations that are more meaning-preserving, but nonetheless largely change
the output sequence. Finally, we show that performing untargeted adversarial
training with meaning-preserving attacks is beneficial to the model in terms of
adversarial robustness, without hurting test performance. A toolkit
implementing our evaluation framework is released at
https://github.com/pmichel31415/teapot-nlp.
| 2,019 | Computation and Language |
A Machine Learning Approach to Comment Toxicity Classification | Now-a-days, derogatory comments are often made by one another, not only in
offline environment but also immensely in online environments like social
networking websites and online communities. So, an Identification combined with
Prevention System in all social networking websites and applications, including
all the communities, existing in the digital world is a necessity. In such a
system, the Identification Block should identify any negative online behaviour
and should signal the Prevention Block to take action accordingly. This study
aims to analyse any piece of text and detecting different types of toxicity
like obscenity, threats, insults and identity-based hatred. The labelled
Wikipedia Comment Dataset prepared by Jigsaw is used for the purpose. A
6-headed Machine Learning tf-idf Model has been made and trained separately,
yielding a Mean Validation Accuracy of 98.08% and Absolute Validation Accuracy
of 91.61%. Such an Automated System should be deployed for enhancing healthy
online conversation
| 2,019 | Computation and Language |
Emotion Action Detection and Emotion Inference: the Task and Dataset | Many Natural Language Processing works on emotion analysis only focus on
simple emotion classification without exploring the potentials of putting
emotion into "event context", and ignore the analysis of emotion-related
events. One main reason is the lack of this kind of corpus. Here we present
Cause-Emotion-Action Corpus, which manually annotates not only emotion, but
also cause events and action events. We propose two new tasks based on the
data-set: emotion causality and emotion inference. The first task is to extract
a triple (cause, emotion, action). The second task is to infer the probable
emotion. We are currently releasing the data-set with 10,603 samples and 15,892
events, basic statistic analysis and baseline on both emotion causality and
emotion inference tasks. Baseline performance demonstrates that there is much
room for both tasks to be improved.
| 2,019 | Computation and Language |
Improving Lemmatization of Non-Standard Languages with Joint Learning | Lemmatization of standard languages is concerned with (i) abstracting over
morphological differences and (ii) resolving token-lemma ambiguities of
inflected words in order to map them to a dictionary headword. In the present
paper we aim to improve lemmatization performance on a set of non-standard
historical languages in which the difficulty is increased by an additional
aspect (iii): spelling variation due to lacking orthographic standards. We
approach lemmatization as a string-transduction task with an encoder-decoder
architecture which we enrich with sentence context information using a
hierarchical sentence encoder. We show significant improvements over the
state-of-the-art when training the sentence encoder jointly for lemmatization
and language modeling. Crucially, our architecture does not require POS or
morphological annotations, which are not always available for historical
corpora. Additionally, we also test the proposed model on a set of
typologically diverse standard languages showing results on par or better than
a model without enhanced sentence representations and previous state-of-the-art
systems. Finally, to encourage future work on processing of non-standard
varieties, we release the dataset of non-standard languages underlying the
present study, based on openly accessible sources.
| 2,019 | Computation and Language |
Imbalanced multi-label classification using multi-task learning with
extractive summarization | Extractive summarization and imbalanced multi-label classification often
require vast amounts of training data to avoid overfitting. In situations where
training data is expensive to generate, leveraging information between tasks is
an attractive approach to increasing the amount of available information. This
paper employs multi-task training of an extractive summarizer and an RNN-based
classifier to improve summarization and classification accuracy by 50% and 75%,
respectively, relative to RNN baselines. We hypothesize that concatenating
sentence encodings based on document and class context increases
generalizability for highly variable corpuses.
| 2,019 | Computation and Language |
Audio De-identification: A New Entity Recognition Task | Named Entity Recognition (NER) has been mostly studied in the context of
written text. Specifically, NER is an important step in de-identification
(de-ID) of medical records, many of which are recorded conversations between a
patient and a doctor. In such recordings, audio spans with personal information
should be redacted, similar to the redaction of sensitive character spans in
de-ID for written text. The application of NER in the context of audio
de-identification has yet to be fully investigated. To this end, we define the
task of audio de-ID, in which audio spans with entity mentions should be
detected. We then present our pipeline for this task, which involves Automatic
Speech Recognition (ASR), NER on the transcript text, and text-to-audio
alignment. Finally, we introduce a novel metric for audio de-ID and a new
evaluation benchmark consisting of a large labeled segment of the Switchboard
and Fisher audio datasets and detail our pipeline's results on it.
| 2,019 | Computation and Language |
The Missing Ingredient in Zero-Shot Neural Machine Translation | Multilingual Neural Machine Translation (NMT) models are capable of
translating between multiple source and target languages. Despite various
approaches to train such models, they have difficulty with zero-shot
translation: translating between language pairs that were not together seen
during training. In this paper we first diagnose why state-of-the-art
multilingual NMT models that rely purely on parameter sharing, fail to
generalize to unseen language pairs. We then propose auxiliary losses on the
NMT encoder that impose representational invariance across languages. Our
simple approach vastly improves zero-shot translation quality without
regressing on supervised directions. For the first time, on WMT14
English-FrenchGerman, we achieve zero-shot performance that is on par with
pivoting. We also demonstrate the easy scalability of our approach to multiple
languages on the IWSLT 2017 shared task.
| 2,019 | Computation and Language |
Question Answering via Web Extracted Tables and Pipelined Models | In this paper, we describe a dataset and baseline result for a question
answering that utilizes web tables. It contains commonly asked questions on the
web and their corresponding answers found in tables on websites. Our dataset is
novel in that every question is paired with a table of a different signature.
In particular, the dataset contains two classes of tables: entity-instance
tables and the key-value tables. Each QA instance comprises a table of either
kind, a natural language question, and a corresponding structured SQL query. We
build our model by dividing question answering into several tasks, including
table retrieval and question element classification, and conduct experiments to
measure the performance of each task. We extract various features specific to
each task and compose a full pipeline which constructs the SQL query from its
parts. Our work provides qualitative results and error analysis for each task,
and identifies in detail the reasoning required to generate SQL expressions
from natural language questions. This analysis of reasoning informs future
models based on neural machine learning.
| 2,019 | Computation and Language |
Topic-Guided Variational Autoencoders for Text Generation | We propose a topic-guided variational autoencoder (TGVAE) model for text
generation. Distinct from existing variational autoencoder (VAE) based
approaches, which assume a simple Gaussian prior for the latent code, our model
specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural
topic module. Each mixture component corresponds to a latent topic, which
provides guidance to generate sentences under the topic. The neural topic
module and the VAE-based neural sequence module in our model are learned
jointly. In particular, a sequence of invertible Householder transformations is
applied to endow the approximate posterior of the latent code with high
flexibility during model inference. Experimental results show that our TGVAE
outperforms alternative approaches on both unconditional and conditional text
generation, which can generate semantically-meaningful sentences with various
topics.
| 2,019 | Computation and Language |
Technical notes: Syntax-aware Representation Learning With Pointer
Networks | This is a work-in-progress report, which aims to share preliminary results of
a novel sequence-to-sequence schema for dependency parsing that relies on a
combination of a BiLSTM and two Pointer Networks (Vinyals et al., 2015), in
which the final softmax function has been replaced with the logistic
regression. The two pointer networks co-operate to develop a latent syntactic
knowledge, by learning the lexical properties of "selection" and the lexical
properties of "selectability", respectively. At the moment and without
fine-tuning, the parser implementation gets a UAS of 93.14% on the English
Penn-treebank (Marcus et al., 1993) annotated with Stanford Dependencies: 2-3%
under the SOTA but yet attractive as a baseline of the approach.
| 2,019 | Computation and Language |
What You Say and How You Say it: Joint Modeling of Topics and Discourse
in Microblog Conversations | This paper presents an unsupervised framework for jointly modeling topic
content and discourse behavior in microblog conversations. Concretely, we
propose a neural model to discover word clusters indicating what a conversation
concerns (i.e., topics) and those reflecting how participants voice their
opinions (i.e., discourse). Extensive experiments show that our model can yield
both coherent topics and meaningful discourse behavior. Further study shows
that our topic and discourse representations can benefit the classification of
microblog messages, especially when they are jointly trained with the
classifier.
| 2,019 | Computation and Language |
Learning Hierarchical Discourse-level Structure for Fake News Detection | On the one hand, nowadays, fake news articles are easily propagated through
various online media platforms and have become a grand threat to the
trustworthiness of information. On the other hand, our understanding of the
language of fake news is still minimal. Incorporating hierarchical
discourse-level structure of fake and real news articles is one crucial step
toward a better understanding of how these articles are structured.
Nevertheless, this has rarely been investigated in the fake news detection
domain and faces tremendous challenges. First, existing methods for capturing
discourse-level structure rely on annotated corpora which are not available for
fake news datasets. Second, how to extract out useful information from such
discovered structures is another challenge. To address these challenges, we
propose Hierarchical Discourse-level Structure for Fake news detection. HDSF
learns and constructs a discourse-level structure for fake/real news articles
in an automated and data-driven manner. Moreover, we identify insightful
structure-related properties, which can explain the discovered structures and
boost our understating of fake news. Conducted experiments show the
effectiveness of the proposed approach. Further structural analysis suggests
that real and fake news present substantial differences in the hierarchical
discourse-level structures.
| 2,019 | Computation and Language |
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