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ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via
Lexically-constrained Neural Machine Translation | We present ParaBank, a large-scale English paraphrase dataset that surpasses
prior work in both quantity and quality. Following the approach of ParaNMT, we
train a Czech-English neural machine translation (NMT) system to generate novel
paraphrases of English reference sentences. By adding lexical constraints to
the NMT decoding procedure, however, we are able to produce multiple
high-quality sentential paraphrases per source sentence, yielding an English
paraphrase resource with more than 4 billion generated tokens and exhibiting
greater lexical diversity. Using human judgments, we also demonstrate that
ParaBank's paraphrases improve over ParaNMT on both semantic similarity and
fluency. Finally, we use ParaBank to train a monolingual NMT model with the
same support for lexically-constrained decoding for sentence rewriting tasks.
| 2,019 | Computation and Language |
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in
Natural Language Inference | Quantitative reasoning is a higher-order reasoning skill that any intelligent
natural language understanding system can reasonably be expected to handle. We
present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual
Entailment), a new framework for quantitative reasoning in textual entailment.
We benchmark the performance of 9 published NLI models on EQUATE, and find that
on average, state-of-the-art methods do not achieve an absolute improvement
over a majority-class baseline, suggesting that they do not implicitly learn to
reason with quantities. We establish a new baseline Q-REAS that manipulates
quantities symbolically. In comparison to the best performing NLI model, it
achieves success on numerical reasoning tests (+24.2%), but has limited verbal
reasoning capabilities (-8.1%). We hope our evaluation framework will support
the development of models of quantitative reasoning in language understanding.
| 2,019 | Computation and Language |
Semi-interactive Attention Network for Answer Understanding in
Reverse-QA | Question answering (QA) is an important natural language processing (NLP)
task and has received much attention in academic research and industry
communities. Existing QA studies assume that questions are raised by humans and
answers are generated by machines. Nevertheless, in many real applications,
machines are also required to determine human needs or perceive human states.
In such scenarios, machines may proactively raise questions and humans supply
answers. Subsequently, machines should attempt to understand the true meaning
of these answers. This new QA approach is called reverse-QA (rQA) throughout
this paper. In this work, the human answer understanding problem is
investigated and solved by classifying the answers into predefined answer-label
categories (e.g., True, False, Uncertain). To explore the relationships between
questions and answers, we use the interactive attention network (IAN) model and
propose an improved structure called semi-interactive attention network
(Semi-IAN). Two Chinese data sets for rQA are compiled. We evaluate several
conventional text classification models for comparison, and experimental
results indicate the promising performance of our proposed models.
| 2,019 | Computation and Language |
What comes next? Extractive summarization by next-sentence prediction | Existing approaches to automatic summarization assume that a length limit for
the summary is given, and view content selection as an optimization problem to
maximize informativeness and minimize redundancy within this budget. This
framework ignores the fact that human-written summaries have rich internal
structure which can be exploited to train a summarization system. We present
NEXTSUM, a novel approach to summarization based on a model that predicts the
next sentence to include in the summary using not only the source article, but
also the summary produced so far. We show that such a model successfully
captures summary-specific discourse moves, and leads to better content
selection performance, in addition to automatically predicting how long the
target summary should be. We perform experiments on the New York Times
Annotated Corpus of summaries, where NEXTSUM outperforms lead and content-model
summarization baselines by significant margins. We also show that the lengths
of summaries produced by our system correlates with the lengths of the
human-written gold standards.
| 2,019 | Computation and Language |
HAS-QA: Hierarchical Answer Spans Model for Open-domain Question
Answering | This paper is concerned with open-domain question answering (i.e., OpenQA).
Recently, some works have viewed this problem as a reading comprehension (RC)
task, and directly applied successful RC models to it. However, the
performances of such models are not so good as that in the RC task. In our
opinion, the perspective of RC ignores three characteristics in OpenQA task: 1)
many paragraphs without the answer span are included in the data collection; 2)
multiple answer spans may exist within one given paragraph; 3) the end position
of an answer span is dependent with the start position. In this paper, we first
propose a new probabilistic formulation of OpenQA, based on a three-level
hierarchical structure, i.e.,~the question level, the paragraph level and the
answer span level. Then a Hierarchical Answer Spans Model (HAS-QA) is designed
to capture each probability. HAS-QA has the ability to tackle the above three
problems, and experiments on public OpenQA datasets show that it significantly
outperforms traditional RC baselines and recent OpenQA baselines.
| 2,019 | Computation and Language |
A Speech Act Classifier for Persian Texts and its Application in
Identifying Rumors | Speech Acts (SAs) are one of the important areas of pragmatics, which give us
a better understanding of the state of mind of the people and convey an
intended language function. Knowledge of the SA of a text can be helpful in
analyzing that text in natural language processing applications. This study
presents a dictionary-based statistical technique for Persian SA recognition.
The proposed technique classifies a text into seven classes of SA based on four
criteria: lexical, syntactic, semantic, and surface features. WordNet as the
tool for extracting synonym and enriching features dictionary is utilized. To
evaluate the proposed technique, we utilized four classification methods
including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB),
and K-Nearest Neighbors (KNN). The experimental results demonstrate that the
proposed method using RF and SVM as the best classifiers achieved a
state-of-the-art performance with an accuracy of 0.95 for classification of
Persian SAs. Our original vision of this work is introducing an application of
SA recognition on social media content, especially the common SA in rumors.
Therefore, the proposed system utilized to determine the common SAs in rumors.
The results showed that Persian rumors are often expressed in three SA classes
including narrative, question, and threat, and in some cases with the request
SA.
| 2,020 | Computation and Language |
Unsupervised Neural Machine Translation with SMT as Posterior
Regularization | Without real bilingual corpus available, unsupervised Neural Machine
Translation (NMT) typically requires pseudo parallel data generated with the
back-translation method for the model training. However, due to weak
supervision, the pseudo data inevitably contain noises and errors that will be
accumulated and reinforced in the subsequent training process, leading to bad
translation performance. To address this issue, we introduce phrase based
Statistic Machine Translation (SMT) models which are robust to noisy data, as
posterior regularizations to guide the training of unsupervised NMT models in
the iterative back-translation process. Our method starts from SMT models built
with pre-trained language models and word-level translation tables inferred
from cross-lingual embeddings. Then SMT and NMT models are optimized jointly
and boost each other incrementally in a unified EM framework. In this way, (1)
the negative effect caused by errors in the iterative back-translation process
can be alleviated timely by SMT filtering noises from its phrase tables;
meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in
SMT. Experiments conducted on en-fr and en-de translation tasks show that our
method outperforms the strong baseline and achieves new state-of-the-art
unsupervised machine translation performance.
| 2,019 | Computation and Language |
Image Based Review Text Generation with Emotional Guidance | In the current field of computer vision, automatically generating texts from
given images has been a fully worked technique. Up till now, most works of this
area focus on image content describing, namely image-captioning. However, rare
researches focus on generating product review texts, which is ubiquitous in the
online shopping malls and is crucial for online shopping selection and
evaluation. Different from content describing, review texts include more
subjective information of customers, which may bring difference to the results.
Therefore, we aimed at a new field concerning generating review text from
customers based on images together with the ratings of online shopping
products, which appear as non-image attributes. We made several adjustments to
the existing image-captioning model to fit our task, in which we should also
take non-image features into consideration. We also did experiments based on
our model and get effective primary results.
| 2,019 | Computation and Language |
Quantum-theoretic Modeling in Computer Science A complex Hilbert space
model for entangled concepts in corpuses of documents | We work out a quantum-theoretic model in complex Hilbert space of a recently
performed test on co-occurrencies of two concepts and their combination in
retrieval processes on specific corpuses of documents. The test violated the
Clauser-Horne-Shimony-Holt version of the Bell inequalities ('CHSH
inequality'), thus indicating the presence of entanglement between the combined
concepts. We make use of a recently elaborated 'entanglement scheme' and
represent the collected data in the tensor product of Hilbert spaces of the
individual concepts, showing that the identified violation is due to the
occurrence of a strong form of entanglement, involving both states and
measurements and reflecting the meaning connection between the component
concepts. These results provide a significant confirmation of the presence of
quantum structures in corpuses of documents, like it is the case for the
entanglement identified in human cognition.
| 2,021 | Computation and Language |
Towards Using Context-Dependent Symbols in CTC Without State-Tying
Decision Trees | Deep neural acoustic models benefit from context-dependent (CD) modeling of
output symbols. We consider direct training of CTC networks with CD outputs,
and identify two issues. The first one is frame-level normalization of
probabilities in CTC, which induces strong language modeling behavior that
leads to overfitting and interference with external language models. The second
one is poor generalization in the presence of numerous lexical units like
triphones or tri-chars. We mitigate the former with utterance-level
normalization of probabilities. The latter typically requires reducing the CD
symbol inventory with state-tying decision trees, which have to be transferred
from classical GMM-HMM systems. We replace the trees with a CD symbol embedding
network, which saves parameters and ensures generalization to unseen and
undersampled CD symbols. The embedding network is trained together with the
rest of the acoustic model and removes one of the last cases in which neural
systems have to be bootstrapped from GMM-HMM ones.
| 2,019 | Computation and Language |
Human few-shot learning of compositional instructions | People learn in fast and flexible ways that have not been emulated by
machines. Once a person learns a new verb "dax," he or she can effortlessly
understand how to "dax twice," "walk and dax," or "dax vigorously." There have
been striking recent improvements in machine learning for natural language
processing, yet the best algorithms require vast amounts of experience and
struggle to generalize new concepts in compositional ways. To better understand
these distinctively human abilities, we study the compositional skills of
people through language-like instruction learning tasks. Our results show that
people can learn and use novel functional concepts from very few examples
(few-shot learning), successfully applying familiar functions to novel inputs.
People can also compose concepts in complex ways that go beyond the provided
demonstrations. Two additional experiments examined the assumptions and
inductive biases that people make when solving these tasks, revealing three
biases: mutual exclusivity, one-to-one mappings, and iconic concatenation. We
discuss the implications for cognitive modeling and the potential for building
machines with more human-like language learning capabilities.
| 2,019 | Computation and Language |
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue | End-to-end task-oriented dialogue is challenging since knowledge bases are
usually large, dynamic and hard to incorporate into a learning framework. We
propose the global-to-local memory pointer (GLMP) networks to address this
issue. In our model, a global memory encoder and a local memory decoder are
proposed to share external knowledge. The encoder encodes dialogue history,
modifies global contextual representation, and generates a global memory
pointer. The decoder first generates a sketch response with unfilled slots.
Next, it passes the global memory pointer to filter the external knowledge for
relevant information, then instantiates the slots via the local memory
pointers. We empirically show that our model can improve copy accuracy and
mitigate the common out-of-vocabulary problem. As a result, GLMP is able to
improve over the previous state-of-the-art models in both simulated bAbI
Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on
automatic and human evaluation.
| 2,019 | Computation and Language |
A Tweet Dataset Annotated for Named Entity Recognition and Stance
Detection | Annotated datasets in different domains are critical for many supervised
learning-based solutions to related problems and for the evaluation of the
proposed solutions. Topics in natural language processing (NLP) similarly
require annotated datasets to be used for such purposes. In this paper, we
target at two NLP problems, named entity recognition and stance detection, and
present the details of a tweet dataset in Turkish annotated for named entity
and stance information. Within the course of the current study, both the named
entity and stance annotations of the included tweets are made publicly
available, although previously the dataset has been publicly shared with stance
annotations only. We believe that this dataset will be useful for uncovering
the possible relationships between named entity recognition and stance
detection in tweets.
| 2,019 | Computation and Language |
Exploiting Synchronized Lyrics And Vocal Features For Music Emotion
Detection | One of the key points in music recommendation is authoring engaging playlists
according to sentiment and emotions. While previous works were mostly based on
audio for music discovery and playlists generation, we take advantage of our
synchronized lyrics dataset to combine text representations and music features
in a novel way; we therefore introduce the Synchronized Lyrics Emotion Dataset.
Unlike other approaches that randomly exploited the audio samples and the whole
text, our data is split according to the temporal information provided by the
synchronization between lyrics and audio. This work shows a comparison between
text-based and audio-based deep learning classification models using different
techniques from Natural Language Processing and Music Information Retrieval
domains. From the experiments on audio we conclude that using vocals only,
instead of the whole audio data improves the overall performances of the audio
classifier. In the lyrics experiments we exploit the state-of-the-art word
representations applied to the main Deep Learning architectures available in
literature. In our benchmarks the results show how the Bilinear LSTM classifier
with Attention based on fastText word embedding performs better than the CNN
applied on audio.
| 2,019 | Computation and Language |
Conversational Intent Understanding for Passengers in Autonomous
Vehicles | Understanding passenger intents and extracting relevant slots are important
building blocks towards developing a contextual dialogue system responsible for
handling certain vehicle-passenger interactions in autonomous vehicles (AV).
When the passengers give instructions to AMIE (Automated-vehicle Multimodal
In-cabin Experience), the agent should parse such commands properly and trigger
the appropriate functionality of the AV system. In our AMIE scenarios, we
describe usages and support various natural commands for interacting with the
vehicle. We collected a multimodal in-cabin data-set with multi-turn dialogues
between the passengers and AMIE using a Wizard-of-Oz scheme. We explored
various recent Recurrent Neural Networks (RNN) based techniques and built our
own hierarchical models to recognize passenger intents along with relevant
slots associated with the action to be performed in AV scenarios. Our
experimental results achieved F1-score of 0.91 on utterance-level intent
recognition and 0.96 on slot extraction models.
| 2,019 | Computation and Language |
Incremental Reading for Question Answering | Any system which performs goal-directed continual learning must not only
learn incrementally but process and absorb information incrementally. Such a
system also has to understand when its goals have been achieved. In this paper,
we consider these issues in the context of question answering. Current
state-of-the-art question answering models reason over an entire passage, not
incrementally. As we will show, naive approaches to incremental reading, such
as restriction to unidirectional language models in the model, perform poorly.
We present extensions to the DocQA [2] model to allow incremental reading
without loss of accuracy. The model also jointly learns to provide the best
answer given the text that is seen so far and predict whether this best-so-far
answer is sufficient.
| 2,019 | Computation and Language |
Answering Comparative Questions: Better than Ten-Blue-Links? | We present CAM (comparative argumentative machine), a novel open-domain IR
system to argumentatively compare objects with respect to information extracted
from the Common Crawl. In a user study, the participants obtained 15% more
accurate answers using CAM compared to a "traditional" keyword-based search and
were 20% faster in finding the answer to comparative questions.
| 2,019 | Computation and Language |
Investigating Antigram Behaviour using Distributional Semantics | The field of computational linguistics constantly presents new challenges and
topics for research. Whether it be analyzing word usage changes over time or
identifying relationships between pairs of seemingly unrelated words. To this
point, we identify Anagrams and Antigrams as words possessing such unique
properties. The presented work is an exploration into generating anagrams from
a given word and determining whether there exists antigram (semantically
opposite anagrams) relationships between the pairs of generated anagrams using
GloVe embeddings. We propose a rudimentary, yet interpretable, rule-based
algorithm for detecting antigrams. On a small dataset of just 12 antigrams, our
approach yielded an accuracy of 39\% which shows that there is much work left
to be done in this space.
| 2,023 | Computation and Language |
Variable-sized input, character-level recurrent neural networks in lead
generation: predicting close rates from raw user inputs | Predicting lead close rates is one of the most problematic tasks in the lead
generation industry. In most cases, the only available data on the prospect is
the self-reported information inputted by the user on the lead form and a few
other data points publicly available through social media and search engine
usage. All the major market niches for lead generation [1], such as insurance,
health & medical and real estate, deal with life-altering decision making that
no amount of data will be ever be able to describe or predict. This paper
illustrates how character-level, deep long short-term memory networks can be
applied to raw user inputs to help predict close rates. The output of the model
is then used as an additional, highly predictive feature to significantly boost
performance of lead scoring models.
| 2,019 | Computation and Language |
Formal models of Structure Building in Music, Language and Animal Songs | Human language, music and a variety of animal vocalisations constitute ways
of sonic communication that exhibit remarkable structural complexity. While the
complexities of language and possible parallels in animal communication have
been discussed intensively, reflections on the complexity of music and animal
song, and their comparisons are underrepresented. In some ways, music and
animal songs are more comparable to each other than to language, as
propositional semantics cannot be used as as indicator of communicative success
or well-formedness, and notions of grammaticality are less easily defined. This
review brings together accounts of the principles of structure building in
language, music and animal song, relating them to the corresponding models in
formal language theory, with a special focus on evaluating the benefits of
using the Chomsky hierarchy (CH). We further discuss common misunderstandings
and shortcomings concerning the CH, as well as extensions or augmentations of
it that address some of these issues, and suggest ways to move beyond.
| 2,019 | Computation and Language |
Sentence transition matrix: An efficient approach that preserves
sentence semantics | Sentence embedding is a significant research topic in the field of natural
language processing (NLP). Generating sentence embedding vectors reflecting the
intrinsic meaning of a sentence is a key factor to achieve an enhanced
performance in various NLP tasks such as sentence classification and document
summarization. Therefore, various sentence embedding models based on supervised
and unsupervised learning have been proposed after the advent of researches
regarding the distributed representation of words. They were evaluated through
semantic textual similarity (STS) tasks, which measure the degree of semantic
preservation of a sentence and neural network-based supervised embedding models
generally yielded state-of-the-art performance. However, these models have a
limitation in that they have multiple parameters to update, thereby requiring a
tremendous amount of labeled training data. In this study, we propose an
efficient approach that learns a transition matrix that refines a sentence
embedding vector to reflect the latent semantic meaning of a sentence. The
proposed method has two practical advantages; (1) it can be applied to any
sentence embedding method, and (2) it can achieve robust performance in STS
tasks irrespective of the number of training examples.
| 2,019 | Computation and Language |
Dependency or Span, End-to-End Uniform Semantic Role Labeling | Semantic role labeling (SRL) aims to discover the predicateargument structure
of a sentence. End-to-end SRL without syntactic input has received great
attention. However, most of them focus on either span-based or dependency-based
semantic representation form and only show specific model optimization
respectively. Meanwhile, handling these two SRL tasks uniformly was less
successful. This paper presents an end-to-end model for both dependency and
span SRL with a unified argument representation to deal with two different
types of argument annotations in a uniform fashion. Furthermore, we jointly
predict all predicates and arguments, especially including long-term ignored
predicate identification subtask. Our single model achieves new
state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL
2008, 2009) SRL benchmarks.
| 2,019 | Computation and Language |
Assessing BERT's Syntactic Abilities | I assess the extent to which the recently introduced BERT model captures
English syntactic phenomena, using (1) naturally-occurring subject-verb
agreement stimuli; (2) "coloreless green ideas" subject-verb agreement stimuli,
in which content words in natural sentences are randomly replaced with words
sharing the same part-of-speech and inflection; and (3) manually crafted
stimuli for subject-verb agreement and reflexive anaphora phenomena. The BERT
model performs remarkably well on all cases.
| 2,019 | Computation and Language |
Learning from Dialogue after Deployment: Feed Yourself, Chatbot! | The majority of conversations a dialogue agent sees over its lifetime occur
after it has already been trained and deployed, leaving a vast store of
potential training signal untapped. In this work, we propose the self-feeding
chatbot, a dialogue agent with the ability to extract new training examples
from the conversations it participates in. As our agent engages in
conversation, it also estimates user satisfaction in its responses. When the
conversation appears to be going well, the user's responses become new training
examples to imitate. When the agent believes it has made a mistake, it asks for
feedback; learning to predict the feedback that will be given improves the
chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with
over 131k training examples, we find that learning from dialogue with a
self-feeding chatbot significantly improves performance, regardless of the
amount of traditional supervision.
| 2,019 | Computation and Language |
Robust Chinese Word Segmentation with Contextualized Word
Representations | In recent years, after the neural-network-based method was proposed, the
accuracy of the Chinese word segmentation task has made great progress.
However, when dealing with out-of-vocabulary words, there is still a large
error rate. We used a simple bidirectional LSTM architecture and a large-scale
pretrained language model to generate high-quality contextualize character
representations, which successfully reduced the weakness of the ambiguous
meanings of each Chinese character that widely appears in Chinese characters,
and hence effectively reduced OOV error rate. State-of-the-art performance is
achieved on many datasets.
| 2,019 | Computation and Language |
Automatic Keyboard Layout Design for Low-Resource Latin-Script Languages | We present our approach to automatically designing and implementing keyboard
layouts on mobile devices for typing low-resource languages written in the
Latin script. For many speakers, one of the barriers in accessing and creating
text content on the web is the absence of input tools for their language. Ease
in typing in these languages would lower technological barriers to online
communication and collaboration, likely leading to the creation of more web
content. Unfortunately, it can be time-consuming to develop layouts manually
even for language communities that use a keyboard layout very similar to
English; starting from scratch requires many configuration files to describe
multiple possible behaviors for each key. With our approach, we only need a
small amount of data in each language to generate keyboard layouts with very
little human effort. This process can help serve speakers of low-resource
languages in a scalable way, allowing us to develop input tools for more
languages. Having input tools that reflect the linguistic diversity of the
world will let as many people as possible use technology to learn, communicate,
and express themselves in their own native languages.
| 2,019 | Computation and Language |
Chinese Word Segmentation: Another Decade Review (2007-2017) | This paper reviews the development of Chinese word segmentation (CWS) in the
most recent decade, 2007-2017. Special attention was paid to the deep learning
technologies that has already permeated into most areas of natural language
processing (NLP). The basic view we have arrived at is that compared to
traditional supervised learning methods, neural network based methods have not
shown any superior performance. The most critical challenge still lies on
balancing of recognition of in-vocabulary (IV) and out-of-vocabulary (OOV)
words. However, as neural models have potentials to capture the essential
linguistic structure of natural language, we are optimistic about significant
progresses may arrive in the near future.
| 2,019 | Computation and Language |
Exploring Semi-supervised Variational Autoencoders for Biomedical
Relation Extraction | The biomedical literature provides a rich source of knowledge such as
protein-protein interactions (PPIs), drug-drug interactions (DDIs) and
chemical-protein interactions (CPIs). Biomedical relation extraction aims to
automatically extract biomedical relations from biomedical text for various
biomedical research. State-of-the-art methods for biomedical relation
extraction are primarily based on supervised machine learning and therefore
depend on (sufficient) labeled data. However, creating large sets of training
data is prohibitively expensive and labor-intensive, especially so in
biomedicine as domain knowledge is required. In contrast, there is a large
amount of unlabeled biomedical text available in PubMed. Hence, computational
methods capable of employing unlabeled data to reduce the burden of manual
annotation are of particular interest in biomedical relation extraction. We
present a novel semi-supervised approach based on variational autoencoder (VAE)
for biomedical relation extraction. Our model consists of the following three
parts, a classifier, an encoder and a decoder. The classifier is implemented
using multi-layer convolutional neural networks (CNNs), and the encoder and
decoder are implemented using both bidirectional long short-term memory
networks (Bi-LSTMs) and CNNs, respectively. The semi-supervised mechanism
allows our model to learn features from both the labeled and unlabeled data. We
evaluate our method on multiple public PPI, DDI and CPI corpora. Experimental
results show that our method effectively exploits the unlabeled data to improve
the performance and reduce the dependence on labeled data. To our best
knowledge, this is the first semi-supervised VAE-based method for (biomedical)
relation extraction. Our results suggest that exploiting such unlabeled data
can be greatly beneficial to improved performance in various biomedical
relation extraction.
| 2,019 | Computation and Language |
Improving Sequence-to-Sequence Learning via Optimal Transport | Sequence-to-sequence models are commonly trained via maximum likelihood
estimation (MLE). However, standard MLE training considers a word-level
objective, predicting the next word given the previous ground-truth partial
sentence. This procedure focuses on modeling local syntactic patterns, and may
fail to capture long-range semantic structure. We present a novel solution to
alleviate these issues. Our approach imposes global sequence-level guidance via
new supervision based on optimal transport, enabling the overall
characterization and preservation of semantic features. We further show that
this method can be understood as a Wasserstein gradient flow trying to match
our model to the ground truth sequence distribution. Extensive experiments are
conducted to validate the utility of the proposed approach, showing consistent
improvements over a wide variety of NLP tasks, including machine translation,
abstractive text summarization, and image captioning.
| 2,019 | Computation and Language |
Modeling Latent Sentence Structure in Neural Machine Translation | Recently it was shown that linguistic structure predicted by a supervised
parser can be beneficial for neural machine translation (NMT). In this work we
investigate a more challenging setup: we incorporate sentence structure as a
latent variable in a standard NMT encoder-decoder and induce it in such a way
as to benefit the translation task. We consider German-English and
Japanese-English translation benchmarks and observe that when using RNN
encoders the model makes no or very limited use of the structure induction
apparatus. In contrast, CNN and word-embedding-based encoders rely on latent
graphs and force them to encode useful, potentially long-distance,
dependencies.
| 2,020 | Computation and Language |
Towards Universal End-to-End Affect Recognition from Multilingual Speech
by ConvNets | We propose an end-to-end affect recognition approach using a Convolutional
Neural Network (CNN) that handles multiple languages, with applications to
emotion and personality recognition from speech. We lay the foundation of a
universal model that is trained on multiple languages at once. As affect is
shared across all languages, we are able to leverage shared information between
languages and improve the overall performance for each one. We obtained an
average improvement of 12.8% on emotion and 10.1% on personality when compared
with the same model trained on each language only. It is end-to-end because we
directly take narrow-band raw waveforms as input. This allows us to accept as
input audio recorded from any source and to avoid the overhead and information
loss of feature extraction. It outperforms a similar CNN using spectrograms as
input by 12.8% for emotion and 6.3% for personality, based on F-scores.
Analysis of the network parameters and layers activation shows that the network
learns and extracts significant features in the first layer, in particular
pitch, energy and contour variations. Subsequent convolutional layers instead
capture language-specific representations through the analysis of
supra-segmental features. Our model represents an important step for the
development of a fully universal affect recognizer, able to recognize
additional descriptors, such as stress, and for the future implementation into
affective interactive systems.
| 2,019 | Computation and Language |
MOROCO: The Moldavian and Romanian Dialectal Corpus | In this work, we introduce the MOldavian and ROmanian Dialectal COrpus
(MOROCO), which is freely available for download at
https://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of
text (with over 10 million tokens) collected from the news domain. The samples
belong to one of the following six topics: culture, finance, politics, science,
sports and tech. The data set is divided into 21719 samples for training, 5921
samples for validation and another 5924 samples for testing. For each sample,
we provide corresponding dialectal and category labels. This allows us to
perform empirical studies on several classification tasks such as (i) binary
discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect
multi-class categorization by topic and (iii) cross-dialect multi-class
categorization by topic. We perform experiments using a shallow approach based
on string kernels, as well as a novel deep approach based on character-level
convolutional neural networks containing Squeeze-and-Excitation blocks. We also
present and analyze the most discriminative features of our best performing
model, before and after named entity removal.
| 2,019 | Computation and Language |
Hierarchical Attentional Hybrid Neural Networks for Document
Classification | Document classification is a challenging task with important applications.
The deep learning approaches to the problem have gained much attention
recently. Despite the progress, the proposed models do not incorporate the
knowledge of the document structure in the architecture efficiently and not
take into account the contexting importance of words and sentences. In this
paper, we propose a new approach based on a combination of convolutional neural
networks, gated recurrent units, and attention mechanisms for document
classification tasks. The main contribution of this work is the use of
convolution layers to extract more meaningful, generalizable and abstract
features by the hierarchical representation. The proposed method in this paper
improves the results of the current attention-based approaches for document
classification.
| 2,019 | Computation and Language |
Beyond Turing: Intelligent Agents Centered on the User | Most research on intelligent agents centers on the agent and not on the user.
We look at the origins of agent-centric research for slot-filling, gaming and
chatbot agents. We then argue that it is important to concentrate more on the
user. After reviewing relevant literature, some approaches for creating and
assessing user-centric systems are proposed.
| 2,019 | Computation and Language |
Adversarial Attacks on Deep Learning Models in Natural Language
Processing: A Survey | With the development of high computational devices, deep neural networks
(DNNs), in recent years, have gained significant popularity in many Artificial
Intelligence (AI) applications. However, previous efforts have shown that DNNs
were vulnerable to strategically modified samples, named adversarial examples.
These samples are generated with some imperceptible perturbations but can fool
the DNNs to give false predictions. Inspired by the popularity of generating
adversarial examples for image DNNs, research efforts on attacking DNNs for
textual applications emerges in recent years. However, existing perturbation
methods for images cannotbe directly applied to texts as text data is discrete.
In this article, we review research works that address this difference and
generatetextual adversarial examples on DNNs. We collect, select, summarize,
discuss and analyze these works in a comprehensive way andcover all the related
information to make the article self-contained. Finally, drawing on the
reviewed literature, we provide further discussions and suggestions on this
topic.
| 2,019 | Computation and Language |
Chemical Names Standardization using Neural Sequence to Sequence Model | Chemical information extraction is to convert chemical knowledge in text into
true chemical database, which is a text processing task heavily relying on
chemical compound name identification and standardization. Once a systematic
name for a chemical compound is given, it will naturally and much simply
convert the name into the eventually required molecular formula. However, for
many chemical substances, they have been shown in many other names besides
their systematic names which poses a great challenge for this task. In this
paper, we propose a framework to do the auto standardization from the
non-systematic names to the corresponding systematic names by using the
spelling error correction, byte pair encoding tokenization and neural sequence
to sequence model. Our framework is trained end to end and is fully
data-driven. Our standardization accuracy on the test dataset achieves 54.04%
which has a great improvement compared to previous state-of-the-art result.
| 2,019 | Computation and Language |
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition
and Linking in Tweets | In recent years, with the prevalence of social media and smart devices,
people causally reveal their locations such as shops, hotels, and restaurants
in their tweets. Recognizing and linking such fine-grained location mentions to
well-defined location profiles are beneficial for retrieval and recommendation
systems. In this paper, we propose DLocRL, a new deep learning pipeline for
fine-grained location recognition and linking in tweets, and verify its
effectiveness on a real-world Twitter dataset.
| 2,019 | Computation and Language |
An Adversarial Approach to High-Quality, Sentiment-Controlled Neural
Dialogue Generation | In this work, we propose a method for neural dialogue response generation
that allows not only generating semantically reasonable responses according to
the dialogue history, but also explicitly controlling the sentiment of the
response via sentiment labels. Our proposed model is based on the paradigm of
conditional adversarial learning; the training of a sentiment-controlled
dialogue generator is assisted by an adversarial discriminator which assesses
the fluency and feasibility of the response generating from the dialogue
history and a given sentiment label. Because of the flexibility of our
framework, the generator could be a standard sequence-to-sequence (SEQ2SEQ)
model or a more complicated one such as a conditional variational
autoencoder-based SEQ2SEQ model. Experimental results using automatic and human
evaluation both demonstrate that our proposed framework is able to generate
both semantically reasonable and sentiment-controlled dialogue responses.
| 2,019 | Computation and Language |
Cross-lingual Language Model Pretraining | Recent studies have demonstrated the efficiency of generative pretraining for
English natural language understanding. In this work, we extend this approach
to multiple languages and show the effectiveness of cross-lingual pretraining.
We propose two methods to learn cross-lingual language models (XLMs): one
unsupervised that only relies on monolingual data, and one supervised that
leverages parallel data with a new cross-lingual language model objective. We
obtain state-of-the-art results on cross-lingual classification, unsupervised
and supervised machine translation. On XNLI, our approach pushes the state of
the art by an absolute gain of 4.9% accuracy. On unsupervised machine
translation, we obtain 34.3 BLEU on WMT'16 German-English, improving the
previous state of the art by more than 9 BLEU. On supervised machine
translation, we obtain a new state of the art of 38.5 BLEU on WMT'16
Romanian-English, outperforming the previous best approach by more than 4 BLEU.
Our code and pretrained models will be made publicly available.
| 2,019 | Computation and Language |
Deep learning and sub-word-unit approach in written art generation | Automatic poetry generation is novel and interesting application of natural
language processing research. It became more popular during the last few years
due to the rapid development of technology and neural computing power. This
line of research can be applied to the study of linguistics and literature, for
social science experiments, or simply for entertainment. The most effective
known method of artificial poem generation uses recurrent neural networks
(RNN). We also used RNNs to generate poems in the style of Adam Mickiewicz. Our
network was trained on the Sir Thaddeus poem. For data pre-processing, we used
a specialized stemming tool, which is one of the major innovations and
contributions of this work. Our experiment was conducted on the source text,
divided into sub-word units (at a level of resolution close to syllables). This
approach is novel and is not often employed in the published literature. The
subwords units seem to be a natural choice for analysis of the Polish language,
as the language is morphologically rich due to cases, gender forms and a large
vocabulary. Moreover, Sir Thaddeus contains rhymes, so the analysis of
syllables can be meaningful. We verified our model with different settings for
the temperature parameter, which controls the randomness of the generated text.
We also compared our results with similar models trained on the same text but
divided into characters (which is the most common approach alongside the use of
full word units). The differences were tremendous. Our solution generated much
better poems that were able to follow the metre and vocabulary of the source
data text.
| 2,019 | Computation and Language |
Debugging Frame Semantic Role Labeling | We propose a quantitative and qualitative analysis of the performances of
statistical models for frame semantic structure extraction. We report on a
replication study on FrameNet 1.7 data and show that preprocessing toolkits
play a major role in argument identification performances, observing gains
similar in their order of magnitude to those reported by recent models for
frame semantic parsing. We report on the robustness of a recent statistical
classifier for frame semantic parsing to lexical configurations of
predicate-argument structures, relying on an artificially augmented dataset
generated using a rule-based algorithm combining valence pattern matching and
lexical substitution. We prove that syntactic pre-processing plays a major role
in the performances of statistical classifiers to argument identification, and
discuss the core reasons of syntactic mismatch between dependency parsers
output and FrameNet syntactic formalism. Finally, we suggest new leads for
improving statistical models for frame semantic parsing, including joint
syntax-semantic parsing relying on FrameNet syntactic formalism, latent classes
inference via split-and-merge algorithms and neural network architectures
relying on rich input representations of words.
| 2,019 | Computation and Language |
Delta-training: Simple Semi-Supervised Text Classification using
Pretrained Word Embeddings | We propose a novel and simple method for semi-supervised text classification.
The method stems from the hypothesis that a classifier with pretrained word
embeddings always outperforms the same classifier with randomly initialized
word embeddings, as empirically observed in NLP tasks. Our method first builds
two sets of classifiers as a form of model ensemble, and then initializes their
word embeddings differently: one using random, the other using pretrained word
embeddings. We focus on different predictions between the two classifiers on
unlabeled data while following the self-training framework. We also use
early-stopping in meta-epoch to improve the performance of our method. Our
method, Delta-training, outperforms the self-training and the co-training
framework in 4 different text classification datasets, showing robustness
against error accumulation.
| 2,019 | Computation and Language |
Attenuating Bias in Word Vectors | Word vector representations are well developed tools for various NLP and
Machine Learning tasks and are known to retain significant semantic and
syntactic structure of languages. But they are prone to carrying and amplifying
bias which can perpetrate discrimination in various applications. In this work,
we explore new simple ways to detect the most stereotypically gendered words in
an embedding and remove the bias from them. We verify how names are masked
carriers of gender bias and then use that as a tool to attenuate bias in
embeddings. Further, we extend this property of names to show how names can be
used to detect other types of bias in the embeddings such as bias based on
race, ethnicity, and age.
| 2,019 | Computation and Language |
Context-Sensitive Malicious Spelling Error Correction | Misspelled words of the malicious kind work by changing specific keywords and
are intended to thwart existing automated applications for cyber-environment
control such as harassing content detection on the Internet and email spam
detection. In this paper, we focus on malicious spelling correction, which
requires an approach that relies on the context and the surface forms of
targeted keywords. In the context of two applications--profanity detection and
email spam detection--we show that malicious misspellings seriously degrade
their performance. We then propose a context-sensitive approach for malicious
spelling correction using word embeddings and demonstrate its superior
performance compared to state-of-the-art spell checkers.
| 2,019 | Computation and Language |
Product-Aware Answer Generation in E-Commerce Question-Answering | In e-commerce portals, generating answers for product-related questions has
become a crucial task. In this paper, we propose the task of product-aware
answer generation, which tends to generate an accurate and complete answer from
large-scale unlabeled e-commerce reviews and product attributes. Unlike
existing question-answering problems, answer generation in e-commerce confronts
three main challenges: (1) Reviews are informal and noisy; (2) joint modeling
of reviews and key-value product attributes is challenging; (3) traditional
methods easily generate meaningless answers. To tackle above challenges, we
propose an adversarial learning based model, named PAAG, which is composed of
three components: a question-aware review representation module, a key-value
memory network encoding attributes, and a recurrent neural network as a
sequence generator. Specifically, we employ a convolutional discriminator to
distinguish whether our generated answer matches the facts. To extract the
salience part of reviews, an attention-based review reader is proposed to
capture the most relevant words given the question. Conducted on a large-scale
real-world e-commerce dataset, our extensive experiments verify the
effectiveness of each module in our proposed model. Moreover, our experiments
show that our model achieves the state-of-the-art performance in terms of both
automatic metrics and human evaluations.
| 2,019 | Computation and Language |
Automated Essay Scoring based on Two-Stage Learning | Current state-of-art feature-engineered and end-to-end Automated Essay Score
(AES) methods are proven to be unable to detect adversarial samples, e.g. the
essays composed of permuted sentences and the prompt-irrelevant essays.
Focusing on the problem, we develop a Two-Stage Learning Framework (TSLF) which
integrates the advantages of both feature-engineered and end-to-end AES models.
In experiments, we compare TSLF against a number of strong baselines, and the
results demonstrate the effectiveness and robustness of our models. TSLF
surpasses all the baselines on five-eighths of prompts and achieves new
state-of-the-art average performance when without negative samples. After
adding some adversarial essays to the original datasets, TSLF outperforms the
feature-engineered and end-to-end baselines to a great extent, and shows great
robustness.
| 2,019 | Computation and Language |
Self-Attentive Model for Headline Generation | Headline generation is a special type of text summarization task. While the
amount of available training data for this task is almost unlimited, it still
remains challenging, as learning to generate headlines for news articles
implies that the model has strong reasoning about natural language. To overcome
this issue, we applied recent Universal Transformer architecture paired with
byte-pair encoding technique and achieved new state-of-the-art results on the
New York Times Annotated corpus with ROUGE-L F1-score 24.84 and ROUGE-2
F1-score 13.48. We also present the new RIA corpus and reach ROUGE-L F1-score
36.81 and ROUGE-2 F1-score 22.15 on it.
| 2,019 | Computation and Language |
AspeRa: Aspect-based Rating Prediction Model | We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa)
that estimates user rating based on review texts for the items and at the same
time discovers coherent aspects of reviews that can be used to explain
predictions or profile users. The AspeRa model uses max-margin losses for joint
item and user embedding learning and a dual-headed architecture; it
significantly outperforms recently proposed state-of-the-art models such as
DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews.
With qualitative examination of the aspects and quantitative evaluation of
rating prediction models based on these aspects, we show how aspect embeddings
can be used in a recommender system.
| 2,019 | Computation and Language |
Context based Analysis of Lexical Semantics for Hindi Language | A word having multiple senses in a text introduces the lexical semantic task
to find out which particular sense is appropriate for the given context. One
such task is Word sense disambiguation which refers to the identification of
the most appropriate meaning of the polysemous word in a given context using
computational algorithms. The language processing research in Hindi, the
official language of India, and other Indian languages is restricted by
unavailability of the standard corpus. For Hindi word sense disambiguation
also, the large corpus is not available. In this work, we prepared the text
containing new senses of certain words leading to the enrichment of the
sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed
two novel lexical associations for Hindi word sense disambiguation based on the
contextual features of the polysemous word. The evaluation of these methods is
carried out over learning algorithms and favorable results are achieved.
| 2,019 | Computation and Language |
Phonetic-enriched Text Representation for Chinese Sentiment Analysis
with Reinforcement Learning | The Chinese pronunciation system offers two characteristics that distinguish
it from other languages: deep phonemic orthography and intonation variations.
We are the first to argue that these two important properties can play a major
role in Chinese sentiment analysis. Particularly, we propose two effective
features to encode phonetic information. Next, we develop a Disambiguate
Intonation for Sentiment Analysis (DISA) network using a reinforcement network.
It functions as disambiguating intonations for each Chinese character (pinyin).
Thus, a precise phonetic representation of Chinese is learned. Furthermore, we
also fuse phonetic features with textual and visual features in order to mimic
the way humans read and understand Chinese text. Experimental results on five
different Chinese sentiment analysis datasets show that the inclusion of
phonetic features significantly and consistently improves the performance of
textual and visual representations and outshines the state-of-the-art Chinese
character level representations.
| 2,019 | Computation and Language |
Evaluating the State-of-the-Art of End-to-End Natural Language
Generation: The E2E NLG Challenge | This paper provides a comprehensive analysis of the first shared task on
End-to-End Natural Language Generation (NLG) and identifies avenues for future
research based on the results. This shared task aimed to assess whether recent
end-to-end NLG systems can generate more complex output by learning from
datasets containing higher lexical richness, syntactic complexity and diverse
discourse phenomena. Introducing novel automatic and human metrics, we compare
62 systems submitted by 17 institutions, covering a wide range of approaches,
including machine learning architectures -- with the majority implementing
sequence-to-sequence models (seq2seq) -- as well as systems based on
grammatical rules and templates. Seq2seq-based systems have demonstrated a
great potential for NLG in the challenge. We find that seq2seq systems
generally score high in terms of word-overlap metrics and human evaluations of
naturalness -- with the winning SLUG system (Juraska et al., 2018) being
seq2seq-based. However, vanilla seq2seq models often fail to correctly express
a given meaning representation if they lack a strong semantic control mechanism
applied during decoding. Moreover, seq2seq models can be outperformed by
hand-engineered systems in terms of overall quality, as well as complexity,
length and diversity of outputs. This research has influenced, inspired and
motivated a number of recent studies outwith the original competition, which we
also summarise as part of this paper.
| 2,019 | Computation and Language |
Sentiment and Sarcasm Classification with Multitask Learning | Sentiment classification and sarcasm detection are both important natural
language processing (NLP) tasks. Sentiment is always coupled with sarcasm where
intensive emotion is expressed. Nevertheless, most literature considers them as
two separate tasks. We argue that knowledge in sarcasm detection can also be
beneficial to sentiment classification and vice versa. We show that these two
tasks are correlated, and present a multi-task learning-based framework using a
deep neural network that models this correlation to improve the performance of
both tasks in a multi-task learning setting. Our method outperforms the state
of the art by 3-4% in the benchmark dataset.
| 2,019 | Computation and Language |
A Question-Entailment Approach to Question Answering | One of the challenges in large-scale information retrieval (IR) is to develop
fine-grained and domain-specific methods to answer natural language questions.
Despite the availability of numerous sources and datasets for answer retrieval,
Question Answering (QA) remains a challenging problem due to the difficulty of
the question understanding and answer extraction tasks. One of the promising
tracks investigated in QA is to map new questions to formerly answered
questions that are `similar'. In this paper, we propose a novel QA approach
based on Recognizing Question Entailment (RQE) and we describe the QA system
and resources that we built and evaluated on real medical questions. First, we
compare machine learning and deep learning methods for RQE using different
kinds of datasets, including textual inference, question similarity and
entailment in both the open and clinical domains. Second, we combine IR models
with the best RQE method to select entailed questions and rank the retrieved
answers. To study the end-to-end QA approach, we built the MedQuAD collection
of 47,457 question-answer pairs from trusted medical sources, that we introduce
and share in the scope of this paper. Following the evaluation process used in
TREC 2017 LiveQA, we find that our approach exceeds the best results of the
medical task with a 29.8% increase over the best official score. The evaluation
results also support the relevance of question entailment for QA and highlight
the effectiveness of combining IR and RQE for future QA efforts. Our findings
also show that relying on a restricted set of reliable answer sources can bring
a substantial improvement in medical QA.
| 2,019 | Computation and Language |
TransferTransfo: A Transfer Learning Approach for Neural Network Based
Conversational Agents | We introduce a new approach to generative data-driven dialogue systems (e.g.
chatbots) called TransferTransfo which is a combination of a Transfer learning
based training scheme and a high-capacity Transformer model. Fine-tuning is
performed by using a multi-task objective which combines several unsupervised
prediction tasks. The resulting fine-tuned model shows strong improvements over
the current state-of-the-art end-to-end conversational models like memory
augmented seq2seq and information-retrieval models. On the privately held
PERSONA-CHAT dataset of the Conversational Intelligence Challenge 2, this
approach obtains a new state-of-the-art, with respective perplexity, Hits@1 and
F1 metrics of 16.28 (45 % absolute improvement), 80.7 (46 % absolute
improvement) and 19.5 (20 % absolute improvement).
| 2,019 | Computation and Language |
A Tool for Spatio-Temporal Analysis of Social Anxiety with Twitter Data | In this paper, we present a tool for analyzing spatio-temporal distribution
of social anxiety. Twitter, one of the most popular social network services,
has been chosen as data source for analysis of social anxiety. Tweets (posted
on the Twitter) contain various emotions and thus these individual emotions
reflect social atmosphere and public opinion, which are often dependent on
spatial and temporal factors. The reason why we choose anxiety among various
emotions is that anxiety is very important emotion that is useful for observing
and understanding social events of communities. We develop a machine learning
based tool to analyze the changes of social atmosphere spatially and
temporally. Our tool classifies whether each Tweet contains anxious content or
not, and also estimates degree of Tweet anxiety. Furthermore, it also
visualizes spatio-temporal distribution of anxiety as a form of web
application, which is incorporated with physical map, word cloud, search engine
and chart viewer. Our tool is applied to a big tweet data in South Korea to
illustrate its usefulness for exploring social atmosphere and public opinion
spatio-temporally.
| 2,019 | Computation and Language |
Semantic Relation Classification via Bidirectional LSTM Networks with
Entity-aware Attention using Latent Entity Typing | Classifying semantic relations between entity pairs in sentences is an
important task in Natural Language Processing (NLP). Most previous models for
relation classification rely on the high-level lexical and syntactic features
obtained by NLP tools such as WordNet, dependency parser, part-of-speech (POS)
tagger, and named entity recognizers (NER). In addition, state-of-the-art
neural models based on attention mechanisms do not fully utilize information of
entity that may be the most crucial features for relation classification. To
address these issues, we propose a novel end-to-end recurrent neural model
which incorporates an entity-aware attention mechanism with a latent entity
typing (LET) method. Our model not only utilizes entities and their latent
types as features effectively but also is more interpretable by visualizing
attention mechanisms applied to our model and results of LET. Experimental
results on the SemEval-2010 Task 8, one of the most popular relation
classification task, demonstrate that our model outperforms existing
state-of-the-art models without any high-level features.
| 2,019 | Computation and Language |
FANDA: A Novel Approach to Perform Follow-up Query Analysis | Recent work on Natural Language Interfaces to Databases (NLIDB) has attracted
considerable attention. NLIDB allow users to search databases using natural
language instead of SQL-like query languages. While saving the users from
having to learn query languages, multi-turn interaction with NLIDB usually
involves multiple queries where contextual information is vital to understand
the users' query intents. In this paper, we address a typical contextual
understanding problem, termed as follow-up query analysis. In spite of its
ubiquity, follow-up query analysis has not been well studied due to two primary
obstacles: the multifarious nature of follow-up query scenarios and the lack of
high-quality datasets. Our work summarizes typical follow-up query scenarios
and provides a new FollowUp dataset with $1000$ query triples on 120 tables.
Moreover, we propose a novel approach FANDA, which takes into account the
structures of queries and employs a ranking model with weakly supervised
max-margin learning. The experimental results on FollowUp demonstrate the
superiority of FANDA over multiple baselines across multiple metrics.
| 2,019 | Computation and Language |
A review of sentiment computation methods with R packages | Four packages in R are analyzed to carry out sentiment analysis. All packages
allow to define custom dictionaries. Just one - Sentiment R - properly accounts
for the presence of negators.
| 2,019 | Computation and Language |
Extracting PICO elements from RCT abstracts using 1-2gram analysis and
multitask classification | The core of evidence-based medicine is to read and analyze numerous papers in
the medical literature on a specific clinical problem and summarize the
authoritative answers to that problem. Currently, to formulate a clear and
focused clinical problem, the popular PICO framework is usually adopted, in
which each clinical problem is considered to consist of four parts:
patient/problem (P), intervention (I), comparison (C) and outcome (O). In this
study, we compared several classification models that are commonly used in
traditional machine learning. Next, we developed a multitask classification
model based on a soft-margin SVM with a specialized feature engineering method
that combines 1-2gram analysis with TF-IDF analysis. Finally, we trained and
tested several generic models on an open-source data set from BioNLP 2018. The
results show that the proposed multitask SVM classification model based on
1-2gram TF-IDF features exhibits the best performance among the tested models.
| 2,019 | Computation and Language |
Semantic Classification of Tabular Datasets via Character-Level
Convolutional Neural Networks | A character-level convolutional neural network (CNN) motivated by
applications in "automated machine learning" (AutoML) is proposed to
semantically classify columns in tabular data. Simulated data containing a set
of base classes is first used to learn an initial set of weights. Hand-labeled
data from the CKAN repository is then used in a transfer-learning paradigm to
adapt the initial weights to a more sophisticated representation of the problem
(e.g., including more classes). In doing so, realistic data imperfections are
learned and the set of classes handled can be expanded from the base set with
reduced labeled data and computing power requirements. Results show the
effectiveness and flexibility of this approach in three diverse domains:
semantic classification of tabular data, age prediction from social media
posts, and email spam classification. In addition to providing further evidence
of the effectiveness of transfer learning in natural language processing (NLP),
our experiments suggest that analyzing the semantic structure of language at
the character level without additional metadata---i.e., network structure,
headers, etc.---can produce competitive accuracy for type classification, spam
classification, and social media age prediction. We present our open-source
toolkit SIMON, an acronym for Semantic Inference for the Modeling of
ONtologies, which implements this approach in a user-friendly and
scalable/parallelizable fashion.
| 2,019 | Computation and Language |
Automatic Parallel Corpus Creation for Hindi-English News Translation
Task | The parallel corpus for multilingual NLP tasks, deep learning applications
like Statistical Machine Translation Systems is very important. The parallel
corpus of Hindi-English language pair available for news translation task till
date is of very limited size as per the requirement of the systems are
concerned. In this work we have developed an automatic parallel corpus
generation system prototype, which creates Hindi-English parallel corpus for
news translation task. Further to verify the quality of generated parallel
corpus we have experimented by taking various performance metrics and the
results are quite interesting.
| 2,019 | Computation and Language |
A BERT Baseline for the Natural Questions | This technical note describes a new baseline for the Natural Questions. Our
model is based on BERT and reduces the gap between the model F1 scores reported
in the original dataset paper and the human upper bound by 30% and 50% relative
for the long and short answer tasks respectively. This baseline has been
submitted to the official NQ leaderboard at
ai.google.com/research/NaturalQuestions. Code, preprocessed data and pretrained
model are available at
https://github.com/google-research/language/tree/master/language/question_answering/bert_joint.
| 2,019 | Computation and Language |
Emergent Linguistic Phenomena in Multi-Agent Communication Games | In this work, we propose a computational framework in which agents equipped
with communication capabilities simultaneously play a series of referential
games, where agents are trained using deep reinforcement learning. We
demonstrate that the framework mirrors linguistic phenomena observed in natural
language: i) the outcome of contact between communities is a function of inter-
and intra-group connectivity; ii) linguistic contact either converges to the
majority protocol, or in balanced cases leads to novel creole languages of
lower complexity; and iii) a linguistic continuum emerges where neighboring
languages are more mutually intelligible than farther removed languages. We
conclude that intricate properties of language evolution need not depend on
complex evolved linguistic capabilities, but can emerge from simple social
exchanges between perceptually-enabled agents playing communication games.
| 2,020 | Computation and Language |
BioBERT: a pre-trained biomedical language representation model for
biomedical text mining | Biomedical text mining is becoming increasingly important as the number of
biomedical documents rapidly grows. With the progress in natural language
processing (NLP), extracting valuable information from biomedical literature
has gained popularity among researchers, and deep learning has boosted the
development of effective biomedical text mining models. However, directly
applying the advancements in NLP to biomedical text mining often yields
unsatisfactory results due to a word distribution shift from general domain
corpora to biomedical corpora. In this article, we investigate how the recently
introduced pre-trained language model BERT can be adapted for biomedical
corpora. We introduce BioBERT (Bidirectional Encoder Representations from
Transformers for Biomedical Text Mining), which is a domain-specific language
representation model pre-trained on large-scale biomedical corpora. With almost
the same architecture across tasks, BioBERT largely outperforms BERT and
previous state-of-the-art models in a variety of biomedical text mining tasks
when pre-trained on biomedical corpora. While BERT obtains performance
comparable to that of previous state-of-the-art models, BioBERT significantly
outperforms them on the following three representative biomedical text mining
tasks: biomedical named entity recognition (0.62% F1 score improvement),
biomedical relation extraction (2.80% F1 score improvement) and biomedical
question answering (12.24% MRR improvement). Our analysis results show that
pre-training BERT on biomedical corpora helps it to understand complex
biomedical texts. We make the pre-trained weights of BioBERT freely available
at https://github.com/naver/biobert-pretrained, and the source code for
fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.
| 2,019 | Computation and Language |
Misleading Metadata Detection on YouTube | YouTube is the leading social media platform for sharing videos. As a result,
it is plagued with misleading content that includes staged videos presented as
real footages from an incident, videos with misrepresented context and videos
where audio/video content is morphed. We tackle the problem of detecting such
misleading videos as a supervised classification task. We develop UCNet - a
deep network to detect fake videos and perform our experiments on two datasets
- VAVD created by us and publicly available FVC [8]. We achieve a macro
averaged F-score of 0.82 while training and testing on a 70:30 split of FVC,
while the baseline model scores 0.36. We find that the proposed model
generalizes well when trained on one dataset and tested on the other.
| 2,019 | Computation and Language |
Word Embeddings: A Survey | This work lists and describes the main recent strategies for building
fixed-length, dense and distributed representations for words, based on the
distributional hypothesis. These representations are now commonly called word
embeddings and, in addition to encoding surprisingly good syntactic and
semantic information, have been proven useful as extra features in many
downstream NLP tasks.
| 2,023 | Computation and Language |
Context in Neural Machine Translation: A Review of Models and
Evaluations | This review paper discusses how context has been used in neural machine
translation (NMT) in the past two years (2017-2018). Starting with a brief
retrospect on the rapid evolution of NMT models, the paper then reviews studies
that evaluate NMT output from various perspectives, with emphasis on those
analyzing limitations of the translation of contextual phenomena. In a
subsequent version, the paper will then present the main methods that were
proposed to leverage context for improving translation quality, and
distinguishes methods that aim to improve the translation of specific phenomena
from those that consider a wider unstructured context.
| 2,019 | Computation and Language |
Language Model Pre-training for Hierarchical Document Representations | Hierarchical neural architectures are often used to capture long-distance
dependencies and have been applied to many document-level tasks such as
summarization, document segmentation, and sentiment analysis. However,
effective usage of such a large context can be difficult to learn, especially
in the case where there is limited labeled data available. Building on the
recent success of language model pretraining methods for learning flat
representations of text, we propose algorithms for pre-training hierarchical
document representations from unlabeled data. Unlike prior work, which has
focused on pre-training contextual token representations or context-independent
{sentence/paragraph} representations, our hierarchical document representations
include fixed-length sentence/paragraph representations which integrate
contextual information from the entire documents. Experiments on document
segmentation, document-level question answering, and extractive document
summarization demonstrate the effectiveness of the proposed pre-training
algorithms.
| 2,019 | Computation and Language |
Implicit Dimension Identification in User-Generated Text with LSTM
Networks | In the process of online storytelling, individual users create and consume
highly diverse content that contains a great deal of implicit beliefs and not
plainly expressed narrative. It is hard to manually detect these implicit
beliefs, intentions and moral foundations of the writers. We study and
investigate two different tasks, each of which reflect the difficulty of
detecting an implicit user's knowledge, intent or belief that may be based on
writer's moral foundation: 1) political perspective detection in news articles
2) identification of informational vs. conversational questions in community
question answering (CQA) archives and. In both tasks we first describe new
interesting annotated datasets and make the datasets publicly available.
Second, we compare various classification algorithms, and show the differences
in their performance on both tasks. Third, in political perspective detection
task we utilize a narrative representation language of local press to identify
perspective differences between presumably neutral American and British press.
| 2,019 | Computation and Language |
Variational Smoothing in Recurrent Neural Network Language Models | We present a new theoretical perspective of data noising in recurrent neural
network language models (Xie et al., 2017). We show that each variant of data
noising is an instance of Bayesian recurrent neural networks with a particular
variational distribution (i.e., a mixture of Gaussians whose weights depend on
statistics derived from the corpus such as the unigram distribution). We use
this insight to propose a more principled method to apply at prediction time
and propose natural extensions to data noising under the variational framework.
In particular, we propose variational smoothing with tied input and output
embedding matrices and an element-wise variational smoothing method. We
empirically verify our analysis on two benchmark language modeling datasets and
demonstrate performance improvements over existing data noising methods.
| 2,019 | Computation and Language |
Dual Co-Matching Network for Multi-choice Reading Comprehension | Multi-choice reading comprehension is a challenging task that requires
complex reasoning procedure. Given passage and question, a correct answer need
to be selected from a set of candidate answers. In this paper, we propose
\textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN})
which model the relationship among passage, question and answer
bidirectionally. Different from existing approaches which only calculate
question-aware or option-aware passage representation, we calculate
passage-aware question representation and passage-aware answer representation
at the same time. To demonstrate the effectiveness of our model, we evaluate
our model on a large-scale multiple choice machine reading comprehension
dataset (i.e. RACE). Experimental result show that our proposed model achieves
new state-of-the-art results.
| 2,019 | Computation and Language |
Promoting Diversity for End-to-End Conversation Response Generation | We present our work on Track 2 in the Dialog System Technology Challenges 7
(DSTC7). The DSTC7-Track 2 aims to evaluate the response generation of fully
data-driven conversation models in knowledge-grounded settings, which provides
the contextual-relevant factual texts. The Sequenceto-Sequence models have been
widely used for end-to-end generative conversation modelling and achieved
impressive results. However, they tend to output dull and repeated responses in
previous studies. Our work aims to promote the diversity for end-to-end
conversation response generation, which follows a two-stage pipeline: 1)
Generate multiple responses. At this stage, two different models are proposed,
i.e., a variational generative (VariGen) model and a retrieval based
(Retrieval) model. 2) Rank and return the most related response by training a
topic coherence discrimination (TCD) model for the ranking process. According
to the official evaluation results, our proposed Retrieval and VariGen systems
ranked first and second respectively on objective diversity metrics, i.e.,
Entropy, among all participant systems. And the VariGen system ranked second on
NIST and METEOR metrics.
| 2,019 | Computation and Language |
Neural Related Work Summarization with a Joint Context-driven Attention
Mechanism | Conventional solutions to automatic related work summarization rely heavily
on human-engineered features. In this paper, we develop a neural data-driven
summarizer by leveraging the seq2seq paradigm, in which a joint context-driven
attention mechanism is proposed to measure the contextual relevance within full
texts and a heterogeneous bibliography graph simultaneously. Our motivation is
to maintain the topic coherency between a related work section and its target
document, where both the textual and graphic contexts play a big role in
characterizing the relationship among scientific publications accurately.
Experimental results on a large dataset show that our approach achieves a
considerable improvement over a typical seq2seq summarizer and five classical
summarization baselines.
| 2,021 | Computation and Language |
Data-to-Text Generation with Style Imitation | Recent neural approaches to data-to-text generation have mostly focused on
improving content fidelity while lacking explicit control over writing styles
(e.g., word choices, sentence structures). More traditional systems use
templates to determine the realization of text. Yet manual or automatic
construction of high-quality templates is difficult, and a template acting as
hard constraints could harm content fidelity when it does not match the record
perfectly. We study a new way of stylistic control by using existing sentences
as soft templates. That is, the model learns to imitate the writing style of
any given exemplar sentence, with automatic adaptions to faithfully describe
the content record. The problem is challenging due to the lack of parallel
data. We develop a neural approach that includes a hybrid attention-copy
mechanism, learns with weak supervisions, and is enhanced with a new content
coverage constraint. We conduct experiments in restaurants and sports domains.
Results show our approach achieves stronger performance than a range of
comparison methods. Our approach balances well between content fidelity and
style control given exemplars that match the records to varying degrees.
| 2,020 | Computation and Language |
Personalized Dialogue Generation with Diversified Traits | Endowing a dialogue system with particular personality traits is essential to
deliver more human-like conversations. However, due to the challenge of
embodying personality via language expression and the lack of large-scale
persona-labeled dialogue data, this research problem is still far from
well-studied. In this paper, we investigate the problem of incorporating
explicit personality traits in dialogue generation to deliver personalized
dialogues.
To this end, firstly, we construct PersonalDialog, a large-scale multi-turn
dialogue dataset containing various traits from a large number of speakers. The
dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers.
Each utterance is associated with a speaker who is marked with traits like Age,
Gender, Location, Interest Tags, etc. Several anonymization schemes are
designed to protect the privacy of each speaker. This large-scale dataset will
facilitate not only the study of personalized dialogue generation, but also
other researches on sociolinguistics or social science.
Secondly, to study how personality traits can be captured and addressed in
dialogue generation, we propose persona-aware dialogue generation models within
the sequence to sequence learning framework. Explicit personality traits
(structured by key-value pairs) are embedded using a trait fusion module.
During the decoding process, two techniques, namely persona-aware attention and
persona-aware bias, are devised to capture and address trait-related
information. Experiments demonstrate that our model is able to address proper
traits in different contexts. Case studies also show interesting results for
this challenging research problem.
| 2,020 | Computation and Language |
Language Independent Sequence Labelling for Opinion Target Extraction | In this research note we present a language independent system to model
Opinion Target Extraction (OTE) as a sequence labelling task. The system
consists of a combination of clustering features implemented on top of a simple
set of shallow local features. Experiments on the well known Aspect Based
Sentiment Analysis (ABSA) benchmarks show that our approach is very competitive
across languages, obtaining best results for six languages in seven different
datasets. Furthermore, the results provide further insights into the behaviour
of clustering features for sequence labelling tasks. The system and models
generated in this work are available for public use and to facilitate
reproducibility of results.
| 2,018 | Computation and Language |
Evaluating Word Embedding Models: Methods and Experimental Results | Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular
word embedding models and discuss desired properties of word models and
evaluation methods (or evaluators). Then, we categorize evaluators into
intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a
representation independent of specific natural language processing tasks while
extrinsic evaluators use word embeddings as input features to a downstream task
and measure changes in performance metrics specific to that task. We report
experimental results of intrinsic and extrinsic evaluators on six word
embedding models. It is shown that different evaluators focus on different
aspects of word models, and some are more correlated with natural language
processing tasks. Finally, we adopt correlation analysis to study performance
consistency of extrinsic and intrinsic evalutors.
| 2,019 | Computation and Language |
Analogies Explained: Towards Understanding Word Embeddings | Word embeddings generated by neural network methods such as word2vec (W2V)
are well known to exhibit seemingly linear behaviour, e.g. the embeddings of
analogy "woman is to queen as man is to king" approximately describe a
parallelogram. This property is particularly intriguing since the embeddings
are not trained to achieve it. Several explanations have been proposed, but
each introduces assumptions that do not hold in practice. We derive a
probabilistically grounded definition of paraphrasing that we re-interpret as
word transformation, a mathematical description of "$w_x$ is to $w_y$". From
these concepts we prove existence of linear relationships between W2V-type
embeddings that underlie the analogical phenomenon, identifying explicit error
terms.
| 2,019 | Computation and Language |
A new evaluation framework for topic modeling algorithms based on
synthetic corpora | Topic models are in widespread use in natural language processing and beyond.
Here, we propose a new framework for the evaluation of probabilistic topic
modeling algorithms based on synthetic corpora containing an unambiguously
defined ground truth topic structure. The major innovation of our approach is
the ability to quantify the agreement between the planted and inferred topic
structures by comparing the assigned topic labels at the level of the tokens.
In experiments, our approach yields novel insights about the relative strengths
of topic models as corpus characteristics vary, and the first evidence of an
"undetectable phase" for topic models when the planted structure is weak. We
also establish the practical relevance of the insights gained for synthetic
corpora by predicting the performance of topic modeling algorithms in
classification tasks in real-world corpora.
| 2,019 | Computation and Language |
OpenHowNet: An Open Sememe-based Lexical Knowledge Base | In this paper, we present an open sememe-based lexical knowledge base
OpenHowNet. Based on well-known HowNet, OpenHowNet comprises three components:
core data which is composed of more than 100 thousand senses annotated with
sememes, OpenHowNet Web which gives a brief introduction to OpenHowNet as well
as provides online exhibition of OpenHowNet information, and OpenHowNet API
which includes several useful APIs such as accessing OpenHowNet core data and
drawing sememe tree structures of senses. In the main text, we first give some
backgrounds including definition of sememe and details of HowNet. And then we
introduce some previous HowNet and sememe-based research works. Last but not
least, we detail the constituents of OpenHowNet and their basic features and
functionalities. Additionally, we briefly make a summary and list some future
works.
| 2,019 | Computation and Language |
Glyce: Glyph-vectors for Chinese Character Representations | It is intuitive that NLP tasks for logographic languages like Chinese should
benefit from the use of the glyph information in those languages. However, due
to the lack of rich pictographic evidence in glyphs and the weak generalization
ability of standard computer vision models on character data, an effective way
to utilize the glyph information remains to be found. In this paper, we address
this gap by presenting Glyce, the glyph-vectors for Chinese character
representations. We make three major innovations: (1) We use historical Chinese
scripts (e.g., bronzeware script, seal script, traditional Chinese, etc) to
enrich the pictographic evidence in characters; (2) We design CNN structures
(called tianzege-CNN) tailored to Chinese character image processing; and (3)
We use image-classification as an auxiliary task in a multi-task learning setup
to increase the model's ability to generalize. We show that glyph-based models
are able to consistently outperform word/char ID-based models in a wide range
of Chinese NLP tasks. We are able to set new state-of-the-art results for a
variety of Chinese NLP tasks, including tagging (NER, CWS, POS), sentence pair
classification, single sentence classification tasks, dependency parsing, and
semantic role labeling. For example, the proposed model achieves an F1 score of
80.6 on the OntoNotes dataset of NER, +1.5 over BERT; it achieves an almost
perfect accuracy of 99.8\% on the Fudan corpus for text classification. Code
found at https://github.com/ShannonAI/glyce.
| 2,020 | Computation and Language |
An Arabic Dependency Treebank in the Travel Domain | In this paper we present a dependency treebank of travel domain sentences in
Modern Standard Arabic. The text comes from a translation of the English
equivalent sentences in the Basic Traveling Expressions Corpus. The treebank
dependency representation is in the style of the Columbia Arabic Treebank. The
paper motivates the effort and discusses the construction process and
guidelines. We also present parsing results and discuss the effect of domain
and genre difference on parsing.
| 2,019 | Computation and Language |
Divide and Generate: Neural Generation of Complex Sentences | We propose a task to generate a complex sentence from a simple sentence in
order to amplify various kinds of responses in the database. We first divide a
complex sentence into a main clause and a subordinate clause to learn a
generator model of modifiers, and then use the model to generate a modifier
clause to create a complex sentence from a simple sentence. We present an
automatic evaluation metric to estimate the quality of the models and show that
a pipeline model outperforms an end-to-end model.
| 2,019 | Computation and Language |
TiFi: Taxonomy Induction for Fictional Domains [Extended version] | Taxonomies are important building blocks of structured knowledge bases, and
their construction from text sources and Wikipedia has received much attention.
In this paper we focus on the construction of taxonomies for fictional domains,
using noisy category systems from fan wikis or text extraction as input. Such
fictional domains are archetypes of entity universes that are poorly covered by
Wikipedia, such as also enterprise-specific knowledge bases or highly
specialized verticals. Our fiction-targeted approach, called TiFi, consists of
three phases: (i) category cleaning, by identifying candidate categories that
truly represent classes in the domain of interest, (ii) edge cleaning, by
selecting subcategory relationships that correspond to class subsumption, and
(iii) top-level construction, by mapping classes onto a subset of high-level
WordNet categories. A comprehensive evaluation shows that TiFi is able to
construct taxonomies for a diverse range of fictional domains such as Lord of
the Rings, The Simpsons or Greek Mythology with very high precision and that it
outperforms state-of-the-art baselines for taxonomy induction by a substantial
margin.
| 2,019 | Computation and Language |
Guidelines for creating man-machine multimodal interfaces | Understanding details of human multimodal interaction can elucidate many
aspects of the type of information processing machines must perform to interact
with humans. This article gives an overview of recent findings from Linguistics
regarding the organization of conversation in turns, adjacent pairs,
(dis)preferred responses, (self)repairs, etc. Besides, we describe how multiple
modalities of signs interfere with each other modifying meanings. Then, we
propose an abstract algorithm that describes how a machine can implement a
double-feedback system that can reproduces a human-like face-to-face
interaction by processing various signs, such as verbal, prosodic, facial
expressions, gestures, etc. Multimodal face-to-face interactions enrich the
exchange of information between agents, mainly because these agents are active
all the time by emitting and interpreting signs simultaneously. This article is
not about an untested new computational model. Instead, it translates findings
from Linguistics as guidelines for designs of multimodal man-machine
interfaces. An algorithm is presented. Brought from Linguistics, it is a
description pointing out how human face-to-face interactions work. The
linguistic findings reported here are the first steps towards the integration
of multimodal communication. Some developers involved on interface designs
carry on working on isolated models for interpreting text, grammar, gestures
and facial expressions, neglecting the interwoven between these signs. In
contrast, for linguists working on the state-of-the-art multimodal integration,
the interpretation of separated modalities leads to an incomplete
interpretation, if not to a miscomprehension of information. The algorithm
proposed herein intends to guide man-machine interface designers who want to
integrate multimodal components on face-to-face interactions as close as
possible to those performed between humans.
| 2,020 | Computation and Language |
Pay Less Attention with Lightweight and Dynamic Convolutions | Self-attention is a useful mechanism to build generative models for language
and images. It determines the importance of context elements by comparing each
element to the current time step. In this paper, we show that a very
lightweight convolution can perform competitively to the best reported
self-attention results. Next, we introduce dynamic convolutions which are
simpler and more efficient than self-attention. We predict separate convolution
kernels based solely on the current time-step in order to determine the
importance of context elements. The number of operations required by this
approach scales linearly in the input length, whereas self-attention is
quadratic. Experiments on large-scale machine translation, language modeling
and abstractive summarization show that dynamic convolutions improve over
strong self-attention models. On the WMT'14 English-German test set dynamic
convolutions achieve a new state of the art of 29.7 BLEU.
| 2,019 | Computation and Language |
No Training Required: Exploring Random Encoders for Sentence
Classification | We explore various methods for computing sentence representations from
pre-trained word embeddings without any training, i.e., using nothing but
random parameterizations. Our aim is to put sentence embeddings on more solid
footing by 1) looking at how much modern sentence embeddings gain over random
methods---as it turns out, surprisingly little; and by 2) providing the field
with more appropriate baselines going forward---which are, as it turns out,
quite strong. We also make important observations about proper experimental
protocol for sentence classification evaluation, together with recommendations
for future research.
| 2,019 | Computation and Language |
Universal Dependency Parsing from Scratch | This paper describes Stanford's system at the CoNLL 2018 UD Shared Task. We
introduce a complete neural pipeline system that takes raw text as input, and
performs all tasks required by the shared task, ranging from tokenization and
sentence segmentation, to POS tagging and dependency parsing. Our single system
submission achieved very competitive performance on big treebanks. Moreover,
after fixing an unfortunate bug, our corrected system would have placed the
2nd, 1st, and 3rd on the official evaluation metrics LAS,MLAS, and BLEX, and
would have outperformed all submission systems on low-resource treebank
categories on all metrics by a large margin. We further show the effectiveness
of different model components through extensive ablation studies.
| 2,019 | Computation and Language |
Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built
with Humans in the Loop | We present the Twitter Job/Employment Corpus, a collection of tweets
annotated by a humans-in-the-loop supervised learning framework that integrates
crowdsourcing contributions and expertise on the local community and employment
environment. Previous computational studies of job-related phenomena have used
corpora collected from workplace social media that are hosted internally by the
employers, and so lacks independence from latent job-related coercion and the
broader context that an open domain, general-purpose medium such as Twitter
provides. Our new corpus promises to be a benchmark for the extraction of
job-related topics and advanced analysis and modeling, and can potentially
benefit a wide range of research communities in the future.
| 2,019 | Computation and Language |
End-to-End Knowledge-Routed Relational Dialogue System for Automatic
Diagnosis | Beyond current conversational chatbots or task-oriented dialogue systems that
have attracted increasing attention, we move forward to develop a dialogue
system for automatic medical diagnosis that converses with patients to collect
additional symptoms beyond their self-reports and automatically makes a
diagnosis. Besides the challenges for conversational dialogue systems (e.g.
topic transition coherency and question understanding), automatic medical
diagnosis further poses more critical requirements for the dialogue rationality
in the context of medical knowledge and symptom-disease relations. Existing
dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017)
mostly rely on data-driven learning and cannot be able to encode extra expert
knowledge graph. In this work, we propose an End-to-End Knowledge-routed
Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical
knowledge graph into the topic transition in dialogue management, and makes it
cooperative with natural language understanding and natural language
generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to
manage topic transitions, which integrates a relational refinement branch for
encoding relations among different symptoms and symptom-disease pairs, and a
knowledge-routed graph branch for topic decision-making. Extensive experiments
on a public medical dialogue dataset show our KR-DS significantly beats
state-of-the-art methods (by more than 8% in diagnosis accuracy). We further
show the superiority of our KR-DS on a newly collected medical dialogue system
dataset, which is more challenging retaining original self-reports and
conversational data between patients and doctors.
| 2,019 | Computation and Language |
Effective weakly supervised semantic frame induction using expression
sharing in hierarchical hidden Markov models | We present a framework for the induction of semantic frames from utterances
in the context of an adaptive command-and-control interface. The system is
trained on an individual user's utterances and the corresponding semantic
frames representing controls. During training, no prior information on the
alignment between utterance segments and frame slots and values is available.
In addition, semantic frames in the training data can contain information that
is not expressed in the utterances. To tackle this weakly supervised
classification task, we propose a framework based on Hidden Markov Models
(HMMs). Structural modifications, resulting in a hierarchical HMM, and an
extension called expression sharing are introduced to minimize the amount of
training time and effort required for the user.
The dataset used for the present study is PATCOR, which contains commands
uttered in the context of a vocally guided card game, Patience. Experiments
were carried out on orthographic and phonetic transcriptions of commands,
segmented on different levels of n-gram granularity. The experimental results
show positive effects of all the studied system extensions, with some effect
differences between the different input representations. Moreover, evaluation
experiments on held-out data with the optimal system configuration show that
the extended system is able to achieve high accuracies with relatively small
amounts of training data.
| 2,019 | Computation and Language |
Compositionality for Recursive Neural Networks | Modelling compositionality has been a longstanding area of research in the
field of vector space semantics. The categorical approach to compositionality
maps grammar onto vector spaces in a principled way, but comes under fire for
requiring the formation of very high-dimensional matrices and tensors, and
therefore being computationally infeasible. In this paper I show how a linear
simplification of recursive neural tensor network models can be mapped directly
onto the categorical approach, giving a way of computing the required matrices
and tensors. This mapping suggests a number of lines of research for both
categorical compositional vector space models of meaning and for recursive
neural network models of compositionality.
| 2,019 | Computation and Language |
Reference-less Quality Estimation of Text Simplification Systems | The evaluation of text simplification (TS) systems remains an open challenge.
As the task has common points with machine translation (MT), TS is often
evaluated using MT metrics such as BLEU. However, such metrics require high
quality reference data, which is rarely available for TS. TS has the advantage
over MT of being a monolingual task, which allows for direct comparisons to be
made between the simplified text and its original version. In this paper, we
compare multiple approaches to reference-less quality estimation of
sentence-level text simplification systems, based on the dataset used for the
QATS 2016 shared task. We distinguish three different dimensions:
gram-maticality, meaning preservation and simplicity. We show that n-gram-based
MT metrics such as BLEU and METEOR correlate the most with human judgment of
grammaticality and meaning preservation, whereas simplicity is best evaluated
by basic length-based metrics.
| 2,018 | Computation and Language |
Tensorized Embedding Layers for Efficient Model Compression | The embedding layers transforming input words into real vectors are the key
components of deep neural networks used in natural language processing.
However, when the vocabulary is large, the corresponding weight matrices can be
enormous, which precludes their deployment in a limited resource setting. We
introduce a novel way of parametrizing embedding layers based on the Tensor
Train (TT) decomposition, which allows compressing the model significantly at
the cost of a negligible drop or even a slight gain in performance. We evaluate
our method on a wide range of benchmarks in natural language processing and
analyze the trade-off between performance and compression ratios for a wide
range of architectures, from MLPs to LSTMs and Transformers.
| 2,020 | Computation and Language |
Span Model for Open Information Extraction on Accurate Corpus | Open information extraction (Open IE) is a challenging task especially due to
its brittle data basis. Most of Open IE systems have to be trained on
automatically built corpus and evaluated on inaccurate test set. In this work,
we first alleviate this difficulty from both sides of training and test sets.
For the former, we propose an improved model design to more sufficiently
exploit training dataset. For the latter, we present our accurately
re-annotated benchmark test set (Re-OIE6) according to a series of linguistic
observation and analysis. Then, we introduce a span model instead of previous
adopted sequence labeling formulization for n-ary Open IE. Our newly introduced
model achieves new state-of-the-art performance on both benchmark evaluation
datasets.
| 2,019 | Computation and Language |
A Generalized Language Model in Tensor Space | In the literature, tensors have been effectively used for capturing the
context information in language models. However, the existing methods usually
adopt relatively-low order tensors, which have limited expressive power in
modeling language. Developing a higher-order tensor representation is
challenging, in terms of deriving an effective solution and showing its
generality. In this paper, we propose a language model named Tensor Space
Language Model (TSLM), by utilizing tensor networks and tensor decomposition.
In TSLM, we build a high-dimensional semantic space constructed by the tensor
product of word vectors. Theoretically, we prove that such tensor
representation is a generalization of the n-gram language model. We further
show that this high-order tensor representation can be decomposed to a
recursive calculation of conditional probability for language modeling. The
experimental results on Penn Tree Bank (PTB) dataset and WikiText benchmark
demonstrate the effectiveness of TSLM.
| 2,019 | Computation and Language |
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text
Classification Tasks | We present EDA: easy data augmentation techniques for boosting performance on
text classification tasks. EDA consists of four simple but powerful operations:
synonym replacement, random insertion, random swap, and random deletion. On
five text classification tasks, we show that EDA improves performance for both
convolutional and recurrent neural networks. EDA demonstrates particularly
strong results for smaller datasets; on average, across five datasets, training
with EDA while using only 50% of the available training set achieved the same
accuracy as normal training with all available data. We also performed
extensive ablation studies and suggest parameters for practical use.
| 2,019 | Computation and Language |
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and
Translation | Text attribute transfer aims to automatically rewrite sentences such that
they possess certain linguistic attributes, while simultaneously preserving
their semantic content. This task remains challenging due to a lack of
supervised parallel data. Existing approaches try to explicitly disentangle
content and attribute information, but this is difficult and often results in
poor content-preservation and ungrammaticality. In contrast, we propose a
simpler approach, Iterative Matching and Translation (IMaT), which: (1)
constructs a pseudo-parallel corpus by aligning a subset of semantically
similar sentences from the source and the target corpora; (2) applies a
standard sequence-to-sequence model to learn the attribute transfer; (3)
iteratively improves the learned transfer function by refining imperfections in
the alignment. In sentiment modification and formality transfer tasks, our
method outperforms complex state-of-the-art systems by a large margin. As an
auxiliary contribution, we produce a publicly-available test set with
human-generated transfer references.
| 2,020 | Computation and Language |
Learning Efficient Lexically-Constrained Neural Machine Translation with
External Memory | Recent years has witnessed dramatic progress of neural machine translation
(NMT), however, the method of manually guiding the translation procedure
remains to be better explored. Previous works proposed to handle such problem
through lexcially-constrained beam search in the decoding phase. Unfortunately,
these lexically-constrained beam search methods suffer two fatal disadvantages:
high computational complexity and hard beam search which generates unexpected
translations. In this paper, we propose to learn the ability of
lexically-constrained translation with external memory, which can overcome the
above mentioned disadvantages. For the training process, automatically
extracted phrase pairs are extracted from alignment and sentence parsing, then
further be encoded into an external memory. This memory is then used to provide
lexically-constrained information for training through a memory-attention
machanism. Various experiments are conducted on WMT Chinese to English and
English to German tasks. All the results can demonstrate the effectiveness of
our method.
| 2,019 | Computation and Language |
Adding Interpretable Attention to Neural Translation Models Improves
Word Alignment | Multi-layer models with multiple attention heads per layer provide superior
translation quality compared to simpler and shallower models, but determining
what source context is most relevant to each target word is more challenging as
a result. Therefore, deriving high-accuracy word alignments from the
activations of a state-of-the-art neural machine translation model is an open
challenge. We propose a simple model extension to the Transformer architecture
that makes use of its hidden representations and is restricted to attend solely
on encoder information to predict the next word. It can be trained on bilingual
data without word-alignment information. We further introduce a novel alignment
inference procedure which applies stochastic gradient descent to directly
optimize the attention activations towards a given target word. The resulting
alignments dramatically outperform the naive approach to interpreting
Transformer attention activations, and are comparable to Giza++ on two publicly
available data sets.
| 2,019 | Computation and Language |
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