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Conditioning LSTM Decoder and Bi-directional Attention Based Question
Answering System | Applying neural-networks on Question Answering has gained increasing
popularity in recent years. In this paper, I implemented a model with
Bi-directional attention flow layer, connected with a Multi-layer LSTM encoder,
connected with one start-index decoder and one conditioning end-index decoder.
I introduce a new end-index decoder layer, conditioning on start-index output.
The Experiment shows this has increased model performance by 15.16%. For
prediction, I proposed a new smart-span equation, rewarding both short answer
length and high probability in start-index and end-index, which further
improved the prediction accuracy. The best single model achieves an F1 score of
73.97% and EM score of 64.95% on test set.
| 2,019 | Computation and Language |
English-Bhojpuri SMT System: Insights from the Karaka Model | This thesis has been divided into six chapters namely: Introduction, Karaka
Model and it impacts on Dependency Parsing, LT Resources for Bhojpuri,
English-Bhojpuri SMT System: Experiment, Evaluation of EB-SMT System, and
Conclusion. Chapter one introduces this PhD research by detailing the
motivation of the study, the methodology used for the study and the literature
review of the existing MT related work in Indian Languages. Chapter two talks
of the theoretical background of Karaka and Karaka model. Along with this, it
talks about previous related work. It also discusses the impacts of the Karaka
model in NLP and dependency parsing. It compares Karaka dependency and
Universal Dependency. It also presents a brief idea of the implementation of
these models in the SMT system for English-Bhojpuri language pair.
| 2,019 | Computation and Language |
Comprehensible Context-driven Text Game Playing | In order to train a computer agent to play a text-based computer game, we
must represent each hidden state of the game. A Long Short-Term Memory (LSTM)
model running over observed texts is a common choice for state construction.
However, a normal Deep Q-learning Network (DQN) for such an agent requires
millions of steps of training or more to converge. As such, an LSTM-based DQN
can take tens of days to finish the training process. Though we can use a
Convolutional Neural Network (CNN) as a text-encoder to construct states much
faster than the LSTM, doing so without an understanding of the syntactic
context of the words being analyzed can slow convergence. In this paper, we use
a fast CNN to encode position- and syntax-oriented structures extracted from
observed texts as states. We additionally augment the reward signal in a
universal and practical manner. Together, we show that our improvements can not
only speed up the process by one order of magnitude but also learn a superior
agent.
| 2,019 | Computation and Language |
Caveats in Generating Medical Imaging Labels from Radiology Reports | Acquiring high-quality annotations in medical imaging is usually a costly
process. Automatic label extraction with natural language processing (NLP) has
emerged as a promising workaround to bypass the need of expert annotation.
Despite the convenience, the limitation of such an approximation has not been
carefully examined and is not well understood. With a challenging set of 1,000
chest X-ray studies and their corresponding radiology reports, we show that
there exists a surprisingly large discrepancy between what radiologists
visually perceive and what they clinically report. Furthermore, with inherently
flawed report as ground truth, the state-of-the-art medical NLP fails to
produce high-fidelity labels.
| 2,019 | Computation and Language |
MASS: Masked Sequence to Sequence Pre-training for Language Generation | Pre-training and fine-tuning, e.g., BERT, have achieved great success in
language understanding by transferring knowledge from rich-resource
pre-training task to the low/zero-resource downstream tasks. Inspired by the
success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for
the encoder-decoder based language generation tasks. MASS adopts the
encoder-decoder framework to reconstruct a sentence fragment given the
remaining part of the sentence: its encoder takes a sentence with randomly
masked fragment (several consecutive tokens) as input, and its decoder tries to
predict this masked fragment. In this way, MASS can jointly train the encoder
and decoder to develop the capability of representation extraction and language
modeling. By further fine-tuning on a variety of zero/low-resource language
generation tasks, including neural machine translation, text summarization and
conversational response generation (3 tasks and totally 8 datasets), MASS
achieves significant improvements over the baselines without pre-training or
with other pre-training methods. Specially, we achieve the state-of-the-art
accuracy (37.5 in terms of BLEU score) on the unsupervised English-French
translation, even beating the early attention-based supervised model.
| 2,019 | Computation and Language |
RelExt: Relation Extraction using Deep Learning approaches for
Cybersecurity Knowledge Graph Improvement | Security Analysts that work in a `Security Operations Center' (SoC) play a
major role in ensuring the security of the organization. The amount of
background knowledge they have about the evolving and new attacks makes a
significant difference in their ability to detect attacks. Open source threat
intelligence sources, like text descriptions about cyber-attacks, can be stored
in a structured fashion in a cybersecurity knowledge graph. A cybersecurity
knowledge graph can be paramount in aiding a security analyst to detect cyber
threats because it stores a vast range of cyber threat information in the form
of semantic triples which can be queried. A semantic triple contains two
cybersecurity entities with a relationship between them. In this work, we
propose a system to create semantic triples over cybersecurity text, using deep
learning approaches to extract possible relationships. We use the set of
semantic triples generated through our system to assert in a cybersecurity
knowledge graph. Security Analysts can retrieve this data from the knowledge
graph, and use this information to form a decision about a cyber-attack.
| 2,019 | Computation and Language |
The method of automatic summarization from different sources | In this article is analyzed technology of automatic text abstracting and
annotation. The role of annotation in automatic search and classification for
different scientific articles is described. The algorithm of summarization of
natural language documents using the concept of importance coefficients is
developed. Such concept allows considering the peculiarity of subject areas and
topics that could be found in different kinds of documents. Method for
generating abstracts of single document based on frequency analysis is
developed. The recognition elements for unstructured text analysis are given.
The method of pre-processing analysis of several documents is developed. This
technique simultaneously considers both statistical approaches to abstracting
and the importance of terms in a particular subject domain. The quality of
generated abstract is evaluated. For the developed system there was conducted
experts evaluation. It was held only for texts in Ukrainian. The developed
system concluding essay has higher aggregate score on all criteria. The
summarization system architecture is building. To build an information system
model there is used CASE-tool AllFusion ERwin Data Modeler. The database scheme
for information saving was built. The system is designed to work primarily with
Ukrainian texts, which gives a significant advantage, since most modern systems
still oriented to English texts
| 2,016 | Computation and Language |
Automatic Inference of Minimalist Grammars using an SMT-Solver | We introduce (1) a novel parser for Minimalist Grammars (MG), encoded as a
system of first-order logic formulae that may be evaluated using an SMT-solver,
and (2) a novel procedure for inferring Minimalist Grammars using this parser.
The input to this procedure is a sequence of sentences that have been annotated
with syntactic relations such as semantic role labels (connecting arguments to
predicates) and subject-verb agreement. The output of this procedure is a set
of minimalist grammars, each of which is able to parse the sentences in the
input sequence such that the parse for a sentence has the same syntactic
relations as those specified in the annotation for that sentence. We applied
this procedure to a set of sentences annotated with syntactic relations and
evaluated the inferred grammars using cost functions inspired by the Minimum
Description Length principle and the Subset principle. Inferred grammars that
were optimal with respect to certain combinations of these cost functions were
found to align with contemporary theories of syntax.
| 2,019 | Computation and Language |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word
Representations | Syntax has been demonstrated highly effective in neural machine translation
(NMT). Previous NMT models integrate syntax by representing 1-best tree outputs
from a well-trained parsing system, e.g., the representative Tree-RNN and
Tree-Linearization methods, which may suffer from error propagation. In this
work, we propose a novel method to integrate source-side syntax implicitly for
NMT. The basic idea is to use the intermediate hidden representations of a
well-trained end-to-end dependency parser, which are referred to as
syntax-aware word representations (SAWRs). Then, we simply concatenate such
SAWRs with ordinary word embeddings to enhance basic NMT models. The method can
be straightforwardly integrated into the widely-used sequence-to-sequence
(Seq2Seq) NMT models. We start with a representative RNN-based Seq2Seq baseline
system, and test the effectiveness of our proposed method on two benchmark
datasets of the Chinese-English and English-Vietnamese translation tasks,
respectively. Experimental results show that the proposed approach is able to
bring significant BLEU score improvements on the two datasets compared with the
baseline, 1.74 points for Chinese-English translation and 0.80 point for
English-Vietnamese translation, respectively. In addition, the approach also
outperforms the explicit Tree-RNN and Tree-Linearization methods.
| 2,019 | Computation and Language |
ShapeGlot: Learning Language for Shape Differentiation | In this work we explore how fine-grained differences between the shapes of
common objects are expressed in language, grounded on images and 3D models of
the objects. We first build a large scale, carefully controlled dataset of
human utterances that each refers to a 2D rendering of a 3D CAD model so as to
distinguish it from a set of shape-wise similar alternatives. Using this
dataset, we develop neural language understanding (listening) and production
(speaking) models that vary in their grounding (pure 3D forms via point-clouds
vs. rendered 2D images), the degree of pragmatic reasoning captured (e.g.
speakers that reason about a listener or not), and the neural architecture
(e.g. with or without attention). We find models that perform well with both
synthetic and human partners, and with held out utterances and objects. We also
find that these models are amenable to zero-shot transfer learning to novel
object classes (e.g. transfer from training on chairs to testing on lamps), as
well as to real-world images drawn from furniture catalogs. Lesion studies
indicate that the neural listeners depend heavily on part-related words and
associate these words correctly with visual parts of objects (without any
explicit network training on object parts), and that transfer to novel classes
is most successful when known part-words are available. This work illustrates a
practical approach to language grounding, and provides a case study in the
relationship between object shape and linguistic structure when it comes to
object differentiation.
| 2,019 | Computation and Language |
Emotion Recognition in Conversation: Research Challenges, Datasets, and
Recent Advances | Emotion is intrinsic to humans and consequently emotion understanding is a
key part of human-like artificial intelligence (AI). Emotion recognition in
conversation (ERC) is becoming increasingly popular as a new research frontier
in natural language processing (NLP) due to its ability to mine opinions from
the plethora of publicly available conversational data in platforms such as
Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential
applications in health-care systems (as a tool for psychological analysis),
education (understanding student frustration) and more. Additionally, ERC is
also extremely important for generating emotion-aware dialogues that require an
understanding of the user's emotions. Catering to these needs calls for
effective and scalable conversational emotion-recognition algorithms. However,
it is a strenuous problem to solve because of several research challenges. In
this paper, we discuss these challenges and shed light on the recent research
in this field. We also describe the drawbacks of these approaches and discuss
the reasons why they fail to successfully overcome the research challenges in
ERC.
| 2,019 | Computation and Language |
On the Feasibility of Automated Detection of Allusive Text Reuse | The detection of allusive text reuse is particularly challenging due to the
sparse evidence on which allusive references rely---commonly based on none or
very few shared words. Arguably, lexical semantics can be resorted to since
uncovering semantic relations between words has the potential to increase the
support underlying the allusion and alleviate the lexical sparsity. A further
obstacle is the lack of evaluation benchmark corpora, largely due to the highly
interpretative character of the annotation process. In the present paper, we
aim to elucidate the feasibility of automated allusion detection. We approach
the matter from an Information Retrieval perspective in which referencing texts
act as queries and referenced texts as relevant documents to be retrieved, and
estimate the difficulty of benchmark corpus compilation by a novel
inter-annotator agreement study on query segmentation. Furthermore, we
investigate to what extent the integration of lexical semantic information
derived from distributional models and ontologies can aid retrieving cases of
allusive reuse. The results show that (i) despite low agreement scores, using
manual queries considerably improves retrieval performance with respect to a
windowing approach, and that (ii) retrieval performance can be moderately
boosted with distributional semantics.
| 2,019 | Computation and Language |
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data
Augmentation | We present state-of-the-art automatic speech recognition (ASR) systems
employing a standard hybrid DNN/HMM architecture compared to an attention-based
encoder-decoder design for the LibriSpeech task. Detailed descriptions of the
system development, including model design, pretraining schemes, training
schedules, and optimization approaches are provided for both system
architectures. Both hybrid DNN/HMM and attention-based systems employ
bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we
employ both LSTM and Transformer based architectures. All our systems are built
using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the
authors, the results obtained when training on the full LibriSpeech training
set, are the best published currently, both for the hybrid DNN/HMM and the
attention-based systems. Our single hybrid system even outperforms previous
results obtained from combining eight single systems. Our comparison shows that
on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the
attention-based system by 15% relative on the clean and 40% relative on the
other test sets in terms of word error rate. Moreover, experiments on a reduced
100h-subset of the LibriSpeech training corpus even show a more pronounced
margin between the hybrid DNN/HMM and attention-based architectures.
| 2,019 | Computation and Language |
Unified Language Model Pre-training for Natural Language Understanding
and Generation | This paper presents a new Unified pre-trained Language Model (UniLM) that can
be fine-tuned for both natural language understanding and generation tasks. The
model is pre-trained using three types of language modeling tasks:
unidirectional, bidirectional, and sequence-to-sequence prediction. The unified
modeling is achieved by employing a shared Transformer network and utilizing
specific self-attention masks to control what context the prediction conditions
on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0
and CoQA question answering tasks. Moreover, UniLM achieves new
state-of-the-art results on five natural language generation datasets,
including improving the CNN/DailyMail abstractive summarization ROUGE-L to
40.51 (2.04 absolute improvement), the Gigaword abstractive summarization
ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question
answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question
generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7
document-grounded dialog response generation NIST-4 to 2.67 (human performance
is 2.65). The code and pre-trained models are available at
https://github.com/microsoft/unilm.
| 2,019 | Computation and Language |
Targeted Sentiment Analysis: A Data-Driven Categorization | Targeted sentiment analysis (TSA), also known as aspect based sentiment
analysis (ABSA), aims at detecting fine-grained sentiment polarity towards
targets in a given opinion document. Due to the lack of labeled datasets and
effective technology, TSA had been intractable for many years. The newly
released datasets and the rapid development of deep learning technologies are
key enablers for the recent significant progress made in this area. However,
the TSA tasks have been defined in various ways with different understandings
towards basic concepts like `target' and `aspect'. In this paper, we categorize
the different tasks and highlight the differences in the available datasets and
their specific tasks. We then further discuss the challenges related to data
collection and data annotation which are overlooked in many previous studies.
| 2,019 | Computation and Language |
MobiVSR: A Visual Speech Recognition Solution for Mobile Devices | Visual speech recognition (VSR) is the task of recognizing spoken language
from video input only, without any audio. VSR has many applications as an
assistive technology, especially if it could be deployed in mobile devices and
embedded systems. The need of intensive computational resources and large
memory footprint are two of the major obstacles in developing neural network
models for VSR in a resource constrained environment. We propose a novel
end-to-end deep neural network architecture for word level VSR called MobiVSR
with a design parameter that aids in balancing the model's accuracy and
parameter count. We use depthwise-separable 3D convolution for the first time
in the domain of VSR and show how it makes our model efficient. MobiVSR
achieves an accuracy of 73\% on a challenging Lip Reading in the Wild dataset
with 6 times fewer parameters and 20 times lesser memory footprint than the
current state of the art. MobiVSR can also be compressed to 6 MB by applying
post training quantization.
| 2,019 | Computation and Language |
Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network | The Legal Judgment Prediction (LJP) is to determine judgment results based on
the fact descriptions of the cases. LJP usually consists of multiple subtasks,
such as applicable law articles prediction, charges prediction, and the term of
the penalty prediction. These multiple subtasks have topological dependencies,
the results of which affect and verify each other. However, existing methods
use dependencies of results among multiple subtasks inefficiently. Moreover,
for cases with similar descriptions but different penalties, current methods
cannot predict accurately because the word collocation information is ignored.
In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word
Collocation Attention mechanism based on the topology structure among subtasks.
Specifically, we design a multi-perspective forward prediction and backward
verification framework to utilize result dependencies among multiple subtasks
effectively. To distinguish cases with similar descriptions but different
penalties, we integrate word collocations features of fact descriptions into
the network via an attention mechanism. The experimental results show our model
achieves significant improvements over baselines on all prediction tasks.
| 2,019 | Computation and Language |
Restoring Arabic vowels through omission-tolerant dictionary lookup | Vowels in Arabic are optional orthographic symbols written as diacritics
above or below letters. In Arabic texts, typically more than 97 percent of
written words do not explicitly show any of the vowels they contain; that is to
say, depending on the author, genre and field, less than 3 percent of words
include any explicit vowel. Although numerous studies have been published on
the issue of restoring the omitted vowels in speech technologies, little
attention has been given to this problem in papers dedicated to written Arabic
technologies.f In this research, we present Arabic-Unitex, an Arabic Language
Resource, with emphasis on vowel representation and encoding. Specifically, we
present two dozens of rules formalizing a detailed description of vowel
omission in written text. They are typographical rules integrated into
large-coverage resources for morphological annotation. For restoring vowels,
our resources are capable of identifying words in which the vowels are not
shown, as well as words in which the vowels are partially or fully included. By
taking into account these rules, our resources are able to compute and restore
for each word form a list of compatible fully vowelized candidates through
omission-tolerant dictionary lookup. Our program performs the analysis of 5000
words/second for running text (20 pages/second). Based on these comprehensive
linguistic resources, we created a spell checker that detects any
invalid/misplaced vowel in a fully or partially vowelized form. Finally, our
resources provide a lexical coverage of more than 99 percent of the words used
in popular newspapers, and restore vowels in words (out of context) simply and
efficiently.
| 2,019 | Computation and Language |
Survey on Evaluation Methods for Dialogue Systems | In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class.
| 2,020 | Computation and Language |
Language Modeling with Deep Transformers | We explore deep autoregressive Transformer models in language modeling for
speech recognition. We focus on two aspects. First, we revisit Transformer
model configurations specifically for language modeling. We show that well
configured Transformer models outperform our baseline models based on the
shallow stack of LSTM recurrent neural network layers. We carry out experiments
on the open-source LibriSpeech 960hr task, for both 200K vocabulary word-level
and 10K byte-pair encoding subword-level language modeling. We apply our
word-level models to conventional hybrid speech recognition by lattice
rescoring, and the subword-level models to attention based encoder-decoder
models by shallow fusion. Second, we show that deep Transformer language models
do not require positional encoding. The positional encoding is an essential
augmentation for the self-attention mechanism which is invariant to sequence
ordering. However, in autoregressive setup, as is the case for language
modeling, the amount of information increases along the position dimension,
which is a positional signal by its own. The analysis of attention weights
shows that deep autoregressive self-attention models can automatically make use
of such positional information. We find that removing the positional encoding
even slightly improves the performance of these models.
| 2,019 | Computation and Language |
Semantic categories of artifacts and animals reflect efficient coding | It has been argued that semantic categories across languages reflect pressure
for efficient communication. Recently, this idea has been cast in terms of a
general information-theoretic principle of efficiency, the Information
Bottleneck (IB) principle, and it has been shown that this principle accounts
for the emergence and evolution of named color categories across languages,
including soft structure and patterns of inconsistent naming. However, it is
not yet clear to what extent this account generalizes to semantic domains other
than color. Here we show that it generalizes to two qualitatively different
semantic domains: names for containers, and for animals. First, we show that
container naming in Dutch and French is near-optimal in the IB sense, and that
IB broadly accounts for soft categories and inconsistent naming patterns in
both languages. Second, we show that a hierarchy of animal categories derived
from IB captures cross-linguistic tendencies in the growth of animal
taxonomies. Taken together, these findings suggest that fundamental
information-theoretic principles of efficient coding may shape semantic
categories across languages and across domains.
| 2,019 | Computation and Language |
Improving Natural Language Interaction with Robots Using Advice | Over the last few years, there has been growing interest in learning models
for physically grounded language understanding tasks, such as the popular
blocks world domain. These works typically view this problem as a single-step
process, in which a human operator gives an instruction and an automated agent
is evaluated on its ability to execute it. In this paper we take the first step
towards increasing the bandwidth of this interaction, and suggest a protocol
for including advice, high-level observations about the task, which can help
constrain the agent's prediction. We evaluate our approach on the blocks world
task, and show that even simple advice can help lead to significant performance
improvements. To help reduce the effort involved in supplying the advice, we
also explore model self-generated advice which can still improve results.
| 2,019 | Computation and Language |
A joint text mining-rank size investigation of the rhetoric structures
of the US Presidents' speeches | This work presents a text mining context and its use for a deep analysis of
the messages delivered by the politicians. Specifically, we deal with an expert
systems-based exploration of the rhetoric dynamics of a large collection of US
Presidents' speeches, ranging from Washington to Trump. In particular, speeches
are viewed as complex expert systems whose structures can be effectively
analyzed through rank-size laws. The methodological contribution of the paper
is twofold. First, we develop a text mining-based procedure for the
construction of the dataset by using a web scraping routine on the Miller
Center website -- the repository collecting the speeches. Second, we explore
the implicit structure of the discourse data by implementing a rank-size
procedure over the individual speeches, being the words of each speech ranked
in terms of their frequencies. The scientific significance of the proposed
combination of text-mining and rank-size approaches can be found in its
flexibility and generality, which let it be reproducible to a wide set of
expert systems and text mining contexts. The usefulness of the proposed method
and the speech subsequent analysis is demonstrated by the findings themselves.
Indeed, in terms of impact, it is worth noting that interesting conclusions of
social, political and linguistic nature on how 45 United States Presidents,
from April 30, 1789 till February 28, 2017 delivered political messages can be
carried out. Indeed, the proposed analysis shows some remarkable regularities,
not only inside a given speech, but also among different speeches. Moreover,
under a purely methodological perspective, the presented contribution suggests
possible ways of generating a linguistic decision-making algorithm.
| 2,019 | Computation and Language |
A Comparison of Techniques for Sentiment Classification of Film Reviews | We undertake the task of comparing lexicon-based sentiment classification of
film reviews with machine learning approaches. We look at existing
methodologies and attempt to emulate and improve on them using a 'given'
lexicon and a bag-of-words approach. We also utilise syntactical information
such as part-of-speech and dependency relations. We will show that a simple
lexicon-based classification achieves good results however machine learning
techniques prove to be the superior tool. We also show that more features do
not necessarily deliver better performance as well as elaborate on three
further enhancements not tested in this article.
| 2,019 | Computation and Language |
A Benchmark Study of Machine Learning Models for Online Fake News
Detection | The proliferation of fake news and its propagation on social media has become
a major concern due to its ability to create devastating impacts. Different
machine learning approaches have been suggested to detect fake news. However,
most of those focused on a specific type of news (such as political) which
leads us to the question of dataset-bias of the models used. In this research,
we conducted a benchmark study to assess the performance of different
applicable machine learning approaches on three different datasets where we
accumulated the largest and most diversified one. We explored a number of
advanced pre-trained language models for fake news detection along with the
traditional and deep learning ones and compared their performances from
different aspects for the first time to the best of our knowledge. We find that
BERT and similar pre-trained models perform the best for fake news detection,
especially with very small dataset. Hence, these models are significantly
better option for languages with limited electronic contents, i.e., training
data. We also carried out several analysis based on the models' performance,
article's topic, article's length, and discussed different lessons learned from
them. We believe that this benchmark study will help the research community to
explore further and news sites/blogs to select the most appropriate fake news
detection method.
| 2,021 | Computation and Language |
Synchronous Bidirectional Neural Machine Translation | Existing approaches to neural machine translation (NMT) generate the target
language sequence token by token from left to right. However, this kind of
unidirectional decoding framework cannot make full use of the target-side
future contexts which can be produced in a right-to-left decoding direction,
and thus suffers from the issue of unbalanced outputs. In this paper, we
introduce a synchronous bidirectional neural machine translation (SB-NMT) that
predicts its outputs using left-to-right and right-to-left decoding
simultaneously and interactively, in order to leverage both of the history and
future information at the same time. Specifically, we first propose a new
algorithm that enables synchronous bidirectional decoding in a single model.
Then, we present an interactive decoding model in which left-to-right
(right-to-left) generation does not only depend on its previously generated
outputs, but also relies on future contexts predicted by right-to-left
(left-to-right) decoding. We extensively evaluate the proposed SB-NMT model on
large-scale NIST Chinese-English, WMT14 English-German, and WMT18
Russian-English translation tasks. Experimental results demonstrate that our
model achieves significant improvements over the strong Transformer model by
3.92, 1.49 and 1.04 BLEU points respectively, and obtains the state-of-the-art
performance on Chinese-English and English-German translation tasks.
| 2,019 | Computation and Language |
Modelling Instance-Level Annotator Reliability for Natural Language
Labelling Tasks | When constructing models that learn from noisy labels produced by multiple
annotators, it is important to accurately estimate the reliability of
annotators. Annotators may provide labels of inconsistent quality due to their
varying expertise and reliability in a domain. Previous studies have mostly
focused on estimating each annotator's overall reliability on the entire
annotation task. However, in practice, the reliability of an annotator may
depend on each specific instance. Only a limited number of studies have
investigated modelling per-instance reliability and these only considered
binary labels. In this paper, we propose an unsupervised model which can handle
both binary and multi-class labels. It can automatically estimate the
per-instance reliability of each annotator and the correct label for each
instance. We specify our model as a probabilistic model which incorporates
neural networks to model the dependency between latent variables and instances.
For evaluation, the proposed method is applied to both synthetic and real data,
including two labelling tasks: text classification and textual entailment.
Experimental results demonstrate our novel method can not only accurately
estimate the reliability of annotators across different instances, but also
achieve superior performance in predicting the correct labels and detecting the
least reliable annotators compared to state-of-the-art baselines.
| 2,019 | Computation and Language |
A Review of Keyphrase Extraction | Keyphrase extraction is a textual information processing task concerned with
the automatic extraction of representative and characteristic phrases from a
document that express all the key aspects of its content. Keyphrases constitute
a succinct conceptual summary of a document, which is very useful in digital
information management systems for semantic indexing, faceted search, document
clustering and classification. This article introduces keyphrase extraction,
provides a well-structured review of the existing work, offers interesting
insights on the different evaluation approaches, highlights open issues and
presents a comparative experimental study of popular unsupervised techniques on
five datasets.
| 2,019 | Computation and Language |
Towards Content Transfer through Grounded Text Generation | Recent work in neural generation has attracted significant interest in
controlling the form of text, such as style, persona, and politeness. However,
there has been less work on controlling neural text generation for content.
This paper introduces the notion of Content Transfer for long-form text
generation, where the task is to generate a next sentence in a document that
both fits its context and is grounded in a content-rich external textual source
such as a news story. Our experiments on Wikipedia data show significant
improvements against competitive baselines. As another contribution of this
paper, we release a benchmark dataset of 640k Wikipedia referenced sentences
paired with the source articles to encourage exploration of this new task.
| 2,019 | Computation and Language |
A Brief Survey of Multilingual Neural Machine Translation | We present a survey on multilingual neural machine translation (MNMT), which
has gained a lot of traction in the recent years. MNMT has been useful in
improving translation quality as a result of knowledge transfer. MNMT is more
promising and interesting than its statistical machine translation counterpart
because end-to-end modeling and distributed representations open new avenues.
Many approaches have been proposed in order to exploit multilingual parallel
corpora for improving translation quality. However, the lack of a comprehensive
survey makes it difficult to determine which approaches are promising and hence
deserve further exploration. In this paper, we present an in-depth survey of
existing literature on MNMT. We categorize various approaches based on the
resource scenarios as well as underlying modeling principles. We hope this
paper will serve as a starting point for researchers and engineers interested
in MNMT.
| 2,020 | Computation and Language |
On the number of k-skip-n-grams | The paper proves that the number of k-skip-n-grams for a corpus of size $L$
is $$\frac{Ln + n + k' - n^2 - nk'}{n} \cdot \binom{n-1+k'}{n-1}$$ where $k' =
\min(L - n + 1, k)$.
| 2,019 | Computation and Language |
Cognitive Graph for Multi-Hop Reading Comprehension at Scale | We propose a new CogQA framework for multi-hop question answering in
web-scale documents. Inspired by the dual process theory in cognitive science,
the framework gradually builds a \textit{cognitive graph} in an iterative
process by coordinating an implicit extraction module (System 1) and an
explicit reasoning module (System 2). While giving accurate answers, our
framework further provides explainable reasoning paths. Specifically, our
implementation based on BERT and graph neural network efficiently handles
millions of documents for multi-hop reasoning questions in the HotpotQA
fullwiki dataset, achieving a winning joint $F_1$ score of 34.9 on the
leaderboard, compared to 23.6 of the best competitor.
| 2,019 | Computation and Language |
Improving Neural Conversational Models with Entropy-Based Data Filtering | Current neural network-based conversational models lack diversity and
generate boring responses to open-ended utterances. Priors such as persona,
emotion, or topic provide additional information to dialog models to aid
response generation, but annotating a dataset with priors is expensive and such
annotations are rarely available. While previous methods for improving the
quality of open-domain response generation focused on either the underlying
model or the training objective, we present a method of filtering dialog
datasets by removing generic utterances from training data using a simple
entropy-based approach that does not require human supervision. We conduct
extensive experiments with different variations of our method, and compare
dialog models across 17 evaluation metrics to show that training on datasets
filtered this way results in better conversational quality as chatbots learn to
output more diverse responses.
| 2,019 | Computation and Language |
Effective Cross-lingual Transfer of Neural Machine Translation Models
without Shared Vocabularies | Transfer learning or multilingual model is essential for low-resource neural
machine translation (NMT), but the applicability is limited to cognate
languages by sharing their vocabularies. This paper shows effective techniques
to transfer a pre-trained NMT model to a new, unrelated language without shared
vocabularies. We relieve the vocabulary mismatch by using cross-lingual word
embedding, train a more language-agnostic encoder by injecting artificial
noises, and generate synthetic data easily from the pre-training data without
back-translation. Our methods do not require restructuring the vocabulary or
retraining the model. We improve plain NMT transfer by up to +5.1% BLEU in five
low-resource translation tasks, outperforming multilingual joint training by a
large margin. We also provide extensive ablation studies on pre-trained
embedding, synthetic data, vocabulary size, and parameter freezing for a better
understanding of NMT transfer.
| 2,019 | Computation and Language |
Deep Residual Output Layers for Neural Language Generation | Many tasks, including language generation, benefit from learning the
structure of the output space, particularly when the space of output labels is
large and the data is sparse. State-of-the-art neural language models
indirectly capture the output space structure in their classifier weights since
they lack parameter sharing across output labels. Learning shared output label
mappings helps, but existing methods have limited expressivity and are prone to
overfitting. In this paper, we investigate the usefulness of more powerful
shared mappings for output labels, and propose a deep residual output mapping
with dropout between layers to better capture the structure of the output space
and avoid overfitting. Evaluations on three language generation tasks show that
our output label mapping can match or improve state-of-the-art recurrent and
self-attention architectures, and suggest that the classifier does not
necessarily need to be high-rank to better model natural language if it is
better at capturing the structure of the output space.
| 2,019 | Computation and Language |
Is Word Segmentation Necessary for Deep Learning of Chinese
Representations? | Segmenting a chunk of text into words is usually the first step of processing
Chinese text, but its necessity has rarely been explored. In this paper, we ask
the fundamental question of whether Chinese word segmentation (CWS) is
necessary for deep learning-based Chinese Natural Language Processing. We
benchmark neural word-based models which rely on word segmentation against
neural char-based models which do not involve word segmentation in four
end-to-end NLP benchmark tasks: language modeling, machine translation,
sentence matching/paraphrase and text classification. Through direct
comparisons between these two types of models, we find that char-based models
consistently outperform word-based models. Based on these observations, we
conduct comprehensive experiments to study why word-based models underperform
char-based models in these deep learning-based NLP tasks. We show that it is
because word-based models are more vulnerable to data sparsity and the presence
of out-of-vocabulary (OOV) words, and thus more prone to overfitting. We hope
this paper could encourage researchers in the community to rethink the
necessity of word segmentation in deep learning-based Chinese Natural Language
Processing. \footnote{Yuxian Meng and Xiaoya Li contributed equally to this
paper.}
| 2,019 | Computation and Language |
Entity-Relation Extraction as Multi-Turn Question Answering | In this paper, we propose a new paradigm for the task of entity-relation
extraction. We cast the task as a multi-turn question answering problem, i.e.,
the extraction of entities and relations is transformed to the task of
identifying answer spans from the context. This multi-turn QA formalization
comes with several key advantages: firstly, the question query encodes
important information for the entity/relation class we want to identify;
secondly, QA provides a natural way of jointly modeling entity and relation;
and thirdly, it allows us to exploit the well developed machine reading
comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora
demonstrate that the proposed paradigm significantly outperforms previous best
models. We are able to obtain the state-of-the-art results on all of the ACE04,
ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets
to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively. Additionally, we
construct a newly developed dataset RESUME in Chinese, which requires
multi-step reasoning to construct entity dependencies, as opposed to the
single-step dependency extraction in the triplet exaction in previous datasets.
The proposed multi-turn QA model also achieves the best performance on the
RESUME dataset.
| 2,019 | Computation and Language |
Atom Responding Machine for Dialog Generation | Recently, improving the relevance and diversity of dialogue system has
attracted wide attention. For a post x, the corresponding response y is usually
diverse in the real-world corpus, while the conventional encoder-decoder model
tends to output the high-frequency (safe but trivial) responses and thus is
difficult to handle the large number of responding styles. To address these
issues, we propose the Atom Responding Machine (ARM), which is based on a
proposed encoder-composer-decoder network trained by a teacher-student
framework. To enrich the generated responses, ARM introduces a large number of
molecule-mechanisms as various responding styles, which are conducted by taking
different combinations from a few atom-mechanisms. In other words, even a
little of atom-mechanisms can make a mickle of molecule-mechanisms. The
experiments demonstrate diversity and quality of the responses generated by
ARM. We also present generating process to show underlying interpretability for
the result.
| 2,019 | Computation and Language |
Assessing the Difficulty of Classifying ConceptNet Relations in a
Multi-Label Classification Setting | Commonsense knowledge relations are crucial for advanced NLU tasks. We
examine the learnability of such relations as represented in CONCEPTNET, taking
into account their specific properties, which can make relation classification
difficult: a given concept pair can be linked by multiple relation types, and
relations can have multi-word arguments of diverse semantic types. We explore a
neural open world multi-label classification approach that focuses on the
evaluation of classification accuracy for individual relations. Based on an
in-depth study of the specific properties of the CONCEPTNET resource, we
investigate the impact of different relation representations and model
variations. Our analysis reveals that the complexity of argument types and
relation ambiguity are the most important challenges to address. We design a
customized evaluation method to address the incompleteness of the resource that
can be expanded in future work.
| 2,019 | Computation and Language |
The Language of Legal and Illegal Activity on the Darknet | The non-indexed parts of the Internet (the Darknet) have become a haven for
both legal and illegal anonymous activity. Given the magnitude of these
networks, scalably monitoring their activity necessarily relies on automated
tools, and notably on NLP tools. However, little is known about what
characteristics texts communicated through the Darknet have, and how well
off-the-shelf NLP tools do on this domain. This paper tackles this gap and
performs an in-depth investigation of the characteristics of legal and illegal
text in the Darknet, comparing it to a clear net website with similar content
as a control condition. Taking drug-related websites as a test case, we find
that texts for selling legal and illegal drugs have several linguistic
characteristics that distinguish them from one another, as well as from the
control condition, among them the distribution of POS tags, and the coverage of
their named entities in Wikipedia.
| 2,019 | Computation and Language |
A Dynamic Evolutionary Framework for Timeline Generation based on
Distributed Representations | Given the collection of timestamped web documents related to the evolving
topic, timeline summarization (TS) highlights its most important events in the
form of relevant summaries to represent the development of a topic over time.
Most of the previous work focuses on fully-observable ranking models and
depends on hand-designed features or complex mechanisms that may not generalize
well. We present a novel dynamic framework for evolutionary timeline generation
leveraging distributed representations, which dynamically finds the most likely
sequence of evolutionary summaries in the timeline, called the Viterbi
timeline, and reduces the impact of events that irrelevant or repeated to the
topic. The assumptions of the coherence and the global view run through our
model. We explore adjacent relevance to constrain timeline coherence and make
sure the events evolve on the same topic with a global view. Experimental
results demonstrate that our framework is feasible to extract summaries for
timeline generation, outperforms various competitive baselines, and achieves
the state-of-the-art performance as an unsupervised approach.
| 2,019 | Computation and Language |
How to Fine-Tune BERT for Text Classification? | Language model pre-training has proven to be useful in learning universal
language representations. As a state-of-the-art language model pre-training
model, BERT (Bidirectional Encoder Representations from Transformers) has
achieved amazing results in many language understanding tasks. In this paper,
we conduct exhaustive experiments to investigate different fine-tuning methods
of BERT on text classification task and provide a general solution for BERT
fine-tuning. Finally, the proposed solution obtains new state-of-the-art
results on eight widely-studied text classification datasets.
| 2,020 | Computation and Language |
Transfer Learning for Scientific Data Chain Extraction in Small Chemical
Corpus with BERT-CRF Model | Computational chemistry develops fast in recent years due to the rapid growth
and breakthroughs in AI. Thanks for the progress in natural language
processing, researchers can extract more fine-grained knowledge in publications
to stimulate the development in computational chemistry. While the works and
corpora in chemical entity extraction have been restricted in the biomedicine
or life science field instead of the chemistry field, we build a new corpus in
chemical bond field annotated for 7 types of entities: compound, solvent,
method, bond, reaction, pKa and pKa value. This paper presents a novel BERT-CRF
model to build scientific chemical data chains by extracting 7 chemical
entities and relations from publications. And we propose a joint model to
extract the entities and relations simultaneously. Experimental results on our
Chemical Special Corpus demonstrate that we achieve state-of-art and
competitive NER performance.
| 2,019 | Computation and Language |
Style Transformer: Unpaired Text Style Transfer without Disentangled
Latent Representation | Disentangling the content and style in the latent space is prevalent in
unpaired text style transfer. However, two major issues exist in most of the
current neural models. 1) It is difficult to completely strip the style
information from the semantics for a sentence. 2) The recurrent neural network
(RNN) based encoder and decoder, mediated by the latent representation, cannot
well deal with the issue of the long-term dependency, resulting in poor
preservation of non-stylistic semantic content. In this paper, we propose the
Style Transformer, which makes no assumption about the latent representation of
source sentence and equips the power of attention mechanism in Transformer to
achieve better style transfer and better content preservation.
| 2,019 | Computation and Language |
Meta-Learning for Low-resource Natural Language Generation in
Task-oriented Dialogue Systems | Natural language generation (NLG) is an essential component of task-oriented
dialogue systems. Despite the recent success of neural approaches for NLG, they
are typically developed for particular domains with rich annotated training
examples. In this paper, we study NLG in a low-resource setting to generate
sentences in new scenarios with handful training examples. We formulate the
problem from a meta-learning perspective, and propose a generalized
optimization-based approach (Meta-NLG) based on the well-recognized
model-agnostic meta-learning (MAML) algorithm. Meta-NLG defines a set of meta
tasks, and directly incorporates the objective of adapting to new low-resource
NLG tasks into the meta-learning optimization process. Extensive experiments
are conducted on a large multi-domain dataset (MultiWoz) with diverse
linguistic variations. We show that Meta-NLG significantly outperforms other
training procedures in various low-resource configurations. We analyze the
results, and demonstrate that Meta-NLG adapts extremely fast and well to
low-resource situations.
| 2,019 | Computation and Language |
Sense Vocabulary Compression through the Semantic Knowledge of WordNet
for Neural Word Sense Disambiguation | In this article, we tackle the issue of the limited quantity of manually
sense annotated corpora for the task of word sense disambiguation, by
exploiting the semantic relationships between senses such as synonymy,
hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton
WordNet, and thus reduce the number of different sense tags that must be
observed to disambiguate all words of the lexical database. We propose two
different methods that greatly reduces the size of neural WSD models, with the
benefit of improving their coverage without additional training data, and
without impacting their precision. In addition to our method, we present a WSD
system which relies on pre-trained BERT word vectors in order to achieve
results that significantly outperform the state of the art on all WSD
evaluation tasks.
| 2,019 | Computation and Language |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing | We propose an efficient neural framework for sentence-level discourse
analysis in accordance with Rhetorical Structure Theory (RST). Our framework
comprises a discourse segmenter to identify the elementary discourse units
(EDU) in a text, and a discourse parser that constructs a discourse tree in a
top-down fashion. Both the segmenter and the parser are based on Pointer
Networks and operate in linear time. Our segmenter yields an $F_1$ score of
95.4, and our parser achieves an $F_1$ score of 81.7 on the aggregated labeled
(relation) metric, surpassing previous approaches by a good margin and
approaching human agreement on both tasks (98.3 and 83.0 $F_1$).
| 2,019 | Computation and Language |
SuperChat: Dialogue Generation by Transfer Learning from Vision to
Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN
Models | The recent work of Super Characters method using two-dimensional word
embedding achieved state-of-the-art results in text classification tasks,
showcasing the promise of this new approach. This paper borrows the idea of
Super Characters method and two-dimensional embedding, and proposes a method of
generating conversational response for open domain dialogues. The experimental
results on a public dataset shows that the proposed SuperChat method generates
high quality responses. An interactive demo is ready to show at the workshop.
| 2,019 | Computation and Language |
Development of Deep Learning Based Natural Language Processing Model for
Turkish | Natural language is one of the most fundamental features that distinguish
people from other living things and enable people to communicate each other.
Language is a tool that enables people to express their feelings and thoughts
and to transfers cultures through generations. Texts and audio are examples of
natural language in daily life. In the natural language, many words disappear
in time, on the other hand new words are derived. Therefore, while the process
of natural language processing (NLP) is complex even for human, it is difficult
to process in computer system. The area of linguistics examines how people use
language. NLP, which requires the collaboration of linguists and computer
scientists, plays an important role in human computer interaction. Studies in
NLP have increased with the use of artificial intelligence technologies in the
field of linguistics. With the deep learning methods which are one of the
artificial intelligence study areas, platforms close to natural language are
being developed. Developed platforms for language comprehension, machine
translation and part of speech (POS) tagging benefit from deep learning
methods. Recurrent Neural Network (RNN), one of the deep learning
architectures, is preferred for processing sequential data such as text or
audio data. In this study, Turkish POS tagging model has been proposed by using
Bidirectional Long-Short Term Memory (BLSTM) which is an RNN type. The proposed
POS tagging model is provided to natural language researchers with a platform
that allows them to perform and use their own analysis. In the development
phase of the platform developed by using BLSTM, the error rate of the POS
tagger has been reduced by taking feedback with expert opinion.
| 2,019 | Computation and Language |
Learning meters of Arabic and English poems with Recurrent Neural
Networks: a step forward for language understanding and synthesis | Recognizing a piece of writing as a poem or prose is usually easy for the
majority of people; however, only specialists can determine which meter a poem
belongs to. In this paper, we build Recurrent Neural Network (RNN) models that
can classify poems according to their meters from plain text. The input text is
encoded at the character level and directly fed to the models without feature
handcrafting. This is a step forward for machine understanding and synthesis of
languages in general, and Arabic language in particular. Among the 16 poem
meters of Arabic and the 4 meters of English the networks were able to
correctly classify poem with an overall accuracy of 96.38\% and 82.31\%
respectively. The poem datasets used to conduct this research were massive,
over 1.5 million of verses, and were crawled from different nontechnical
sources, almost Arabic and English literature sites, and in different
heterogeneous and unstructured formats. These datasets are now made publicly
available in clean, structured, and documented format for other future
research. To the best of the authors' knowledge, this research is the first to
address classifying poem meters in a machine learning approach, in general, and
in RNN featureless based approach, in particular. In addition, the dataset is
the first publicly available dataset ready for the purpose of future
computational research.
| 2,019 | Computation and Language |
Modeling user context for valence prediction from narratives | Automated prediction of valence, one key feature of a person's emotional
state, from individuals' personal narratives may provide crucial information
for mental healthcare (e.g. early diagnosis of mental diseases, supervision of
disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect
challenge, the task of valence prediction was framed as a three-class
classification problem using 8 seconds fragments from individuals' narratives.
As such, the task did not allow for exploring contextual information of the
narratives. In this work, we investigate the intrinsic information from
multiple narratives recounted by the same individual in order to predict their
current state-of-mind. Furthermore, with generalizability in mind, we decided
to focus our experiments exclusively on textual information as the public
availability of audio narratives is limited compared to text. Our hypothesis
is, that context modeling might provide insights about emotion triggering
concepts (e.g. events, people, places) mentioned in the narratives that are
linked to an individual's state of mind. We explore multiple machine learning
techniques to model narratives. We find that the models are able to capture
inter-individual differences, leading to more accurate predictions of an
individual's emotional state, as compared to single narratives.
| 2,019 | Computation and Language |
Sparse Sequence-to-Sequence Models | Sequence-to-sequence models are a powerful workhorse of NLP. Most variants
employ a softmax transformation in both their attention mechanism and output
layer, leading to dense alignments and strictly positive output probabilities.
This density is wasteful, making models less interpretable and assigning
probability mass to many implausible outputs. In this paper, we propose sparse
sequence-to-sequence models, rooted in a new family of $\alpha$-entmax
transformations, which includes softmax and sparsemax as particular cases, and
is sparse for any $\alpha > 1$. We provide fast algorithms to evaluate these
transformations and their gradients, which scale well for large vocabulary
sizes. Our models are able to produce sparse alignments and to assign nonzero
probability to a short list of plausible outputs, sometimes rendering beam
search exact. Experiments on morphological inflection and machine translation
reveal consistent gains over dense models.
| 2,019 | Computation and Language |
A logical-based corpus for cross-lingual evaluation | At present, different deep learning models are presenting high accuracy on
popular inference datasets such as SNLI, MNLI, and SciTail. However, there are
different indicators that those datasets can be exploited by using some simple
linguistic patterns. This fact poses difficulties to our understanding of the
actual capacity of machine learning models to solve the complex task of textual
inference. We propose a new set of syntactic tasks focused on contradiction
detection that require specific capacities over linguistic logical forms such
as: Boolean coordination, quantifiers, definite description, and counting
operators. We evaluate two kinds of deep learning models that implicitly
exploit language structure: recurrent models and the Transformer network BERT.
We show that although BERT is clearly more efficient to generalize over most
logical forms, there is space for improvement when dealing with counting
operators. Since the syntactic tasks can be implemented in different languages,
we show a successful case of cross-lingual transfer learning between English
and Portuguese.
| 2,019 | Computation and Language |
The relational processing limits of classic and contemporary neural
network models of language processing | The ability of neural networks to capture relational knowledge is a matter of
long-standing controversy. Recently, some researchers in the PDP side of the
debate have argued that (1) classic PDP models can handle relational structure
(Rogers & McClelland, 2008, 2014) and (2) the success of deep learning
approaches to text processing suggests that structured representations are
unnecessary to capture the gist of human language (Rabovsky et al., 2018). In
the present study we tested the Story Gestalt model (St. John, 1992), a classic
PDP model of text comprehension, and a Sequence-to-Sequence with Attention
model (Bahdanau et al., 2015), a contemporary deep learning architecture for
text processing. Both models were trained to answer questions about stories
based on the thematic roles that several concepts played on the stories. In
three critical test we varied the statistical structure of new stories while
keeping their relational structure constant with respect to the training data.
Each model was susceptible to each statistical structure manipulation to a
different degree, with their performance failing below chance at least under
one manipulation. We argue that the failures of both models are due to the fact
that they cannotperform dynamic binding of independent roles and fillers.
Ultimately, these results cast doubts onthe suitability of traditional neural
networks models for explaining phenomena based on relational reasoning,
including language processing.
| 2,019 | Computation and Language |
Challenges in Building Intelligent Open-domain Dialog Systems | There is a resurgent interest in developing intelligent open-domain dialog
systems due to the availability of large amounts of conversational data and the
recent progress on neural approaches to conversational AI. Unlike traditional
task-oriented bots, an open-domain dialog system aims to establish long-term
connections with users by satisfying the human need for communication,
affection, and social belonging. This paper reviews the recent works on neural
approaches that are devoted to addressing three challenges in developing such
systems: semantics, consistency, and interactiveness. Semantics requires a
dialog system to not only understand the content of the dialog but also
identify user's social needs during the conversation. Consistency requires the
system to demonstrate a consistent personality to win users trust and gain
their long-term confidence. Interactiveness refers to the system's ability to
generate interpersonal responses to achieve particular social goals such as
entertainment, conforming, and task completion. The works we select to present
here is based on our unique views and are by no means complete. Nevertheless,
we hope that the discussion will inspire new research in developing more
intelligent dialog systems.
| 2,020 | Computation and Language |
Multi-step Retriever-Reader Interaction for Scalable Open-domain
Question Answering | This paper introduces a new framework for open-domain question answering in
which the retriever and the reader iteratively interact with each other. The
framework is agnostic to the architecture of the machine reading model, only
requiring access to the token-level hidden representations of the reader. The
retriever uses fast nearest neighbor search to scale to corpora containing
millions of paragraphs. A gated recurrent unit updates the query at each step
conditioned on the state of the reader and the reformulated query is used to
re-rank the paragraphs by the retriever. We conduct analysis and show that
iterative interaction helps in retrieving informative paragraphs from the
corpus. Finally, we show that our multi-step-reasoning framework brings
consistent improvement when applied to two widely used reader architectures
DrQA and BiDAF on various large open-domain datasets --- TriviaQA-unfiltered,
QuasarT, SearchQA, and SQuAD-Open.
| 2,019 | Computation and Language |
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment
Analysis | Related tasks often have inter-dependence on each other and perform better
when solved in a joint framework. In this paper, we present a deep multi-task
learning framework that jointly performs sentiment and emotion analysis both.
The multi-modal inputs (i.e., text, acoustic and visual frames) of a video
convey diverse and distinctive information, and usually do not have equal
contribution in the decision making. We propose a context-level inter-modal
attention framework for simultaneously predicting the sentiment and expressed
emotions of an utterance. We evaluate our proposed approach on CMU-MOSEI
dataset for multi-modal sentiment and emotion analysis. Evaluation results
suggest that multi-task learning framework offers improvement over the
single-task framework. The proposed approach reports new state-of-the-art
performance for both sentiment analysis and emotion analysis.
| 2,019 | Computation and Language |
Curriculum Learning for Domain Adaptation in Neural Machine Translation | We introduce a curriculum learning approach to adapt generic neural machine
translation models to a specific domain. Samples are grouped by their
similarities to the domain of interest and each group is fed to the training
algorithm with a particular schedule. This approach is simple to implement on
top of any neural framework or architecture, and consistently outperforms both
unadapted and adapted baselines in experiments with two distinct domains and
two language pairs.
| 2,019 | Computation and Language |
Ontology-Aware Clinical Abstractive Summarization | Automatically generating accurate summaries from clinical reports could save
a clinician's time, improve summary coverage, and reduce errors. We propose a
sequence-to-sequence abstractive summarization model augmented with
domain-specific ontological information to enhance content selection and
summary generation. We apply our method to a dataset of radiology reports and
show that it significantly outperforms the current state-of-the-art on this
task in terms of rouge scores. Extensive human evaluation conducted by a
radiologist further indicates that this approach yields summaries that are less
likely to omit important details, without sacrificing readability or accuracy.
| 2,019 | Computation and Language |
Extraction and Analysis of Clinically Important Follow-up
Recommendations in a Large Radiology Dataset | Communication of follow-up recommendations when abnormalities are identified
on imaging studies is prone to error. In this paper, we present a natural
language processing approach based on deep learning to automatically identify
clinically important recommendations in radiology reports. Our approach first
identifies the recommendation sentences and then extracts reason, test, and
time frame of the identified recommendations. To train our extraction models,
we created a corpus of 567 radiology reports annotated for recommendation
information. Our extraction models achieved 0.92 f-score for recommendation
sentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score for
time frame. We applied the extraction models to a set of over 3.3 million
radiology reports and analyzed the adherence of follow-up recommendations.
| 2,019 | Computation and Language |
BERT Rediscovers the Classical NLP Pipeline | Pre-trained text encoders have rapidly advanced the state of the art on many
NLP tasks. We focus on one such model, BERT, and aim to quantify where
linguistic information is captured within the network. We find that the model
represents the steps of the traditional NLP pipeline in an interpretable and
localizable way, and that the regions responsible for each step appear in the
expected sequence: POS tagging, parsing, NER, semantic roles, then coreference.
Qualitative analysis reveals that the model can and often does adjust this
pipeline dynamically, revising lower-level decisions on the basis of
disambiguating information from higher-level representations.
| 2,019 | Computation and Language |
When a Good Translation is Wrong in Context: Context-Aware Machine
Translation Improves on Deixis, Ellipsis, and Lexical Cohesion | Though machine translation errors caused by the lack of context beyond one
sentence have long been acknowledged, the development of context-aware NMT
systems is hampered by several problems. Firstly, standard metrics are not
sensitive to improvements in consistency in document-level translations.
Secondly, previous work on context-aware NMT assumed that the sentence-aligned
parallel data consisted of complete documents while in most practical scenarios
such document-level data constitutes only a fraction of the available parallel
data. To address the first issue, we perform a human study on an
English-Russian subtitles dataset and identify deixis, ellipsis and lexical
cohesion as three main sources of inconsistency. We then create test sets
targeting these phenomena. To address the second shortcoming, we consider a
set-up in which a much larger amount of sentence-level data is available
compared to that aligned at the document level. We introduce a model that is
suitable for this scenario and demonstrate major gains over a context-agnostic
baseline on our new benchmarks without sacrificing performance as measured with
BLEU.
| 2,019 | Computation and Language |
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image
Representations | In vision-and-language grounding problems, fine-grained representations of
the image are considered to be of paramount importance. Most of the current
systems incorporate visual features and textual concepts as a sketch of an
image. However, plainly inferred representations are usually undesirable in
that they are composed of separate components, the relations of which are
elusive. In this work, we aim at representing an image with a set of integrated
visual regions and corresponding textual concepts, reflecting certain
semantics. To this end, we build the Mutual Iterative Attention (MIA) module,
which integrates correlated visual features and textual concepts, respectively,
by aligning the two modalities. We evaluate the proposed approach on two
representative vision-and-language grounding tasks, i.e., image captioning and
visual question answering. In both tasks, the semantic-grounded image
representations consistently boost the performance of the baseline models under
all metrics across the board. The results demonstrate that our approach is
effective and generalizes well to a wide range of models for image-related
applications. (The code is available at https://github.com/fenglinliu98/MIA)
| 2,019 | Computation and Language |
Dual Supervised Learning for Natural Language Understanding and
Generation | Natural language understanding (NLU) and natural language generation (NLG)
are both critical research topics in the NLP field. Natural language
understanding is to extract the core semantic meaning from the given
utterances, while natural language generation is opposite, of which the goal is
to construct corresponding sentences based on the given semantics. However,
such dual relationship has not been investigated in the literature. This paper
proposes a new learning framework for language understanding and generation on
top of dual supervised learning, providing a way to exploit the duality. The
preliminary experiments show that the proposed approach boosts the performance
for both tasks.
| 2,020 | Computation and Language |
Selection Bias Explorations and Debias Methods for Natural Language
Sentence Matching Datasets | Natural Language Sentence Matching (NLSM) has gained substantial attention
from both academics and the industry, and rich public datasets contribute a lot
to this process. However, biased datasets can also hurt the generalization
performance of trained models and give untrustworthy evaluation results. For
many NLSM datasets, the providers select some pairs of sentences into the
datasets, and this sampling procedure can easily bring unintended pattern,
i.e., selection bias. One example is the QuoraQP dataset, where some
content-independent naive features are unreasonably predictive. Such features
are the reflection of the selection bias and termed as the leakage features. In
this paper, we investigate the problem of selection bias on six NLSM datasets
and find that four out of them are significantly biased. We further propose a
training and evaluation framework to alleviate the bias. Experimental results
on QuoraQP suggest that the proposed framework can improve the generalization
ability of trained models, and give more trustworthy evaluation results for
real-world adoptions.
| 2,019 | Computation and Language |
Representing Schema Structure with Graph Neural Networks for Text-to-SQL
Parsing | Research on parsing language to SQL has largely ignored the structure of the
database (DB) schema, either because the DB was very simple, or because it was
observed at both training and test time. In Spider, a recently-released
text-to-SQL dataset, new and complex DBs are given at test time, and so the
structure of the DB schema can inform the predicted SQL query. In this paper,
we present an encoder-decoder semantic parser, where the structure of the DB
schema is encoded with a graph neural network, and this representation is later
used at both encoding and decoding time. Evaluation shows that encoding the
schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically
above the current state of the art, which is at 19.7%.
| 2,019 | Computation and Language |
A Surprisingly Robust Trick for Winograd Schema Challenge | The Winograd Schema Challenge (WSC) dataset WSC273 and its inference
counterpart WNLI are popular benchmarks for natural language understanding and
commonsense reasoning. In this paper, we show that the performance of three
language models on WSC273 strongly improves when fine-tuned on a similar
pronoun disambiguation problem dataset (denoted WSCR). We additionally generate
a large unsupervised WSC-like dataset. By fine-tuning the BERT language model
both on the introduced and on the WSCR dataset, we achieve overall accuracies
of 72.5% and 74.7% on WSC273 and WNLI, improving the previous state-of-the-art
solutions by 8.8% and 9.6%, respectively. Furthermore, our fine-tuned models
are also consistently more robust on the "complex" subsets of WSC273,
introduced by Trichelair et al. (2018).
| 2,019 | Computation and Language |
What do you learn from context? Probing for sentence structure in
contextualized word representations | Contextualized representation models such as ELMo (Peters et al., 2018a) and
BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a
diverse array of downstream NLP tasks. Building on recent token-level probing
work, we introduce a novel edge probing task design and construct a broad suite
of sub-sentence tasks derived from the traditional structured NLP pipeline. We
probe word-level contextual representations from four recent models and
investigate how they encode sentence structure across a range of syntactic,
semantic, local, and long-range phenomena. We find that existing models trained
on language modeling and translation produce strong representations for
syntactic phenomena, but only offer comparably small improvements on semantic
tasks over a non-contextual baseline.
| 2,019 | Computation and Language |
Exact Hard Monotonic Attention for Character-Level Transduction | Many common character-level, string-to string transduction tasks, e.g.,
grapheme-tophoneme conversion and morphological inflection, consist almost
exclusively of monotonic transductions. However, neural sequence-to sequence
models that use non-monotonic soft attention often outperform popular monotonic
models. In this work, we ask the following question: Is monotonicity really a
helpful inductive bias for these tasks? We develop a hard attention
sequence-to-sequence model that enforces strict monotonicity and learns a
latent alignment jointly while learning to transduce. With the help of dynamic
programming, we are able to compute the exact marginalization over all
monotonic alignments. Our models achieve state-of-the-art performance on
morphological inflection. Furthermore, we find strong performance on two other
character-level transduction tasks. Code is available at
https://github.com/shijie-wu/neural-transducer.
| 2,024 | Computation and Language |
Correlating neural and symbolic representations of language | Analysis methods which enable us to better understand the representations and
functioning of neural models of language are increasingly needed as deep
learning becomes the dominant approach in NLP. Here we present two methods
based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which
allow us to directly quantify how strongly the information encoded in neural
activation patterns corresponds to information represented by symbolic
structures such as syntax trees. We first validate our methods on the case of a
simple synthetic language for arithmetic expressions with clearly defined
syntax and semantics, and show that they exhibit the expected pattern of
results. We then apply our methods to correlate neural representations of
English sentences with their constituency parse trees.
| 2,023 | Computation and Language |
Controlled CNN-based Sequence Labeling for Aspect Extraction | One key task of fine-grained sentiment analysis on reviews is to extract
aspects or features that users have expressed opinions on. This paper focuses
on supervised aspect extraction using a modified CNN called controlled CNN
(Ctrl). The modified CNN has two types of control modules. Through asynchronous
parameter updating, it prevents over-fitting and boosts CNN's performance
significantly. This model achieves state-of-the-art results on standard aspect
extraction datasets. To the best of our knowledge, this is the first paper to
apply control modules to aspect extraction.
| 2,019 | Computation and Language |
Incorporating Sememes into Chinese Definition Modeling | Chinese definition modeling is a challenging task that generates a dictionary
definition in Chinese for a given Chinese word. To accomplish this task, we
construct the Chinese Definition Modeling Corpus (CDM), which contains triples
of word, sememes and the corresponding definition. We present two novel models
to improve Chinese definition modeling: the Adaptive-Attention model (AAM) and
the Self- and Adaptive-Attention Model (SAAM). AAM successfully incorporates
sememes for generating the definition with an adaptive attention mechanism. It
has the capability to decide which sememes to focus on and when to pay
attention to sememes. SAAM further replaces recurrent connections in AAM with
self-attention and relies entirely on the attention mechanism, reducing the
path length between word, sememes and definition. Experiments on CDM
demonstrate that by incorporating sememes, our best proposed model can
outperform the state-of-the-art method by +6.0 BLEU.
| 2,019 | Computation and Language |
Articulatory and bottleneck features for speaker-independent ASR of
dysarthric speech | The rapid population aging has stimulated the development of assistive
devices that provide personalized medical support to the needies suffering from
various etiologies. One prominent clinical application is a computer-assisted
speech training system which enables personalized speech therapy to patients
impaired by communicative disorders in the patient's home environment. Such a
system relies on the robust automatic speech recognition (ASR) technology to be
able to provide accurate articulation feedback. With the long-term aim of
developing off-the-shelf ASR systems that can be incorporated in clinical
context without prior speaker information, we compare the ASR performance of
speaker-independent bottleneck and articulatory features on dysarthric speech
used in conjunction with dedicated neural network-based acoustic models that
have been shown to be robust against spectrotemporal deviations. We report ASR
performance of these systems on two dysarthric speech datasets of different
characteristics to quantify the achieved performance gains. Despite the
remaining performance gap between the dysarthric and normal speech, significant
improvements have been reported on both datasets using speaker-independent ASR
architectures.
| 2,019 | Computation and Language |
HIBERT: Document Level Pre-training of Hierarchical Bidirectional
Transformers for Document Summarization | Neural extractive summarization models usually employ a hierarchical encoder
for document encoding and they are trained using sentence-level labels, which
are created heuristically using rule-based methods. Training the hierarchical
encoder with these \emph{inaccurate} labels is challenging. Inspired by the
recent work on pre-training transformer sentence encoders
\cite{devlin:2018:arxiv}, we propose {\sc Hibert} (as shorthand for {\bf
HI}erachical {\bf B}idirectional {\bf E}ncoder {\bf R}epresentations from {\bf
T}ransformers) for document encoding and a method to pre-train it using
unlabeled data. We apply the pre-trained {\sc Hibert} to our summarization
model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on
the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times
dataset. We also achieve the state-of-the-art performance on these two
datasets.
| 2,019 | Computation and Language |
Joint Source-Target Self Attention with Locality Constraints | The dominant neural machine translation models are based on the
encoder-decoder structure, and many of them rely on an unconstrained receptive
field over source and target sequences. In this paper we study a new
architecture that breaks with both conventions. Our simplified architecture
consists in the decoder part of a transformer model, based on self-attention,
but with locality constraints applied on the attention receptive field. As
input for training, both source and target sentences are fed to the network,
which is trained as a language model. At inference time, the target tokens are
predicted autoregressively starting with the source sequence as previous
tokens. The proposed model achieves a new state of the art of 35.7 BLEU on
IWSLT'14 German-English and matches the best reported results in the literature
on the WMT'14 English-German and WMT'14 English-French translation benchmarks.
| 2,019 | Computation and Language |
Latent Universal Task-Specific BERT | This paper describes a language representation model which combines the
Bidirectional Encoder Representations from Transformers (BERT) learning
mechanism described in Devlin et al. (2018) with a generalization of the
Universal Transformer model described in Dehghani et al. (2018). We further
improve this model by adding a latent variable that represents the persona and
topics of interests of the writer for each training example. We also describe a
simple method to improve the usefulness of our language representation for
solving problems in a specific domain at the expense of its ability to
generalize to other fields. Finally, we release a pre-trained language
representation model for social texts that was trained on 100 million tweets.
| 2,019 | Computation and Language |
Machine Learning based English Sentiment Analysis | Sentiment analysis or opinion mining aims to determine attitudes, judgments
and opinions of customers for a product or a service. This is a great system to
help manufacturers or servicers know the satisfaction level of customers about
their products or services. From that, they can have appropriate adjustments.
We use a popular machine learning method, being Support Vector Machine, combine
with the library in Waikato Environment for Knowledge Analysis (WEKA) to build
Java web program which analyzes the sentiment of English comments belongs one
in four types of woman products. That are dresses, handbags, shoes and rings.
We have developed and test our system with a training set having 300 comments
and a test set having 400 comments. The experimental results of the system
about precision, recall and F measures for positive comments are 89.3%, 95.0%
and 92,.1%; for negative comments are 97.1%, 78.5% and 86.8%; and for neutral
comments are 76.7%, 86.2% and 81.2%.
| 2,014 | Computation and Language |
Using Entity Relations for Opinion Mining of Vietnamese Comments | In this paper, we propose several novel techniques to extract and mining
opinions of Vietnamese reviews of customers about a number of products traded
on e-commerce in Vietnam. The assessment is based on the emotional level of
customers on a specific product such as mobile and laptop. We exploit the
features of the products because they are much interested by customers and have
many products in the Vietnam e-commerce market. Thence, it can be known the
favorites and dislikes of customers about exploited products.
| 2,014 | Computation and Language |
What do Entity-Centric Models Learn? Insights from Entity Linking in
Multi-Party Dialogue | Humans use language to refer to entities in the external world. Motivated by
this, in recent years several models that incorporate a bias towards learning
entity representations have been proposed. Such entity-centric models have
shown empirical success, but we still know little about why. In this paper we
analyze the behavior of two recently proposed entity-centric models in a
referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4).
We show that these models outperform the state of the art on this task, and
that they do better on lower frequency entities than a counterpart model that
is not entity-centric, with the same model size. We argue that making models
entity-centric naturally fosters good architectural decisions. However, we also
show that these models do not really build entity representations and that they
make poor use of linguistic context. These negative results underscore the need
for model analysis, to test whether the motivations for particular
architectures are borne out in how models behave when deployed.
| 2,019 | Computation and Language |
Effective Sentence Scoring Method using Bidirectional Language Model for
Speech Recognition | In automatic speech recognition, many studies have shown performance
improvements using language models (LMs). Recent studies have tried to use
bidirectional LMs (biLMs) instead of conventional unidirectional LMs (uniLMs)
for rescoring the $N$-best list decoded from the acoustic model. In spite of
their theoretical benefits, the biLMs have not given notable improvements
compared to the uniLMs in their experiments. This is because their biLMs do not
consider the interaction between the two directions. In this paper, we propose
a novel sentence scoring method considering the interaction between the past
and the future words on the biLM. Our experimental results on the LibriSpeech
corpus show that the biLM with the proposed sentence scoring outperforms the
uniLM for the $N$-best list rescoring, consistently and significantly in all
experimental conditions. The analysis of WERs by word position demonstrates
that the biLM is more robust than the uniLM especially when a recognized
sentence is short or a misrecognized word is at the beginning of the sentence.
| 2,019 | Computation and Language |
Towards Interlingua Neural Machine Translation | Common intermediate language representation in neural machine translation can
be used to extend bilingual to multilingual systems by incremental training. In
this paper, we propose a new architecture based on introducing an interlingual
loss as an additional training objective. By adding and forcing this
interlingual loss, we are able to train multiple encoders and decoders for each
language, sharing a common intermediate representation. Translation results on
the low-resourced tasks (Turkish-English and Kazakh-English tasks, from the
popular Workshop on Machine Translation benchmark) show the following BLEU
improvements up to 2.8. However, results on a larger dataset (Russian-English
and Kazakh-English, from the same baselines) show BLEU loses if the same
amount. While our system is only providing improvements for the low-resourced
tasks in terms of translation quality, our system is capable of quickly
deploying new language pairs without retraining the rest of the system, which
may be a game-changer in some situations (i.e. in a disaster crisis where
international help is required towards a small region or to develop some
translation system for a client). Precisely, what is most relevant from our
architecture is that it is capable of: (1) reducing the number of production
systems, with respect to the number of languages, from quadratic to linear (2)
incrementally adding a new language in the system without retraining languages
previously there and (3) allowing for translations from the new language to all
the others present in the system
| 2,019 | Computation and Language |
Tracing cultural diachronic semantic shifts in Russian using word
embeddings: test sets and baselines | The paper introduces manually annotated test sets for the task of tracing
diachronic (temporal) semantic shifts in Russian. The two test sets are
complementary in that the first one covers comparatively strong semantic
changes occurring to nouns and adjectives from pre-Soviet to Soviet times,
while the second one covers comparatively subtle socially and culturally
determined shifts occurring in years from 2000 to 2014. Additionally, the
second test set offers more granular classification of shifts degree, but is
limited to only adjectives.
The introduction of the test sets allowed us to evaluate several
well-established algorithms of semantic shifts detection (posing this as a
classification problem), most of which have never been tested on Russian
material. All of these algorithms use distributional word embedding models
trained on the corresponding in-domain corpora. The resulting scores provide
solid comparison baselines for future studies tackling similar tasks. We
publish the datasets, code and the trained models in order to facilitate
further research in automatically detecting temporal semantic shifts for
Russian words, with time periods of different granularities.
| 2,019 | Computation and Language |
TraceWalk: Semantic-based Process Graph Embedding for Consistency
Checking | Process consistency checking (PCC), an interdiscipline of natural language
processing (NLP) and business process management (BPM), aims to quantify the
degree of (in)consistencies between graphical and textual descriptions of a
process. However, previous studies heavily depend on a great deal of complex
expert-defined knowledge such as alignment rules and assessment metrics, thus
suffer from the problems of low accuracy and poor adaptability when applied in
open-domain scenarios. To address the above issues, this paper makes the first
attempt that uses deep learning to perform PCC. Specifically, we proposed
TraceWalk, using semantic information of process graphs to learn latent node
representations, and integrates it into a convolutional neural network (CNN)
based model called TraceNet to predict consistencies. The theoretical proof
formally provides the PCC's lower limit and experimental results demonstrate
that our approach performs more accurately than state-of-the-art baselines.
| 2,019 | Computation and Language |
Gated Convolutional Neural Networks for Domain Adaptation | Domain Adaptation explores the idea of how to maximize performance on a
target domain, distinct from source domain, upon which the classifier was
trained. This idea has been explored for the task of sentiment analysis
extensively. The training of reviews pertaining to one domain and evaluation on
another domain is widely studied for modeling a domain independent algorithm.
This further helps in understanding correlation between domains. In this paper,
we show that Gated Convolutional Neural Networks (GCN) perform effectively at
learning sentiment analysis in a manner where domain dependant knowledge is
filtered out using its gates. We perform our experiments on multiple gate
architectures: Gated Tanh ReLU Unit (GTRU), Gated Tanh Unit (GTU) and Gated
Linear Unit (GLU). Extensive experimentation on two standard datasets relevant
to the task, reveal that training with Gated Convolutional Neural Networks give
significantly better performance on target domains than regular convolution and
recurrent based architectures. While complex architectures like attention,
filter domain specific knowledge as well, their complexity order is remarkably
high as compared to gated architectures. GCNs rely on convolution hence gaining
an upper hand through parallelization.
| 2,019 | Computation and Language |
Dynamically Fused Graph Network for Multi-hop Reasoning | Text-based question answering (TBQA) has been studied extensively in recent
years. Most existing approaches focus on finding the answer to a question
within a single paragraph. However, many difficult questions require multiple
supporting evidence from scattered text among two or more documents. In this
paper, we propose Dynamically Fused Graph Network(DFGN), a novel method to
answer those questions requiring multiple scattered evidence and reasoning over
them. Inspired by human's step-by-step reasoning behavior, DFGN includes a
dynamic fusion layer that starts from the entities mentioned in the given
query, explores along the entity graph dynamically built from the text, and
gradually finds relevant supporting entities from the given documents. We
evaluate DFGN on HotpotQA, a public TBQA dataset requiring multi-hop reasoning.
DFGN achieves competitive results on the public board. Furthermore, our
analysis shows DFGN produces interpretable reasoning chains.
| 2,019 | Computation and Language |
The Materials Science Procedural Text Corpus: Annotating Materials
Synthesis Procedures with Shallow Semantic Structures | Materials science literature contains millions of materials synthesis
procedures described in unstructured natural language text. Large-scale
analysis of these synthesis procedures would facilitate deeper scientific
understanding of materials synthesis and enable automated synthesis planning.
Such analysis requires extracting structured representations of synthesis
procedures from the raw text as a first step. To facilitate the training and
evaluation of synthesis extraction models, we introduce a dataset of 230
synthesis procedures annotated by domain experts with labeled graphs that
express the semantics of the synthesis sentences. The nodes in this graph are
synthesis operations and their typed arguments, and labeled edges specify
relations between the nodes. We describe this new resource in detail and
highlight some specific challenges to annotating scientific text with shallow
semantic structure. We make the corpus available to the community to promote
further research and development of scientific information extraction systems.
| 2,019 | Computation and Language |
IMHO Fine-Tuning Improves Claim Detection | Claims are the central component of an argument. Detecting claims across
different domains or data sets can often be challenging due to their varying
conceptualization. We propose to alleviate this problem by fine tuning a
language model using a Reddit corpus of 5.5 million opinionated claims. These
claims are self-labeled by their authors using the internet acronyms IMO/IMHO
(in my (humble) opinion). Empirical results show that using this approach
improves the state of art performance across four benchmark argumentation data
sets by an average of 4 absolute F1 points in claim detection. As these data
sets include diverse domains such as social media and student essays this
improvement demonstrates the robustness of fine-tuning on this novel corpus.
| 2,019 | Computation and Language |
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using
Neural Language Models | Large-scale clinical data is invaluable to driving many computational
scientific advances today. However, understandable concerns regarding patient
privacy hinder the open dissemination of such data and give rise to suboptimal
siloed research. De-identification methods attempt to address these concerns
but were shown to be susceptible to adversarial attacks. In this work, we focus
on the vast amounts of unstructured natural language data stored in clinical
notes and propose to automatically generate synthetic clinical notes that are
more amenable to sharing using generative models trained on real de-identified
records. To evaluate the merit of such notes, we measure both their privacy
preservation properties as well as utility in training clinical NLP models.
Experiments using neural language models yield notes whose utility is close to
that of the real ones in some clinical NLP tasks, yet leave ample room for
future improvements.
| 2,019 | Computation and Language |
Improving Question Answering over Incomplete KBs with Knowledge-Aware
Reader | We propose a new end-to-end question answering model, which learns to
aggregate answer evidence from an incomplete knowledge base (KB) and a set of
retrieved text snippets. Under the assumptions that the structured KB is easier
to query and the acquired knowledge can help the understanding of unstructured
text, our model first accumulates knowledge of entities from a question-related
KB subgraph; then reformulates the question in the latent space and reads the
texts with the accumulated entity knowledge at hand. The evidence from KB and
texts are finally aggregated to predict answers. On the widely-used KBQA
benchmark WebQSP, our model achieves consistent improvements across settings
with different extents of KB incompleteness.
| 2,019 | Computation and Language |
ERNIE: Enhanced Language Representation with Informative Entities | Neural language representation models such as BERT pre-trained on large-scale
corpora can well capture rich semantic patterns from plain text, and be
fine-tuned to consistently improve the performance of various NLP tasks.
However, the existing pre-trained language models rarely consider incorporating
knowledge graphs (KGs), which can provide rich structured knowledge facts for
better language understanding. We argue that informative entities in KGs can
enhance language representation with external knowledge. In this paper, we
utilize both large-scale textual corpora and KGs to train an enhanced language
representation model (ERNIE), which can take full advantage of lexical,
syntactic, and knowledge information simultaneously. The experimental results
have demonstrated that ERNIE achieves significant improvements on various
knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art
model BERT on other common NLP tasks. The source code of this paper can be
obtained from https://github.com/thunlp/ERNIE.
| 2,019 | Computation and Language |
Plotting Markson's 'Mistress' | The post-modern novel 'Wittgenstein's Mistress' by David Markson (1988)
presents the reader with a very challenging non linear narrative, that itself
appears to one of the novel's themes. We present a distant reading of this work
designed to complement a close reading of it by David Foster Wallace (1990).
Using a combination of text analysis, entity recognition and networks, we plot
repetitive structures in the novel's narrative relating them to its critical
analysis.
| 2,019 | Computation and Language |
Distant Learning for Entity Linking with Automatic Noise Detection | Accurate entity linkers have been produced for domains and languages where
annotated data (i.e., texts linked to a knowledge base) is available. However,
little progress has been made for the settings where no or very limited amounts
of labeled data are present (e.g., legal or most scientific domains). In this
work, we show how we can learn to link mentions without having any labeled
examples, only a knowledge base and a collection of unannotated texts from the
corresponding domain. In order to achieve this, we frame the task as a
multi-instance learning problem and rely on surface matching to create initial
noisy labels. As the learning signal is weak and our surrogate labels are
noisy, we introduce a noise detection component in our model: it lets the model
detect and disregard examples which are likely to be noisy. Our method, jointly
learning to detect noise and link entities, greatly outperforms the surface
matching baseline. For a subset of entity categories, it even approaches the
performance of supervised learning.
| 2,019 | Computation and Language |
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven
Dynamic Hierarchical Conditional Variational Network | The prosodic aspects of speech signals produced by current text-to-speech
systems are typically averaged over training material, and as such lack the
variety and liveliness found in natural speech. To avoid monotony and averaged
prosody contours, it is desirable to have a way of modeling the variation in
the prosodic aspects of speech, so audio signals can be synthesized in multiple
ways for a given text. We present a new, hierarchically structured conditional
variational autoencoder to generate prosodic features (fundamental frequency,
energy and duration) suitable for use with a vocoder or a generative model like
WaveNet. At inference time, an embedding representing the prosody of a sentence
may be sampled from the variational layer to allow for prosodic variation. To
efficiently capture the hierarchical nature of the linguistic input (words,
syllables and phones), both the encoder and decoder parts of the auto-encoder
are hierarchical, in line with the linguistic structure, with layers being
clocked dynamically at the respective rates. We show in our experiments that
our dynamic hierarchical network outperforms a non-hierarchical
state-of-the-art baseline, and, additionally, that prosody transfer across
sentences is possible by employing the prosody embedding of one sentence to
generate the speech signal of another.
| 2,019 | Computation and Language |
Adaptation of Deep Bidirectional Multilingual Transformers for Russian
Language | The paper introduces methods of adaptation of multilingual masked language
models for a specific language. Pre-trained bidirectional language models show
state-of-the-art performance on a wide range of tasks including reading
comprehension, natural language inference, and sentiment analysis. At the
moment there are two alternative approaches to train such models: monolingual
and multilingual. While language specific models show superior performance,
multilingual models allow to perform a transfer from one language to another
and solve tasks for different languages simultaneously. This work shows that
transfer learning from a multilingual model to monolingual model results in
significant growth of performance on such tasks as reading comprehension,
paraphrase detection, and sentiment analysis. Furthermore, multilingual
initialization of monolingual model substantially reduces training time.
Pre-trained models for the Russian language are open sourced.
| 2,019 | Computation and Language |
Conversion Prediction Using Multi-task Conditional Attention Networks to
Support the Creation of Effective Ad Creative | Accurately predicting conversions in advertisements is generally a
challenging task, because such conversions do not occur frequently. In this
paper, we propose a new framework to support creating high-performing ad
creatives, including the accurate prediction of ad creative text conversions
before delivering to the consumer. The proposed framework includes three key
ideas: multi-task learning, conditional attention, and attention highlighting.
Multi-task learning is an idea for improving the prediction accuracy of
conversion, which predicts clicks and conversions simultaneously, to solve the
difficulty of data imbalance. Furthermore, conditional attention focuses
attention of each ad creative with the consideration of its genre and target
gender, thus improving conversion prediction accuracy. Attention highlighting
visualizes important words and/or phrases based on conditional attention. We
evaluated the proposed framework with actual delivery history data (14,000
creatives displayed more than a certain number of times from Gunosy Inc.), and
confirmed that these ideas improve the prediction performance of conversions,
and visualize noteworthy words according to the creatives' attributes.
| 2,019 | Computation and Language |
Availability-Based Production Predicts Speakers' Real-time Choices of
Mandarin Classifiers | Speakers often face choices as to how to structure their intended message
into an utterance. Here we investigate the influence of contextual
predictability on the encoding of linguistic content manifested by speaker
choice in a classifier language. In English, a numeral modifies a noun directly
(e.g., three computers). In classifier languages such as Mandarin Chinese, it
is obligatory to use a classifier (CL) with the numeral and the noun (e.g.,
three CL.machinery computer, three CL.general computer). While different nouns
are compatible with different specific classifiers, there is a general
classifier "ge" (CL.general) that can be used with most nouns. When the
upcoming noun is less predictable, the use of a more specific classifier would
reduce surprisal at the noun thus potentially facilitate comprehension
(predicted by Uniform Information Density, Levy & Jaeger, 2007), but the use of
that more specific classifier may be dispreferred from a production standpoint
if accessing the general classifier is always available (predicted by
Availability-Based Production; Bock, 1987; Ferreira & Dell, 2000). Here we use
a picture-naming experiment showing that Availability-Based Production predicts
speakers' real-time choices of Mandarin classifiers.
| 2,019 | Computation and Language |
Don't Blame Distributional Semantics if it can't do Entailment | Distributional semantics has had enormous empirical success in Computational
Linguistics and Cognitive Science in modeling various semantic phenomena, such
as semantic similarity, and distributional models are widely used in
state-of-the-art Natural Language Processing systems. However, the theoretical
status of distributional semantics within a broader theory of language and
cognition is still unclear: What does distributional semantics model? Can it
be, on its own, a fully adequate model of the meanings of linguistic
expressions? The standard answer is that distributional semantics is not fully
adequate in this regard, because it falls short on some of the central aspects
of formal semantic approaches: truth conditions, entailment, reference, and
certain aspects of compositionality. We argue that this standard answer rests
on a misconception: These aspects do not belong in a theory of expression
meaning, they are instead aspects of speaker meaning, i.e., communicative
intentions in a particular context. In a slogan: words do not refer, speakers
do. Clearing this up enables us to argue that distributional semantics on its
own is an adequate model of expression meaning. Our proposal sheds light on the
role of distributional semantics in a broader theory of language and cognition,
its relationship to formal semantics, and its place in computational models.
| 2,019 | Computation and Language |
Learning Cross-lingual Embeddings from Twitter via Distant Supervision | Cross-lingual embeddings represent the meaning of words from different
languages in the same vector space. Recent work has shown that it is possible
to construct such representations by aligning independently learned monolingual
embedding spaces, and that accurate alignments can be obtained even without
external bilingual data. In this paper we explore a research direction that has
been surprisingly neglected in the literature: leveraging noisy user-generated
text to learn cross-lingual embeddings particularly tailored towards social
media applications. While the noisiness and informal nature of the social media
genre poses additional challenges to cross-lingual embedding methods, we find
that it also provides key opportunities due to the abundance of code-switching
and the existence of a shared vocabulary of emoji and named entities. Our
contribution consists of a very simple post-processing step that exploits these
phenomena to significantly improve the performance of state-of-the-art
alignment methods.
| 2,020 | Computation and Language |
Multi-hop Reading Comprehension across Multiple Documents by Reasoning
over Heterogeneous Graphs | Multi-hop reading comprehension (RC) across documents poses new challenge
over single-document RC because it requires reasoning over multiple documents
to reach the final answer. In this paper, we propose a new model to tackle the
multi-hop RC problem. We introduce a heterogeneous graph with different types
of nodes and edges, which is named as Heterogeneous Document-Entity (HDE)
graph. The advantage of HDE graph is that it contains different granularity
levels of information including candidates, documents and entities in specific
document contexts. Our proposed model can do reasoning over the HDE graph with
nodes representation initialized with co-attention and self-attention based
context encoders. We employ Graph Neural Networks (GNN) based message passing
algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the
blind test set of the Qangaroo WikiHop data set, our HDE graph based single
model delivers competitive result, and the ensemble model achieves the
state-of-the-art performance.
| 2,019 | Computation and Language |
Functorial Question Answering | Distributional compositional (DisCo) models are functors that compute the
meaning of a sentence from the meaning of its words. We show that DisCo models
in the category of sets and relations correspond precisely to relational
databases. As a consequence, we get complexity-theoretic reductions from
semantics and entailment of a fragment of natural language to evaluation and
containment of conjunctive queries, respectively. Finally, we define question
answering as an NP-complete problem.
| 2,020 | Computation and Language |
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