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Improving Chinese Segmentation-free Word Embedding With Unsupervised
Association Measure | Recent work on segmentation-free word embedding(sembei) developed a new
pipeline of word embedding for unsegmentated language while avoiding
segmentation as a preprocessing step. However, too many noisy n-grams existing
in the embedding vocabulary that do not have strong association strength
between characters would limit the quality of learned word embedding. To deal
with this problem, a new version of segmentation-free word embedding model is
proposed by collecting n-grams vocabulary via a novel unsupervised association
measure called pointwise association with times information(PATI). Comparing
with the commonly used n-gram filtering method like frequency used in sembei
and pointwise mutual information(PMI), the proposed method leverages more
latent information from the corpus and thus is able to collect more valid
n-grams that have stronger cohesion as embedding targets in unsegmented
language data, such as Chinese texts. Further experiments on Chinese SNS data
show that the proposed model improves performance of word embedding in
downstream tasks.
| 2,020 | Computation and Language |
Exploratory Analysis of COVID-19 Related Tweets in North America to
Inform Public Health Institutes | Social media is a rich source where we can learn about people's reactions to
social issues. As COVID-19 has significantly impacted on people's lives, it is
essential to capture how people react to public health interventions and
understand their concerns. In this paper, we aim to investigate people's
reactions and concerns about COVID-19 in North America, especially focusing on
Canada. We analyze COVID-19 related tweets using topic modeling and
aspect-based sentiment analysis, and interpret the results with public health
experts. We compare timeline of topics discussed with timing of implementation
of public health interventions for COVID-19. We also examine people's sentiment
about COVID-19 related issues. We discuss how the results can be helpful for
public health agencies when designing a policy for new interventions. Our work
shows how Natural Language Processing (NLP) techniques could be applied to
public health questions with domain expert involvement.
| 2,020 | Computation and Language |
CORD19STS: COVID-19 Semantic Textual Similarity Dataset | In order to combat the COVID-19 pandemic, society can benefit from various
natural language processing applications, such as dialog medical diagnosis
systems and information retrieval engines calibrated specifically for COVID-19.
These applications rely on the ability to measure semantic textual similarity
(STS), making STS a fundamental task that can benefit several downstream
applications. However, existing STS datasets and models fail to translate their
performance to a domain-specific environment such as COVID-19. To overcome this
gap, we introduce CORD19STS dataset which includes 13,710 annotated sentence
pairs collected from COVID-19 open research dataset (CORD-19) challenge. To be
specific, we generated one million sentence pairs using different sampling
strategies. We then used a finetuned BERT-like language model, which we call
Sen-SCI-CORD19-BERT, to calculate the similarity scores between sentence pairs
to provide a balanced dataset with respect to the different semantic similarity
levels, which gives us a total of 32K sentence pairs. Each sentence pair was
annotated by five Amazon Mechanical Turk (AMT) crowd workers, where the labels
represent different semantic similarity levels between the sentence pairs (i.e.
related, somewhat-related, and not-related). After employing a rigorous
qualification tasks to verify collected annotations, our final CORD19STS
dataset includes 13,710 sentence pairs.
| 2,020 | Computation and Language |
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation
using Pretraining Language Model | This paper describes our submission to subtask a and b of SemEval-2020 Task
4. For subtask a, we use a ALBERT based model with improved input form to pick
out the common sense statement from two statement candidates. For subtask b, we
use a multiple choice model enhanced by hint sentence mechanism to select the
reason from given options about why a statement is against common sense.
Besides, we propose a novel transfer learning strategy between subtasks which
help improve the performance. The accuracy scores of our system are 95.6 / 94.9
on official test set and rank 7$^{th}$ / 2$^{nd}$ on Post-Evaluation
leaderboard.
| 2,020 | Computation and Language |
Reflection-based Word Attribute Transfer | Word embeddings, which often represent such analogic relations as king - man
+ woman = queen, can be used to change a word's attribute, including its
gender. For transferring king into queen in this analogy-based manner, we
subtract a difference vector man - woman based on the knowledge that king is
male. However, developing such knowledge is very costly for words and
attributes. In this work, we propose a novel method for word attribute transfer
based on reflection mappings without such an analogy operation. Experimental
results show that our proposed method can transfer the word attributes of the
given words without changing the words that do not have the target attributes.
| 2,020 | Computation and Language |
Relevance Transformer: Generating Concise Code Snippets with Relevance
Feedback | Tools capable of automatic code generation have the potential to augment
programmer's capabilities. While straightforward code retrieval is incorporated
into many IDEs, an emerging area is explicit code generation. Code generation
is currently approached as a Machine Translation task, with Recurrent Neural
Network (RNN) based encoder-decoder architectures trained on code-description
pairs. In this work we introduce and study modern Transformer architectures for
this task. We further propose a new model called the Relevance Transformer that
incorporates external knowledge using pseudo-relevance feedback. The Relevance
Transformer biases the decoding process to be similar to existing retrieved
code while enforcing diversity. We perform experiments on multiple standard
benchmark datasets for code generation including Django, Hearthstone, and
CoNaLa. The results show improvements over state-of-the-art methods based on
BLEU evaluation. The Relevance Transformer model shows the potential of
Transformer-based architectures for code generation and introduces a method of
incorporating pseudo-relevance feedback during inference.
| 2,020 | Computation and Language |
Learning Spoken Language Representations with Neural Lattice Language
Modeling | Pre-trained language models have achieved huge improvement on many NLP tasks.
However, these methods are usually designed for written text, so they do not
consider the properties of spoken language. Therefore, this paper aims at
generalizing the idea of language model pre-training to lattices generated by
recognition systems. We propose a framework that trains neural lattice language
models to provide contextualized representations for spoken language
understanding tasks. The proposed two-stage pre-training approach reduces the
demands of speech data and has better efficiency. Experiments on intent
detection and dialogue act recognition datasets demonstrate that our proposed
method consistently outperforms strong baselines when evaluated on spoken
inputs. The code is available at https://github.com/MiuLab/Lattice-ELMo.
| 2,020 | Computation and Language |
A Broad-Coverage Deep Semantic Lexicon for Verbs | Progress on deep language understanding is inhibited by the lack of a broad
coverage lexicon that connects linguistic behavior to ontological concepts and
axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the
coverage of WordNet and syntactic and semantic details that meet or exceed
existing resources. Bootstrapping from a hand-built lexicon and ontology, new
ontological concepts and lexical entries, together with semantic role
preferences and entailment axioms, are automatically derived by combining
multiple constraints from parsing dictionary definitions and examples. We
evaluated the accuracy of the technique along a number of different dimensions
and were able to obtain high accuracy in deriving new concepts and lexical
entries. COLLIE-V is publicly available.
| 2,020 | Computation and Language |
Bilingual Dictionary Based Neural Machine Translation without Using
Parallel Sentences | In this paper, we propose a new task of machine translation (MT), which is
based on no parallel sentences but can refer to a ground-truth bilingual
dictionary. Motivated by the ability of a monolingual speaker learning to
translate via looking up the bilingual dictionary, we propose the task to see
how much potential an MT system can attain using the bilingual dictionary and
large scale monolingual corpora, while is independent on parallel sentences. We
propose anchored training (AT) to tackle the task. AT uses the bilingual
dictionary to establish anchoring points for closing the gap between source
language and target language. Experiments on various language pairs show that
our approaches are significantly better than various baselines, including
dictionary-based word-by-word translation, dictionary-supervised cross-lingual
word embedding transformation, and unsupervised MT. On distant language pairs
that are hard for unsupervised MT to perform well, AT performs remarkably
better, achieving performances comparable to supervised SMT trained on more
than 4M parallel sentences.
| 2,020 | Computation and Language |
Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial
Naive Bayes | Recently, sentiment polarity detection has increased attention to NLP
researchers due to the massive availability of customer's opinions or reviews
in the online platform. Due to the continued expansion of e-commerce sites, the
rate of purchase of various products, including books, are growing enormously
among the people. Reader's opinions/reviews affect the buying decision of a
customer in most cases. This work introduces a machine learning-based technique
to determine sentiment polarities (either positive or negative category) from
Bengali book reviews. To assess the effectiveness of the proposed technique, a
corpus with 2000 reviews on Bengali books is developed. A comparative analysis
with various approaches (such as logistic regression, naive Bayes, SVM, and
SGD) also performed by taking into consideration of the unigram, bigram, and
trigram features, respectively. Experimental result reveals that the
multinomial Naive Bayes with unigram feature outperforms the other techniques
with 84% accuracy on the test set.
| 2,020 | Computation and Language |
DART: Open-Domain Structured Data Record to Text Generation | We present DART, an open domain structured DAta Record to Text generation
dataset with over 82k instances (DARTs). Data-to-Text annotations can be a
costly process, especially when dealing with tables which are the major source
of structured data and contain nontrivial structures. To this end, we propose a
procedure of extracting semantic triples from tables that encodes their
structures by exploiting the semantic dependencies among table headers and the
table title. Our dataset construction framework effectively merged
heterogeneous sources from open domain semantic parsing and dialogue-act-based
meaning representation tasks by utilizing techniques such as: tree ontology
annotation, question-answer pair to declarative sentence conversion, and
predicate unification, all with minimum post-editing. We present systematic
evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to
show that DART (1) poses new challenges to existing data-to-text datasets and
(2) facilitates out-of-domain generalization. Our data and code can be found at
https://github.com/Yale-LILY/dart.
| 2,021 | Computation and Language |
Contextualized Spoken Word Representations from Convolutional
Autoencoders | A lot of work has been done to build text-based language models for
performing different NLP tasks, but not much research has been done in the case
of audio-based language models. This paper proposes a Convolutional Autoencoder
based neural architecture to model syntactically and semantically adequate
contextualized representations of varying length spoken words. The use of such
representations can not only lead to great advances in the audio-based NLP
tasks but can also curtail the loss of information like tone, expression,
accent, etc while converting speech to text to perform these tasks. The
performance of the proposed model is validated by (1) examining the generated
vector space, and (2) evaluating its performance on three benchmark datasets
for measuring word similarities, against existing widely used text-based
language models that are trained on the transcriptions. The proposed model was
able to demonstrate its robustness when compared to the other two
language-based models.
| 2,020 | Computation and Language |
Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords | We present a new release of the Czech-English parallel corpus CzEng 2.0
consisting of over 2 billion words (2 "gigawords") in each language. The corpus
contains document-level information and is filtered with several techniques to
lower the amount of noise. In addition to the data in the previous version of
CzEng, it contains new authentic and also high-quality synthetic parallel data.
CzEng is freely available for research and educational purposes.
| 2,020 | Computation and Language |
Research on Annotation Rules and Recognition Algorithm Based on Phrase
Window | At present, most Natural Language Processing technology is based on the
results of Word Segmentation for Dependency Parsing, which mainly uses an
end-to-end method based on supervised learning. There are two main problems
with this method: firstly, the la-beling rules are complex and the data is too
difficult to label, the workload of which is large; secondly, the algorithm
cannot recognize the multi-granularity and diversity of language components. In
order to solve these two problems, we propose labeling rules based on phrase
windows, and designed corresponding phrase recognition algorithms. The labeling
rule uses phrases as the minimum unit, di-vides sentences into 7 types of
nestable phrase types, and marks the grammatical dependencies between phrases.
The corresponding algorithm, drawing on the idea of identifying the target area
in the image field, can find the start and end positions of various phrases in
the sentence, and realize the synchronous recognition of nested phrases and
grammatical dependencies. The results of the experiment shows that the labeling
rule is convenient and easy to use, and there is no ambiguity; the algorithm is
more grammatically multi-granular and diverse than the end-to-end algorithm.
Experiments on the CPWD dataset improve the accuracy of the end-to-end method
by about 1 point. The corresponding method was applied to the CCL2018
competition, and the first place in the Chinese Metaphor Sentiment Analysis
Task.
| 2,020 | Computation and Language |
Continual BERT: Continual Learning for Adaptive Extractive Summarization
of COVID-19 Literature | The scientific community continues to publish an overwhelming amount of new
research related to COVID-19 on a daily basis, leading to much literature
without little to no attention. To aid the community in understanding the
rapidly flowing array of COVID-19 literature, we propose a novel BERT
architecture that provides a brief yet original summarization of lengthy
papers. The model continually learns on new data in online fashion while
minimizing catastrophic forgetting, thus fitting to the need of the community.
Benchmark and manual examination of its performance show that the model provide
a sound summary of new scientific literature.
| 2,020 | Computation and Language |
The Go Transformer: Natural Language Modeling for Game Play | This work applies natural language modeling to generate plausible strategic
moves in the ancient game of Go. We train the Generative Pretrained Transformer
(GPT-2) to mimic the style of Go champions as archived in Smart Game Format
(SGF), which offers a text description of move sequences. The trained model
further generates valid but previously unseen strategies for Go. Because GPT-2
preserves punctuation and spacing, the raw output of the text generator
provides inputs to game visualization and creative patterns, such as the Sabaki
project's game engine using auto-replays. Results demonstrate that language
modeling can capture both the sequencing format of championship Go games and
their strategic formations. Compared to random game boards, the GPT-2
fine-tuning shows efficient opening move sequences favoring corner play over
less advantageous center and side play. Game generation as a language modeling
task offers novel approaches to more than 40 other board games where historical
text annotation provides training data (e.g., Amazons & Connect 4/6).
| 2,020 | Computation and Language |
scb-mt-en-th-2020: A Large English-Thai Parallel Corpus | The primary objective of our work is to build a large-scale English-Thai
dataset for machine translation. We construct an English-Thai machine
translation dataset with over 1 million segment pairs, curated from various
sources, namely news, Wikipedia articles, SMS messages, task-based dialogs,
web-crawled data and government documents. Methodology for gathering data,
building parallel texts and removing noisy sentence pairs are presented in a
reproducible manner. We train machine translation models based on this dataset.
Our models' performance are comparable to that of Google Translation API (as of
May 2020) for Thai-English and outperform Google when the Open Parallel Corpus
(OPUS) is included in the training data for both Thai-English and English-Thai
translation. The dataset, pre-trained models, and source code to reproduce our
work are available for public use.
| 2,021 | Computation and Language |
An Emergency Medical Services Clinical Audit System driven by Named
Entity Recognition from Deep Learning | Clinical performance audits are routinely performed in Emergency Medical
Services (EMS) to ensure adherence to treatment protocols, to identify
individual areas of weakness for remediation, and to discover systemic
deficiencies to guide the development of the training syllabus. At present,
these audits are performed by manual chart review which is time-consuming and
laborious. In this paper, we present an automatic audit system based on both
the structured and unstructured ambulance case records and clinical notes with
a deep neural network-based named entities recognition model. The dataset used
in this study contained 58,898 unlabelled ambulance incidents encountered by
the Singapore Civil Defence Force from 1st April 2019 to 30th June 2019. A
weakly-supervised training approach was adopted to label the sentences. Later
on, we trained three different models to perform the NER task. All three models
achieve F1 scores of around 0.981 under entity type matching evaluation and
around 0.976 under strict evaluation, while the BiLSTM-CRF model is 1~2 orders
of magnitude lighter and faster than our BERT-based models. Overall, our
approach yielded a named entity recognition model that could reliably identify
clinical entities from unstructured paramedic free-text reports. Our proposed
system may improve the efficiency of clinical performance audits and can also
help with EMS database research.
| 2,020 | Computation and Language |
What Gives the Answer Away? Question Answering Bias Analysis on Video QA
Datasets | Question answering biases in video QA datasets can mislead multimodal model
to overfit to QA artifacts and jeopardize the model's ability to generalize.
Understanding how strong these QA biases are and where they come from helps the
community measure progress more accurately and provide researchers insights to
debug their models. In this paper, we analyze QA biases in popular video
question answering datasets and discover pretrained language models can answer
37-48% questions correctly without using any multimodal context information,
far exceeding the 20% random guess baseline for 5-choose-1 multiple-choice
questions. Our ablation study shows biases can come from annotators and type of
questions. Specifically, annotators that have been seen during training are
better predicted by the model and reasoning, abstract questions incur more
biases than factual, direct questions. We also show empirically that using
annotator-non-overlapping train-test splits can reduce QA biases for video QA
datasets.
| 2,020 | Computation and Language |
Evaluating German Transformer Language Models with Syntactic Agreement
Tests | Pre-trained transformer language models (TLMs) have recently refashioned
natural language processing (NLP): Most state-of-the-art NLP models now operate
on top of TLMs to benefit from contextualization and knowledge induction. To
explain their success, the scientific community conducted numerous analyses.
Besides other methods, syntactic agreement tests were utilized to analyse TLMs.
Most of the studies were conducted for the English language, however. In this
work, we analyse German TLMs. To this end, we design numerous agreement tasks,
some of which consider peculiarities of the German language. Our experimental
results show that state-of-the-art German TLMs generally perform well on
agreement tasks, but we also identify and discuss syntactic structures that
push them to their limits.
| 2,020 | Computation and Language |
Cross-lingual Inductive Transfer to Detect Offensive Language | With the growing use of social media and its availability, many instances of
the use of offensive language have been observed across multiple languages and
domains. This phenomenon has given rise to the growing need to detect the
offensive language used in social media cross-lingually. In OffensEval 2020,
the organizers have released the \textit{multilingual Offensive Language
Identification Dataset} (mOLID), which contains tweets in five different
languages, to detect offensive language. In this work, we introduce a
cross-lingual inductive approach to identify the offensive language in tweets
using the contextual word embedding \textit{XLM-RoBERTa} (XLM-R). We show that
our model performs competitively on all five languages, obtaining the fourth
position in the English task with an F1-score of $0.919$ and eighth position in
the Turkish task with an F1-score of $0.781$. Further experimentation proves
that our model works competitively in a zero-shot learning environment, and is
extensible to other languages.
| 2,020 | Computation and Language |
The curious case of developmental BERTology: On sparsity, transfer
learning, generalization and the brain | In this essay, we explore a point of intersection between deep learning and
neuroscience, through the lens of large language models, transfer learning and
network compression. Just like perceptual and cognitive neurophysiology has
inspired effective deep neural network architectures which in turn make a
useful model for understanding the brain, here we explore how biological neural
development might inspire efficient and robust optimization procedures which in
turn serve as a useful model for the maturation and aging of the brain.
| 2,020 | Computation and Language |
ISA: An Intelligent Shopping Assistant | Despite the growth of e-commerce, brick-and-mortar stores are still the
preferred destinations for many people. In this paper, we present ISA, a
mobile-based intelligent shopping assistant that is designed to improve
shopping experience in physical stores. ISA assists users by leveraging
advanced techniques in computer vision, speech processing, and natural language
processing. An in-store user only needs to take a picture or scan the barcode
of the product of interest, and then the user can talk to the assistant about
the product. The assistant can also guide the user through the purchase process
or recommend other similar products to the user. We take a data-driven approach
in building the engines of ISA's natural language processing component, and the
engines achieve good performance.
| 2,020 | Computation and Language |
Language Modeling with Reduced Densities | This work originates from the observation that today's state-of-the-art
statistical language models are impressive not only for their performance, but
also - and quite crucially - because they are built entirely from correlations
in unstructured text data. The latter observation prompts a fundamental
question that lies at the heart of this paper: What mathematical structure
exists in unstructured text data? We put forth enriched category theory as a
natural answer. We show that sequences of symbols from a finite alphabet, such
as those found in a corpus of text, form a category enriched over
probabilities. We then address a second fundamental question: How can this
information be stored and modeled in a way that preserves the categorical
structure? We answer this by constructing a functor from our enriched category
of text to a particular enriched category of reduced density operators. The
latter leverages the Loewner order on positive semidefinite operators, which
can further be interpreted as a toy example of entailment.
| 2,021 | Computation and Language |
Research on multi-dimensional end-to-end phrase recognition algorithm
based on background knowledge | At present, the deep end-to-end method based on supervised learning is used
in entity recognition and dependency analysis. There are two problems in this
method: firstly, background knowledge cannot be introduced; secondly, multi
granularity and nested features of natural language cannot be recognized. In
order to solve these problems, the annotation rules based on phrase window are
proposed, and the corresponding multi-dimensional end-to-end phrase recognition
algorithm is designed. This annotation rule divides sentences into seven types
of nested phrases, and indicates the dependency between phrases. The algorithm
can not only introduce background knowledge, recognize all kinds of nested
phrases in sentences, but also recognize the dependency between phrases. The
experimental results show that the annotation rule is easy to use and has no
ambiguity; the matching algorithm is more consistent with the multi granularity
and diversity characteristics of syntax than the traditional end-to-end
algorithm. The experiment on CPWD dataset, by introducing background knowledge,
the new algorithm improves the accuracy of the end-to-end method by more than
one point. The corresponding method was applied to the CCL 2018 competition and
won the first place in the task of Chinese humor type recognition.
| 2,020 | Computation and Language |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex
Question Answering over Knowledge Base | Complex question answering over knowledge base (Complex KBQA) is challenging
because it requires various compositional reasoning capabilities, such as
multi-hop inference, attribute comparison, set operation. Existing benchmarks
have some shortcomings that limit the development of Complex KBQA: 1) they only
provide QA pairs without explicit reasoning processes; 2) questions are poor in
diversity or scale. To this end, we introduce KQA Pro, a dataset for Complex
KBQA including ~120K diverse natural language questions. We introduce a
compositional and interpretable programming language KoPL to represent the
reasoning process of complex questions. For each question, we provide the
corresponding KoPL program and SPARQL query, so that KQA Pro serves for both
KBQA and semantic parsing tasks. Experimental results show that SOTA KBQA
methods cannot achieve promising results on KQA Pro as on current datasets,
which suggests that KQA Pro is challenging and Complex KBQA requires further
research efforts. We also treat KQA Pro as a diagnostic dataset for testing
multiple reasoning skills, conduct a thorough evaluation of existing models and
discuss further directions for Complex KBQA. Our codes and datasets can be
obtained from https://github.com/shijx12/KQAPro_Baselines.
| 2,022 | Computation and Language |
Audio-Visual Understanding of Passenger Intents for In-Cabin
Conversational Agents | Building multimodal dialogue understanding capabilities situated in the
in-cabin context is crucial to enhance passenger comfort in autonomous vehicle
(AV) interaction systems. To this end, understanding passenger intents from
spoken interactions and vehicle vision systems is a crucial component for
developing contextual and visually grounded conversational agents for AV.
Towards this goal, we explore AMIE (Automated-vehicle Multimodal In-cabin
Experience), the in-cabin agent responsible for handling multimodal
passenger-vehicle interactions. In this work, we discuss the benefits of a
multimodal understanding of in-cabin utterances by incorporating
verbal/language input together with the non-verbal/acoustic and visual clues
from inside and outside the vehicle. Our experimental results outperformed
text-only baselines as we achieved improved performances for intent detection
with a multimodal approach.
| 2,020 | Computation and Language |
Best-First Beam Search | Decoding for many NLP tasks requires an effective heuristic algorithm for
approximating exact search since the problem of searching the full output space
is often intractable, or impractical in many settings. The default algorithm
for this job is beam search -- a pruned version of breadth-first search. Quite
surprisingly, beam search often returns better results than exact inference due
to beneficial search bias for NLP tasks. In this work, we show that the
standard implementation of beam search can be made up to 10x faster in
practice. Our method assumes that the scoring function is monotonic in the
sequence length, which allows us to safely prune hypotheses that cannot be in
the final set of hypotheses early on. We devise effective monotonic
approximations to popular nonmonontic scoring functions, including length
normalization and mutual information decoding. Lastly, we propose a
memory-reduced variant of Best-First Beam Search, which has a similar
beneficial search bias in terms of downstream performance, but runs in a
fraction of the time.
| 2,022 | Computation and Language |
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases | With the rapid development of knowledge bases (KBs), link prediction task,
which completes KBs with missing facts, has been broadly studied in especially
binary relational KBs (a.k.a knowledge graph) with powerful tensor
decomposition related methods. However, the ubiquitous n-ary relational KBs
with higher-arity relational facts are paid less attention, in which existing
translation based and neural network based approaches have weak expressiveness
and high complexity in modeling various relations. Tensor decomposition has not
been considered for n-ary relational KBs, while directly extending tensor
decomposition related methods of binary relational KBs to the n-ary case does
not yield satisfactory results due to exponential model complexity and their
strong assumptions on binary relations. To generalize tensor decomposition for
n-ary relational KBs, in this work, we propose GETD, a generalized model based
on Tucker decomposition and Tensor Ring decomposition. The existing negative
sampling technique is also generalized to the n-ary case for GETD. In addition,
we theoretically prove that GETD is fully expressive to completely represent
any KBs. Extensive evaluations on two representative n-ary relational KB
datasets demonstrate the superior performance of GETD, significantly improving
the state-of-the-art methods by over 15\%. Moreover, GETD further obtains the
state-of-the-art results on the benchmark binary relational KB datasets.
| 2,020 | Computation and Language |
Improving Conversational Recommender Systems via Knowledge Graph based
Semantic Fusion | Conversational recommender systems (CRS) aim to recommend high-quality items
to users through interactive conversations. Although several efforts have been
made for CRS, two major issues still remain to be solved. First, the
conversation data itself lacks of sufficient contextual information for
accurately understanding users' preference. Second, there is a semantic gap
between natural language expression and item-level user preference. To address
these issues, we incorporate both word-oriented and entity-oriented knowledge
graphs (KG) to enhance the data representations in CRSs, and adopt Mutual
Information Maximization to align the word-level and entity-level semantic
spaces. Based on the aligned semantic representations, we further develop a
KG-enhanced recommender component for making accurate recommendations, and a
KG-enhanced dialog component that can generate informative keywords or entities
in the response text. Extensive experiments have demonstrated the effectiveness
of our approach in yielding better performance on both recommendation and
conversation tasks.
| 2,020 | Computation and Language |
Learning Neural Textual Representations for Citation Recommendation | With the rapid growth of the scientific literature, manually selecting
appropriate citations for a paper is becoming increasingly challenging and
time-consuming. While several approaches for automated citation recommendation
have been proposed in the recent years, effective document representations for
citation recommendation are still elusive to a large extent. For this reason,
in this paper we propose a novel approach to citation recommendation which
leverages a deep sequential representation of the documents (Sentence-BERT)
cascaded with Siamese and triplet networks in a submodular scoring function. To
the best of our knowledge, this is the first approach to combine deep
representations and submodular selection for a task of citation recommendation.
Experiments have been carried out using a popular benchmark dataset - the ACL
Anthology Network corpus - and evaluated against baselines and a
state-of-the-art approach using metrics such as the MRR and F1-at-k score. The
results show that the proposed approach has been able to outperform all the
compared approaches in every measured metric.
| 2,020 | Computation and Language |
Automatic Detection of Sexist Statements Commonly Used at the Workplace | Detecting hate speech in the workplace is a unique classification task, as
the underlying social context implies a subtler version of conventional hate
speech. Applications regarding a state-of the-art workplace sexism detection
model include aids for Human Resources departments, AI chatbots and sentiment
analysis. Most existing hate speech detection methods, although robust and
accurate, focus on hate speech found on social media, specifically Twitter. The
context of social media is much more anonymous than the workplace, therefore it
tends to lend itself to more aggressive and "hostile" versions of sexism.
Therefore, datasets with large amounts of "hostile" sexism have a slightly
easier detection task since "hostile" sexist statements can hinge on a couple
words that, regardless of context, tip the model off that a statement is
sexist. In this paper we present a dataset of sexist statements that are more
likely to be said in the workplace as well as a deep learning model that can
achieve state-of-the art results. Previous research has created
state-of-the-art models to distinguish "hostile" and "benevolent" sexism based
simply on aggregated Twitter data. Our deep learning methods, initialized with
GloVe or random word embeddings, use LSTMs with attention mechanisms to
outperform those models on a more diverse, filtered dataset that is more
targeted towards workplace sexism, leading to an F1 score of 0.88.
| 2,020 | Computation and Language |
Analysis of Predictive Coding Models for Phonemic Representation
Learning in Small Datasets | Neural network models using predictive coding are interesting from the
viewpoint of computational modelling of human language acquisition, where the
objective is to understand how linguistic units could be learned from speech
without any labels. Even though several promising predictive coding -based
learning algorithms have been proposed in the literature, it is currently
unclear how well they generalise to different languages and training dataset
sizes. In addition, despite that such models have shown to be effective
phonemic feature learners, it is unclear whether minimisation of the predictive
loss functions of these models also leads to optimal phoneme-like
representations. The present study investigates the behaviour of two predictive
coding models, Autoregressive Predictive Coding and Contrastive Predictive
Coding, in a phoneme discrimination task (ABX task) for two languages with
different dataset sizes. Our experiments show a strong correlation between the
autoregressive loss and the phoneme discrimination scores with the two
datasets. However, to our surprise, the CPC model shows rapid convergence
already after one pass over the training data, and, on average, its
representations outperform those of APC on both languages.
| 2,020 | Computation and Language |
A Survey on Transfer Learning in Natural Language Processing | Deep learning models usually require a huge amount of data. However, these
large datasets are not always attainable. This is common in many challenging
NLP tasks. Consider Neural Machine Translation, for instance, where curating
such large datasets may not be possible specially for low resource languages.
Another limitation of deep learning models is the demand for huge computing
resources. These obstacles motivate research to question the possibility of
knowledge transfer using large trained models. The demand for transfer learning
is increasing as many large models are emerging. In this survey, we feature the
recent transfer learning advances in the field of NLP. We also provide a
taxonomy for categorizing different transfer learning approaches from the
literature.
| 2,020 | Computation and Language |
Mental representations of objects reflect the ways in which we interact
with them | In order to interact with objects in our environment, humans rely on an
understanding of the actions that can be performed on them, as well as their
properties. When considering concrete motor actions, this knowledge has been
called the object affordance. Can this notion be generalized to any type of
interaction that one can have with an object? In this paper we introduce a
method to represent objects in a space where each dimension corresponds to a
broad mode of interaction, based on verb selectional preferences in text
corpora. This object embedding makes it possible to predict human judgments of
verb applicability to objects better than a variety of alternative approaches.
Furthermore, we show that the dimensions in this space can be used to predict
categorical and functional dimensions in a state-of-the-art mental
representation of objects, derived solely from human judgements of object
similarity. These results suggest that interaction knowledge accounts for a
large part of mental representations of objects.
| 2,021 | Computation and Language |
Neural relation extraction: a survey | Neural relation extraction discovers semantic relations between entities from
unstructured text using deep learning methods. In this study, we present a
comprehensive review of methods on neural network based relation extraction. We
discuss advantageous and incompetent sides of existing studies and investigate
additional research directions and improvement ideas in this field.
| 2,020 | Computation and Language |
Chatbot: A Conversational Agent employed with Named Entity Recognition
Model using Artificial Neural Network | Chatbot is a technology that is used to mimic human behavior using natural
language. There are different types of Chatbot that can be used as
conversational agent in various business domains in order to increase the
customer service and satisfaction. For any business domain, it requires a
knowledge base to be built for that domain and design an information retrieval
based system that can respond the user with a piece of documentation or
generated sentences. The core component of a Chatbot is Natural Language
Understanding (NLU) which has been impressively improved by deep learning
methods. But we often lack such properly built NLU modules and requires more
time to build it from scratch for high quality conversations. This may
encourage fresh learners to build a Chatbot from scratch with simple
architecture and using small dataset, although it may have reduced
functionality, rather than building high quality data driven methods. This
research focuses on Named Entity Recognition (NER) and Intent Classification
models which can be integrated into NLU service of a Chatbot. Named entities
will be inserted manually in the knowledge base and automatically detected in a
given sentence. The NER model in the proposed architecture is based on
artificial neural network which is trained on manually created entities and
evaluated using CoNLL-2003 dataset.
| 2,020 | Computation and Language |
Cooking Is All About People: Comment Classification On Cookery Channels
Using BERT and Classification Models (Malayalam-English Mix-Code) | The scope of a lucrative career promoted by Google through its video
distribution platform YouTube has attracted a large number of users to become
content creators. An important aspect of this line of work is the feedback
received in the form of comments which show how well the content is being
received by the audience. However, volume of comments coupled with spam and
limited tools for comment classification makes it virtually impossible for a
creator to go through each and every comment and gather constructive feedback.
Automatic classification of comments is a challenge even for established
classification models, since comments are often of variable lengths riddled
with slang, symbols and abbreviations. This is a greater challenge where
comments are multilingual as the messages are often rife with the respective
vernacular. In this work, we have evaluated top-performing classification
models for classifying comments which are a mix of different combinations of
English and Malayalam (only English, only Malayalam and Mix of English and
Malayalam). The statistical analysis of results indicates that Multinomial
Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random
Forest and Decision Trees offer similar level of accuracy in comment
classification. Further, we have also evaluated 3 multilingual transformer
based language models (BERT, DISTILBERT and XLM) and compared their performance
to the traditional machine learning classification techniques. XLM was the
top-performing BERT model with an accuracy of 67.31. Random Forest with Term
Frequency Vectorizer was the best performing model out of all the traditional
classification models with an accuracy of 63.59.
| 2,020 | Computation and Language |
Open Domain Suggestion Mining Leveraging Fine-Grained Analysis | Suggestion mining tasks are often semantically complex and lack sophisticated
methodologies that can be applied to real-world data. The presence of
suggestions across a large diversity of domains and the absence of large
labelled and balanced datasets render this task particularly challenging to
deal with. In an attempt to overcome these challenges, we propose a two-tier
pipeline that leverages Discourse Marker based oversampling and fine-grained
suggestion mining techniques to retrieve suggestions from online forums.
Through extensive comparison on a real-world open-domain suggestion dataset, we
demonstrate how the oversampling technique combined with transformer based
fine-grained analysis can beat the state of the art. Additionally, we perform
extensive qualitative and qualitative analysis to give construct validity to
our proposed pipeline. Finally, we discuss the practical, computational and
reproducibility aspects of the deployment of our pipeline across the web.
| 2,020 | Computation and Language |
Building Interpretable Interaction Trees for Deep NLP Models | This paper proposes a method to disentangle and quantify interactions among
words that are encoded inside a DNN for natural language processing. We
construct a tree to encode salient interactions extracted by the DNN. Six
metrics are proposed to analyze properties of interactions between constituents
in a sentence. The interaction is defined based on Shapley values of words,
which are considered as an unbiased estimation of word contributions to the
network prediction. Our method is used to quantify word interactions encoded
inside the BERT, ELMo, LSTM, CNN, and Transformer networks. Experimental
results have provided a new perspective to understand these DNNs, and have
demonstrated the effectiveness of our method.
| 2,021 | Computation and Language |
Normalizador Neural de Datas e Endere\c{c}os | Documents of any kind present a wide variety of date and address formats, in
some cases dates can be written entirely in full or even have different types
of separators. The pattern disorder in addresses is even greater due to the
greater possibility of interchanging between streets, neighborhoods, cities and
states. In the context of natural language processing, problems of this nature
are handled by rigid tools such as ReGex or DateParser, which are efficient as
long as the expected input is pre-configured. When these algorithms are given
an unexpected format, errors and unwanted outputs happen. To circumvent this
challenge, we present a solution with deep neural networks state of art T5 that
treats non-preconfigured formats of dates and addresses with accuracy above 90%
in some cases. With this model, our proposal brings generalization to the task
of normalizing dates and addresses. We also deal with this problem with noisy
data that simulates possible errors in the text.
| 2,020 | Computation and Language |
Segmentation Approach for Coreference Resolution Task | In coreference resolution, it is important to consider all members of a
coreference cluster and decide about all of them at once. This technique can
help to avoid losing precision and also in finding long-distance relations. The
presented paper is a report of an ongoing study on an idea which proposes a new
approach for coreference resolution which can resolve all coreference mentions
to a given mention in the document in one pass. This has been accomplished by
defining an embedding method for the position of all members of a coreference
cluster in a document and resolving all of them for a given mention. In the
proposed method, the BERT model has been used for encoding the documents and a
head network designed to capture the relations between the embedded tokens.
These are then converted to the proposed span position embedding matrix which
embeds the position of all coreference mentions in the document. We tested this
idea on CoNLL 2012 dataset and although the preliminary results from this
method do not quite meet the state-of-the-art results, they are promising and
they can capture features like long-distance relations better than the other
approaches.
| 2,020 | Computation and Language |
A Novel BGCapsule Network for Text Classification | Several text classification tasks such as sentiment analysis, news
categorization, multi-label classification and opinion classification are
challenging problems even for modern deep learning networks. Recently, Capsule
Networks (CapsNets) are proposed for image classification. It has been shown
that CapsNets have several advantages over Convolutional Neural Networks
(CNNs), while their validity in the domain of text has been less explored. In
this paper, we propose a novel hybrid architecture viz., BGCapsule, which is a
Capsule model preceded by an ensemble of Bidirectional Gated Recurrent Units
(BiGRU) for several text classification tasks. We employed an ensemble of
Bidirectional GRUs for feature extraction layer preceding the primary capsule
layer. The hybrid architecture, after performing basic pre-processing steps,
consists of five layers: an embedding layer based on GloVe, a BiGRU based
ensemble layer, a primary capsule layer, a flatten layer and fully connected
ReLU layer followed by a fully connected softmax layer. In order to evaluate
the effectiveness of BGCapsule, we conducted extensive experiments on five
benchmark datasets (ranging from 10,000 records to 700,000 records) including
Movie Review (MR Imdb 2005), AG News dataset, Dbpedia ontology dataset, Yelp
Review Full dataset and Yelp review polarity dataset. These benchmarks cover
several text classification tasks such as news categorization, sentiment
analysis, multiclass classification, multi-label classification and opinion
classification. We found that our proposed architecture (BGCapsule) achieves
better accuracy compared to the existing methods without the help of any
external linguistic knowledge such as positive sentiment keywords and negative
sentiment keywords. Further, BGCapsule converged faster compared to other
extant techniques.
| 2,020 | Computation and Language |
Tweets Sentiment Analysis via Word Embeddings and Machine Learning
Techniques | Sentiment analysis of social media data consists of attitudes, assessments,
and emotions which can be considered a way human think. Understanding and
classifying the large collection of documents into positive and negative
aspects are a very difficult task. Social networks such as Twitter, Facebook,
and Instagram provide a platform in order to gather information about peoples
sentiments and opinions. Considering the fact that people spend hours daily on
social media and share their opinion on various different topics helps us
analyze sentiments better. More and more companies are using social media tools
to provide various services and interact with customers. Sentiment Analysis
(SA) classifies the polarity of given tweets to positive and negative tweets in
order to understand the sentiments of the public. This paper aims to perform
sentiment analysis of real-time 2019 election twitter data using the feature
selection model word2vec and the machine learning algorithm random forest for
sentiment classification. Word2vec with Random Forest improves the accuracy of
sentiment analysis significantly compared to traditional methods such as BOW
and TF-IDF. Word2vec improves the quality of features by considering contextual
semantics of words in a text hence improving the accuracy of machine learning
and sentiment analysis.
| 2,020 | Computation and Language |
Unsupervised Online Grounding of Natural Language during Human-Robot
Interactions | Allowing humans to communicate through natural language with robots requires
connections between words and percepts. The process of creating these
connections is called symbol grounding and has been studied for nearly three
decades. Although many studies have been conducted, not many considered
grounding of synonyms and the employed algorithms either work only offline or
in a supervised manner. In this paper, a cross-situational learning based
grounding framework is proposed that allows grounding of words and phrases
through corresponding percepts without human supervision and online, i.e. it
does not require any explicit training phase, but instead updates the obtained
mappings for every new encountered situation. The proposed framework is
evaluated through an interaction experiment between a human tutor and a robot,
and compared to an existing unsupervised grounding framework. The results show
that the proposed framework is able to ground words through their corresponding
percepts online and in an unsupervised manner, while outperforming the baseline
framework.
| 2,020 | Computation and Language |
Discourse Coherence, Reference Grounding and Goal Oriented Dialogue | Prior approaches to realizing mixed-initiative human--computer referential
communication have adopted information-state or collaborative problem-solving
approaches. In this paper, we argue for a new approach, inspired by
coherence-based models of discourse such as SDRT \cite{asher-lascarides:2003a},
in which utterances attach to an evolving discourse structure and the
associated knowledge graph of speaker commitments serves as an interface to
real-world reasoning and conversational strategy. As first steps towards
implementing the approach, we describe a simple dialogue system in a
referential communication domain that accumulates constraints across discourse,
interprets them using a learned probabilistic model, and plans clarification
using reinforcement learning.
| 2,020 | Computation and Language |
Less is More: Rejecting Unreliable Reviews for Product Question
Answering | Promptly and accurately answering questions on products is important for
e-commerce applications. Manually answering product questions (e.g. on
community question answering platforms) results in slow response and does not
scale. Recent studies show that product reviews are a good source for
real-time, automatic product question answering (PQA). In the literature, PQA
is formulated as a retrieval problem with the goal to search for the most
relevant reviews to answer a given product question. In this paper, we focus on
the issue of answerability and answer reliability for PQA using reviews. Our
investigation is based on the intuition that many questions may not be
answerable with a finite set of reviews. When a question is not answerable, a
system should return nil answers rather than providing a list of irrelevant
reviews, which can have significant negative impact on user experience.
Moreover, for answerable questions, only the most relevant reviews that answer
the question should be included in the result. We propose a conformal
prediction based framework to improve the reliability of PQA systems, where we
reject unreliable answers so that the returned results are more concise and
accurate at answering the product question, including returning nil answers for
unanswerable questions. Experiments on a widely used Amazon dataset show
encouraging results of our proposed framework. More broadly, our results
demonstrate a novel and effective application of conformal methods to a
retrieval task.
| 2,020 | Computation and Language |
Automatic Personality Prediction; an Enhanced Method Using Ensemble
Modeling | Human personality is significantly represented by those words which he/she
uses in his/her speech or writing. As a consequence of spreading the
information infrastructures (specifically the Internet and social media), human
communications have reformed notably from face to face communication.
Generally, Automatic Personality Prediction (or Perception) (APP) is the
automated forecasting of the personality on different types of human
generated/exchanged contents (like text, speech, image, video, etc.). The major
objective of this study is to enhance the accuracy of APP from the text. To
this end, we suggest five new APP methods including term frequency
vector-based, ontology-based, enriched ontology-based, latent semantic analysis
(LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the
base ones, contribute to each other to enhance the APP accuracy through
ensemble modeling (stacking) based on a hierarchical attention network (HAN) as
the meta-model. The results show that ensemble modeling enhances the accuracy
of APP.
| 2,022 | Computation and Language |
DISCO PAL: Diachronic Spanish Sonnet Corpus with Psychological and
Affective Labels | Nowadays, there are many applications of text mining over corpora from
different languages. However, most of them are based on texts in prose, lacking
applications that work with poetry texts. An example of an application of text
mining in poetry is the usage of features derived from their individual words
in order to capture the lexical, sublexical and interlexical meaning, and infer
the General Affective Meaning (GAM) of the text. However, even though this
proposal has been proved as useful for poetry in some languages, there is a
lack of studies for both Spanish poetry and for highly-structured poetic
compositions such as sonnets. This article presents a study over an annotated
corpus of Spanish sonnets, in order to analyse if it is possible to build
features from their individual words for predicting their GAM. The purpose of
this is to model sonnets at an affective level. The article also analyses the
relationship between the GAM of the sonnets and the content itself. For this,
we consider the content from a psychological perspective, identifying with tags
when a sonnet is related to a specific term. Then, we study how GAM changes
according to each of those psychological terms.
The corpus used contains 274 Spanish sonnets from authors of different
centuries, from 15th to 19th. This corpus was annotated by different domain
experts. The experts annotated the poems with affective and lexico-semantic
features, as well as with domain concepts that belong to psychology. Thanks to
this, the corpus of sonnets can be used in different applications, such as
poetry recommender systems, personality text mining studies of the authors, or
the usage of poetry for therapeutic purposes.
| 2,021 | Computation and Language |
Principal Word Vectors | We generalize principal component analysis for embedding words into a vector
space. The generalization is made in two major levels. The first is to
generalize the concept of the corpus as a counting process which is defined by
three key elements vocabulary set, feature (annotation) set, and context. This
generalization enables the principal word embedding method to generate word
vectors with regard to different types of contexts and different types of
annotations provided for a corpus. The second is to generalize the
transformation step used in most of the word embedding methods. To this end, we
define two levels of transformations. The first is a quadratic transformation,
which accounts for different types of weighting over the vocabulary units and
contextual features. Second is an adaptive non-linear transformation, which
reshapes the data distribution to be meaningful to principal component
analysis. The effect of these generalizations on the word vectors is
intrinsically studied with regard to the spread and the discriminability of the
word vectors. We also provide an extrinsic evaluation of the contribution of
the principal word vectors on a word similarity benchmark and the task of
dependency parsing. Our experiments are finalized by a comparison between the
principal word vectors and other sets of word vectors generated with popular
word embedding methods. The results obtained from our intrinsic evaluation
metrics show that the spread and the discriminability of the principal word
vectors are higher than that of other word embedding methods. The results
obtained from the extrinsic evaluation metrics show that the principal word
vectors are better than some of the word embedding methods and on par with
popular methods of word embedding.
| 2,020 | Computation and Language |
Greedy Transition-Based Dependency Parsing with Discrete and Continuous
Supertag Features | We study the effect of rich supertag features in greedy transition-based
dependency parsing. While previous studies have shown that sparse boolean
features representing the 1-best supertag of a word can improve parsing
accuracy, we show that we can get further improvements by adding a continuous
vector representation of the entire supertag distribution for a word. In this
way, we achieve the best results for greedy transition-based parsing with
supertag features with $88.6\%$ LAS and $90.9\%$ UASon the English Penn
Treebank converted to Stanford Dependencies.
| 2,020 | Computation and Language |
Targeting the Benchmark: On Methodology in Current Natural Language
Processing Research | It has become a common pattern in our field: One group introduces a language
task, exemplified by a dataset, which they argue is challenging enough to serve
as a benchmark. They also provide a baseline model for it, which then soon is
improved upon by other groups. Often, research efforts then move on, and the
pattern repeats itself. What is typically left implicit is the argumentation
for why this constitutes progress, and progress towards what. In this paper, we
try to step back for a moment from this pattern and work out possible
argumentations and their parts.
| 2,020 | Computation and Language |
CompRes: A Dataset for Narrative Structure in News | This paper addresses the task of automatically detecting narrative structures
in raw texts. Previous works have utilized the oral narrative theory by Labov
and Waletzky to identify various narrative elements in personal stories texts.
Instead, we direct our focus to news articles, motivated by their growing
social impact as well as their role in creating and shaping public opinion.
We introduce CompRes -- the first dataset for narrative structure in news
media. We describe the process in which the dataset was constructed: first, we
designed a new narrative annotation scheme, better suited for news media, by
adapting elements from the narrative theory of Labov and Waletzky (Complication
and Resolution) and adding a new narrative element of our own (Success); then,
we used that scheme to annotate a set of 29 English news articles (containing
1,099 sentences) collected from news and partisan websites. We use the
annotated dataset to train several supervised models to identify the different
narrative elements, achieving an $F_1$ score of up to 0.7. We conclude by
suggesting several promising directions for future work.
| 2,023 | Computation and Language |
Advances of Transformer-Based Models for News Headline Generation | Pretrained language models based on Transformer architecture are the reason
for recent breakthroughs in many areas of NLP, including sentiment analysis,
question answering, named entity recognition. Headline generation is a special
kind of text summarization task. Models need to have strong natural language
understanding that goes beyond the meaning of individual words and sentences
and an ability to distinguish essential information to succeed in it. In this
paper, we fine-tune two pretrained Transformer-based models (mBART and
BertSumAbs) for that task and achieve new state-of-the-art results on the RIA
and Lenta datasets of Russian news. BertSumAbs increases ROUGE on average by
2.9 and 2.0 points respectively over previous best score achieved by
Phrase-Based Attentional Transformer and CopyNet.
| 2,020 | Computation and Language |
Handling Collocations in Hierarchical Latent Tree Analysis for Topic
Modeling | Topic modeling has been one of the most active research areas in machine
learning in recent years. Hierarchical latent tree analysis (HLTA) has been
recently proposed for hierarchical topic modeling and has shown superior
performance over state-of-the-art methods. However, the models used in HLTA
have a tree structure and cannot represent the different meanings of multiword
expressions sharing the same word appropriately. Therefore, we propose a method
for extracting and selecting collocations as a preprocessing step for HLTA. The
selected collocations are replaced with single tokens in the bag-of-words model
before running HLTA. Our empirical evaluation shows that the proposed method
led to better performance of HLTA on three of the four data sets tested.
| 2,020 | Computation and Language |
What Can We Learn From Almost a Decade of Food Tweets | We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow
domain related to food, drinks, eating and drinking. The corpus has been
collected over time-span of over 8 years and includes over 2 million tweets
entailed with additional useful data. We also separate two sub-corpora of
question and answer tweets and sentiment annotated tweets. We analyse contents
of the corpus and demonstrate use-cases for the sub-corpora by training
domain-specific question-answering and sentiment-analysis models using data
from the corpus.
| 2,020 | Computation and Language |
Pragmatic information in translation: a corpus-based study of tense and
mood in English and German | Grammatical tense and mood are important linguistic phenomena to consider in
natural language processing (NLP) research. We consider the correspondence
between English and German tense and mood in translation. Human translators do
not find this correspondence easy, and as we will show through careful
analysis, there are no simplistic ways to map tense and mood from one language
to another. Our observations about the challenges of human translation of tense
and mood have important implications for multilingual NLP. Of particular
importance is the challenge of modeling tense and mood in rule-based,
phrase-based statistical and neural machine translation.
| 2,020 | Computation and Language |
Temporally Correlated Task Scheduling for Sequence Learning | Sequence learning has attracted much research attention from the machine
learning community in recent years. In many applications, a sequence learning
task is usually associated with multiple temporally correlated auxiliary tasks,
which are different in terms of how much input information to use or which
future step to predict. For example, (i) in simultaneous machine translation,
one can conduct translation under different latency (i.e., how many input words
to read/wait before translation); (ii) in stock trend forecasting, one can
predict the price of a stock in different future days (e.g., tomorrow, the day
after tomorrow). While it is clear that those temporally correlated tasks can
help each other, there is a very limited exploration on how to better leverage
multiple auxiliary tasks to boost the performance of the main task. In this
work, we introduce a learnable scheduler to sequence learning, which can
adaptively select auxiliary tasks for training depending on the model status
and the current training data. The scheduler and the model for the main task
are jointly trained through bi-level optimization. Experiments show that our
method significantly improves the performance of simultaneous machine
translation and stock trend forecasting.
| 2,021 | Computation and Language |
Topic Modeling on User Stories using Word Mover's Distance | Requirements elicitation has recently been complemented with crowd-based
techniques, which continuously involve large, heterogeneous groups of users who
express their feedback through a variety of media. Crowd-based elicitation has
great potential for engaging with (potential) users early on but also results
in large sets of raw and unstructured feedback. Consolidating and analyzing
this feedback is a key challenge for turning it into sensible user
requirements. In this paper, we focus on topic modeling as a means to identify
topics within a large set of crowd-generated user stories and compare three
approaches: (1) a traditional approach based on Latent Dirichlet Allocation,
(2) a combination of word embeddings and principal component analysis, and (3)
a combination of word embeddings and Word Mover's Distance. We evaluate the
approaches on a publicly available set of 2,966 user stories written and
categorized by crowd workers. We found that a combination of word embeddings
and Word Mover's Distance is most promising. Depending on the word embeddings
we use in our approaches, we manage to cluster the user stories in two ways:
one that is closer to the original categorization and another that allows new
insights into the dataset, e.g. to find potentially new categories.
Unfortunately, no measure exists to rate the quality of our results
objectively. Still, our findings provide a basis for future work towards
analyzing crowd-sourced user stories.
| 2,020 | Computation and Language |
SacreROUGE: An Open-Source Library for Using and Developing
Summarization Evaluation Metrics | We present SacreROUGE, an open-source library for using and developing
summarization evaluation metrics. SacreROUGE removes many obstacles that
researchers face when using or developing metrics: (1) The library provides
Python wrappers around the official implementations of existing evaluation
metrics so they share a common, easy-to-use interface; (2) it provides
functionality to evaluate how well any metric implemented in the library
correlates to human-annotated judgments, so no additional code needs to be
written for a new evaluation metric; and (3) it includes scripts for loading
datasets that contain human judgments so they can easily be used for
evaluation. This work describes the design of the library, including the core
Metric interface, the command-line API for evaluating summarization models and
metrics, and the scripts to load and reformat publicly available datasets. The
development of SacreROUGE is ongoing and open to contributions from the
community.
| 2,020 | Computation and Language |
Class LM and word mapping for contextual biasing in End-to-End ASR | In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid
interest in the speech recognition community. They convert speech input to text
units in a single trainable Neural Network model. In ASR, many utterances
contain rich named entities. Such named entities may be user or location
specific and they are not seen during training. A single model makes it
inflexible to utilize dynamic contextual information during inference. In this
paper, we propose to train a context aware E2E model and allow the beam search
to traverse into the context FST during inference. We also propose a simple
method to adjust the cost discrepancy between the context FST and the base
model. This algorithm is able to reduce the named entity utterance WER by 57%
with little accuracy degradation on regular utterances. Although an E2E model
does not need pronunciation dictionary, it's interesting to make use of
existing pronunciation knowledge to improve accuracy. In this paper, we propose
an algorithm to map the rare entity words to common words via pronunciation and
treat the mapped words as an alternative form to the original word during
recognition. This algorithm further reduces the WER on the named entity
utterances by another 31%.
| 2,020 | Computation and Language |
Multi-Dialect Arabic BERT for Country-Level Dialect Identification | Arabic dialect identification is a complex problem for a number of inherent
properties of the language itself. In this paper, we present the experiments
conducted, and the models developed by our competing team, Mawdoo3 AI, along
the way to achieving our winning solution to subtask 1 of the Nuanced Arabic
Dialect Identification (NADI) shared task. The dialect identification subtask
provides 21,000 country-level labeled tweets covering all 21 Arab countries. An
unlabeled corpus of 10M tweets from the same domain is also presented by the
competition organizers for optional use. Our winning solution itself came in
the form of an ensemble of different training iterations of our pre-trained
BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask
at hand. We publicly release the pre-trained language model component of our
winning solution under the name of Multi-dialect-Arabic-BERT model, for any
interested researcher out there.
| 2,020 | Computation and Language |
GloVeInit at SemEval-2020 Task 1: Using GloVe Vector Initialization for
Unsupervised Lexical Semantic Change Detection | This paper presents a vector initialization approach for the SemEval2020 Task
1: Unsupervised Lexical Semantic Change Detection. Given two corpora belonging
to different time periods and a set of target words, this task requires us to
classify whether a word gained or lost a sense over time (subtask 1) and to
rank them on the basis of the changes in their word senses (subtask 2). The
proposed approach is based on using Vector Initialization method to align GloVe
embeddings. The idea is to consecutively train GloVe embeddings for both
corpora, while using the first model to initialize the second one. This paper
is based on the hypothesis that GloVe embeddings are more suited for the Vector
Initialization method than SGNS embeddings. It presents an intuitive reasoning
behind this hypothesis, and also talks about the impact of various factors and
hyperparameters on the performance of the proposed approach. Our model ranks
13th and 10th among 33 teams in the two subtasks. The implementation has been
shared publicly.
| 2,020 | Computation and Language |
Deep or Simple Models for Semantic Tagging? It Depends on your Data
[Experiments] | Semantic tagging, which has extensive applications in text mining, predicts
whether a given piece of text conveys the meaning of a given semantic tag. The
problem of semantic tagging is largely solved with supervised learning and
today, deep learning models are widely perceived to be better for semantic
tagging. However, there is no comprehensive study supporting the popular
belief. Practitioners often have to train different types of models for each
semantic tagging task to identify the best model. This process is both
expensive and inefficient.
We embark on a systematic study to investigate the following question: Are
deep models the best performing model for all semantic tagging tasks? To answer
this question, we compare deep models against "simple models" over datasets
with varying characteristics. Specifically, we select three prevalent deep
models (i.e. CNN, LSTM, and BERT) and two simple models (i.e. LR and SVM), and
compare their performance on the semantic tagging task over 21 datasets.
Results show that the size, the label ratio, and the label cleanliness of a
dataset significantly impact the quality of semantic tagging. Simple models
achieve similar tagging quality to deep models on large datasets, but the
runtime of simple models is much shorter. Moreover, simple models can achieve
better tagging quality than deep models when targeting datasets show worse
label cleanliness and/or more severe imbalance. Based on these findings, our
study can systematically guide practitioners in selecting the right learning
model for their semantic tagging task.
| 2,020 | Computation and Language |
Feature Selection on Noisy Twitter Short Text Messages for Language
Identification | The task of written language identification involves typically the detection
of the languages present in a sample of text. Moreover, a sequence of text may
not belong to a single inherent language but also may be mixture of text
written in multiple languages. This kind of text is generated in large volumes
from social media platforms due to its flexible and user friendly environment.
Such text contains very large number of features which are essential for
development of statistical, probabilistic as well as other kinds of language
models. The large number of features have rich as well as irrelevant and
redundant features which have diverse effect over the performance of the
learning model. Therefore, feature selection methods are significant in
choosing feature that are most relevant for an efficient model. In this
article, we basically consider the Hindi-English language identification task
as Hindi and English are often two most widely spoken languages of India. We
apply different feature selection algorithms across various learning algorithms
in order to analyze the effect of the algorithm as well as the number of
features on the performance of the task. The methodology focuses on the word
level language identification using a novel dataset of 6903 tweets extracted
from Twitter. Various n-gram profiles are examined with different feature
selection algorithms over many classifiers. Finally, an exhaustive comparative
analysis is put forward with respect to the overall experiments conducted for
the task.
| 2,019 | Computation and Language |
I3rab: A New Arabic Dependency Treebank Based on Arabic Grammatical
Theory | Treebanks are valuable linguistic resources that include the syntactic
structure of a language sentence in addition to POS-tags and morphological
features. They are mainly utilized in modeling statistical parsers. Although
the statistical natural language parser has recently become more accurate for
languages such as English, those for the Arabic language still have low
accuracy. The purpose of this paper is to construct a new Arabic dependency
treebank based on the traditional Arabic grammatical theory and the
characteristics of the Arabic language, to investigate their effects on the
accuracy of statistical parsers. The proposed Arabic dependency treebank,
called I3rab, contrasts with existing Arabic dependency treebanks in two main
concepts. The first concept is the approach of determining the main word of the
sentence, and the second concept is the representation of the joined and covert
pronouns. To evaluate I3rab, we compared its performance against a subset of
Prague Arabic Dependency Treebank that shares a comparable level of details.
The conducted experiments show that the percentage improvement reached up to
7.5% in UAS and 18.8% in LAS.
| 2,020 | Computation and Language |
Is Machine Learning Speaking my Language? A Critical Look at the
NLP-Pipeline Across 8 Human Languages | Natural Language Processing (NLP) is increasingly used as a key ingredient in
critical decision-making systems such as resume parsers used in sorting a list
of job candidates. NLP systems often ingest large corpora of human text,
attempting to learn from past human behavior and decisions in order to produce
systems that will make recommendations about our future world. Over 7000 human
languages are being spoken today and the typical NLP pipeline underrepresents
speakers of most of them while amplifying the voices of speakers of other
languages. In this paper, a team including speakers of 8 languages - English,
Chinese, Urdu, Farsi, Arabic, French, Spanish, and Wolof - takes a critical
look at the typical NLP pipeline and how even when a language is technically
supported, substantial caveats remain to prevent full participation. Despite
huge and admirable investments in multilingual support in many tools and
resources, we are still making NLP-guided decisions that systematically and
dramatically underrepresent the voices of much of the world.
| 2,020 | Computation and Language |
HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable
Hyper Projections | Achieving state-of-the-art performance on natural language understanding
tasks typically relies on fine-tuning a fresh model for every task.
Consequently, this approach leads to a higher overall parameter cost, along
with higher technical maintenance for serving multiple models. Learning a
single multi-task model that is able to do well for all the tasks has been a
challenging and yet attractive proposition. In this paper, we propose
\textsc{HyperGrid}, a new approach for highly effective multi-task learning.
The proposed approach is based on a decomposable hypernetwork that learns
grid-wise projections that help to specialize regions in weight matrices for
different tasks. In order to construct the proposed hypernetwork, our method
learns the interactions and composition between a global (task-agnostic) state
and a local task-specific state. We apply our proposed \textsc{HyperGrid} on
the current state-of-the-art T5 model, demonstrating strong performance across
the GLUE and SuperGLUE benchmarks when using only a single multi-task model.
Our method helps bridge the gap between fine-tuning and multi-task learning
approaches.
| 2,020 | Computation and Language |
Stance Detection in Web and Social Media: A Comparative Study | Online forums and social media platforms are increasingly being used to
discuss topics of varying polarities where different people take different
stances. Several methodologies for automatic stance detection from text have
been proposed in literature. To our knowledge, there has not been any
systematic investigation towards their reproducibility, and their comparative
performances. In this work, we explore the reproducibility of several existing
stance detection models, including both neural models and classical
classifier-based models. Through experiments on two datasets -- (i)~the popular
SemEval microblog dataset, and (ii)~a set of health-related online news
articles -- we also perform a detailed comparative analysis of various methods
and explore their shortcomings. Implementations of all algorithms discussed in
this paper are available at
https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.
| 2,019 | Computation and Language |
Neural disambiguation of lemma and part of speech in morphologically
rich languages | We consider the problem of disambiguating the lemma and part of speech of
ambiguous words in morphologically rich languages. We propose a method for
disambiguating ambiguous words in context, using a large un-annotated corpus of
text, and a morphological analyser -- with no manual disambiguation or data
annotation. We assume that the morphological analyser produces multiple
analyses for ambiguous words. The idea is to train recurrent neural networks on
the output that the morphological analyser produces for unambiguous words. We
present performance on POS and lemma disambiguation that reaches or surpasses
the state of the art -- including supervised models -- using no manually
annotated data. We evaluate the method on several morphologically rich
languages.
| 2,020 | Computation and Language |
Do You Have the Right Scissors? Tailoring Pre-trained Language Models
via Monte-Carlo Methods | It has been a common approach to pre-train a language model on a large corpus
and fine-tune it on task-specific data. In practice, we observe that
fine-tuning a pre-trained model on a small dataset may lead to over- and/or
under-estimation problem. In this paper, we propose MC-Tailor, a novel method
to alleviate the above issue in text generation tasks by truncating and
transferring the probability mass from over-estimated regions to
under-estimated ones. Experiments on a variety of text generation datasets show
that MC-Tailor consistently and significantly outperforms the fine-tuning
approach. Our code is available at this url.
| 2,020 | Computation and Language |
Generating Fluent Adversarial Examples for Natural Languages | Efficiently building an adversarial attacker for natural language processing
(NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it
is difficult to make small perturbations along the direction of gradients.
Secondly, the fluency of the generated examples cannot be guaranteed. In this
paper, we propose MHA, which addresses both problems by performing
Metropolis-Hastings sampling, whose proposal is designed with the guidance of
gradients. Experiments on IMDB and SNLI show that our proposed MHA outperforms
the baseline model on attacking capability. Adversarial training with MAH also
leads to better robustness and performance.
| 2,020 | Computation and Language |
Transformer with Depth-Wise LSTM | Increasing the depth of models allows neural models to model complicated
functions but may also lead to optimization issues. The Transformer translation
model employs the residual connection to ensure its convergence. In this paper,
we suggest that the residual connection has its drawbacks, and propose to train
Transformers with the depth-wise LSTM which regards outputs of layers as steps
in time series instead of residual connections, under the motivation that the
vanishing gradient problem suffered by deep networks is the same as recurrent
networks applied to long sequences, while LSTM (Hochreiter and Schmidhuber,
1997) has been proven of good capability in capturing long-distance
relationship, and its design may alleviate some drawbacks of residual
connections while ensuring the convergence. We integrate the computation of
multi-head attention networks and feed-forward networks with the depth-wise
LSTM for the Transformer, which shows how to utilize the depth-wise LSTM like
the residual connection. Our experiment with the 6-layer Transformer shows that
our approach can bring about significant BLEU improvements in both WMT 14
English-German and English-French tasks, and our deep Transformer experiment
demonstrates the effectiveness of the depth-wise LSTM on the convergence of
deep Transformers. Additionally, we propose to measure the impacts of the
layer's non-linearity on the performance by distilling the analyzing layer of
the trained model into a linear transformation and observing the performance
degradation with the replacement. Our analysis results support the more
efficient use of per-layer non-linearity with depth-wise LSTM than with
residual connections.
| 2,020 | Computation and Language |
Paranoid Transformer: Reading Narrative of Madness as Computational
Approach to Creativity | This papers revisits the receptive theory in context of computational
creativity. It presents a case study of a Paranoid Transformer - a fully
autonomous text generation engine with raw output that could be read as the
narrative of a mad digital persona without any additional human post-filtering.
We describe technical details of the generative system, provide examples of
output and discuss the impact of receptive theory, chance discovery and
simulation of fringe mental state on the understanding of computational
creativity.
| 2,020 | Computation and Language |
A Label Attention Model for ICD Coding from Clinical Text | ICD coding is a process of assigning the International Classification of
Disease diagnosis codes to clinical/medical notes documented by health
professionals (e.g. clinicians). This process requires significant human
resources, and thus is costly and prone to error. To handle the problem,
machine learning has been utilized for automatic ICD coding. Previous
state-of-the-art models were based on convolutional neural networks, using a
single/several fixed window sizes. However, the lengths and interdependence
between text fragments related to ICD codes in clinical text vary
significantly, leading to the difficulty of deciding what the best window sizes
are. In this paper, we propose a new label attention model for automatic ICD
coding, which can handle both the various lengths and the interdependence of
the ICD code related text fragments. Furthermore, as the majority of ICD codes
are not frequently used, leading to the extremely imbalanced data issue, we
additionally propose a hierarchical joint learning mechanism extending our
label attention model to handle the issue, using the hierarchical relationships
among the codes. Our label attention model achieves new state-of-the-art
results on three benchmark MIMIC datasets, and the joint learning mechanism
helps improve the performances for infrequent codes.
| 2,020 | Computation and Language |
A Feature Analysis for Multimodal News Retrieval | Content-based information retrieval is based on the information contained in
documents rather than using metadata such as keywords. Most information
retrieval methods are either based on text or image. In this paper, we
investigate the usefulness of multimodal features for cross-lingual news search
in various domains: politics, health, environment, sport, and finance. To this
end, we consider five feature types for image and text and compare the
performance of the retrieval system using different combinations. Experimental
results show that retrieval results can be improved when considering both
visual and textual information. In addition, it is observed that among textual
features entity overlap outperforms word embeddings, while geolocation
embeddings achieve better performance among visual features in the retrieval
task.
| 2,020 | Computation and Language |
GGPONC: A Corpus of German Medical Text with Rich Metadata Based on
Clinical Practice Guidelines | The lack of publicly accessible text corpora is a major obstacle for progress
in natural language processing. For medical applications, unfortunately, all
language communities other than English are low-resourced. In this work, we
present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely
distributable German language corpus based on clinical practice guidelines for
oncology. This corpus is one of the largest ever built from German medical
documents. Unlike clinical documents, clinical guidelines do not contain any
patient-related information and can therefore be used without data protection
restrictions. Moreover, GGPONC is the first corpus for the German language
covering diverse conditions in a large medical subfield and provides a variety
of metadata, such as literature references and evidence levels. By applying and
evaluating existing medical information extraction pipelines for German text,
we are able to draw comparisons for the use of medical language to other
corpora, medical and non-medical ones.
| 2,020 | Computation and Language |
HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social
Good | The paper describes the organisation of the "HateSpeech Detection" (HSD) task
at the VLSP workshop 2019 on detecting the fine-grained presence of hate speech
in Vietnamese textual items (i.e., messages) extracted from Facebook, which is
the most popular social network site (SNS) in Vietnam. The task is organised as
a multi-class classification task and based on a large-scale dataset containing
25,431 Vietnamese textual items from Facebook. The task participants were
challenged to build a classification model that is capable of classifying an
item to one of 3 classes, i.e., "HATE", "OFFENSIVE" and "CLEAN". HSD attracted
a large number of participants and was a popular task at VLSP 2019. In
particular, there were 71 teams signed up for the task, 14 of them submitted
results with 380 valid submissions from 20th September 2019 to 4th October
2019.
| 2,020 | Computation and Language |
An Enhanced Text Classification to Explore Health based Indian
Government Policy Tweets | Government-sponsored policy-making and scheme generations is one of the means
of protecting and promoting the social, economic, and personal development of
the citizens. The evaluation of effectiveness of these schemes done by
government only provide the statistical information in terms of facts and
figures which do not include the in-depth knowledge of public perceptions,
experiences and views on the topic. In this research work, we propose an
improved text classification framework that classifies the Twitter data of
different health-based government schemes. The proposed framework leverages the
language representation models (LR models) BERT, ELMO, and USE. However, these
LR models have less real-time applicability due to the scarcity of the ample
annotated data. To handle this, we propose a novel GloVe word embeddings and
class-specific sentiments based text augmentation approach (named Mod-EDA)
which boosts the performance of text classification task by increasing the size
of labeled data. Furthermore, the trained model is leveraged to identify the
level of engagement of citizens towards these policies in different communities
such as middle-income and low-income groups.
| 2,020 | Computation and Language |
Can neural networks acquire a structural bias from raw linguistic data? | We evaluate whether BERT, a widely used neural network for sentence
processing, acquires an inductive bias towards forming structural
generalizations through pretraining on raw data. We conduct four experiments
testing its preference for structural vs. linear generalizations in different
structure-dependent phenomena. We find that BERT makes a structural
generalization in 3 out of 4 empirical domains---subject-auxiliary inversion,
reflexive binding, and verb tense detection in embedded clauses---but makes a
linear generalization when tested on NPI licensing. We argue that these results
are the strongest evidence so far from artificial learners supporting the
proposition that a structural bias can be acquired from raw data. If this
conclusion is correct, it is tentative evidence that some linguistic universals
can be acquired by learners without innate biases. However, the precise
implications for human language acquisition are unclear, as humans learn
language from significantly less data than BERT.
| 2,020 | Computation and Language |
An Empirical Study on Robustness to Spurious Correlations using
Pre-trained Language Models | Recent work has shown that pre-trained language models such as BERT improve
robustness to spurious correlations in the dataset. Intrigued by these results,
we find that the key to their success is generalization from a small amount of
counterexamples where the spurious correlations do not hold. When such minority
examples are scarce, pre-trained models perform as poorly as models trained
from scratch. In the case of extreme minority, we propose to use multi-task
learning (MTL) to improve generalization. Our experiments on natural language
inference and paraphrase identification show that MTL with the right auxiliary
tasks significantly improves performance on challenging examples without
hurting the in-distribution performance. Further, we show that the gain from
MTL mainly comes from improved generalization from the minority examples. Our
results highlight the importance of data diversity for overcoming spurious
correlations.
| 2,020 | Computation and Language |
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring
Systems | Automatic scoring engines have been used for scoring approximately fifteen
million test-takers in just the last three years. This number is increasing
further due to COVID-19 and the associated automation of education and testing.
Despite such wide usage, the AI-based testing literature of these "intelligent"
models is highly lacking. Most of the papers proposing new models rely only on
quadratic weighted kappa (QWK) based agreement with human raters for showing
model efficacy. However, this effectively ignores the highly multi-feature
nature of essay scoring. Essay scoring depends on features like coherence,
grammar, relevance, sufficiency and, vocabulary. To date, there has been no
study testing Automated Essay Scoring: AES systems holistically on all these
features. With this motivation, we propose a model agnostic adversarial
evaluation scheme and associated metrics for AES systems to test their natural
language understanding capabilities and overall robustness. We evaluate the
current state-of-the-art AES models using the proposed scheme and report the
results on five recent models. These models range from
feature-engineering-based approaches to the latest deep learning algorithms. We
find that AES models are highly overstable. Even heavy modifications(as much as
25%) with content unrelated to the topic of the questions do not decrease the
score produced by the models. On the other hand, irrelevant content, on
average, increases the scores, thus showing that the model evaluation strategy
and rubrics should be reconsidered. We also ask 200 human raters to score both
an original and adversarial response to seeing if humans can detect differences
between the two and whether they agree with the scores assigned by auto scores.
| 2,021 | Computation and Language |
What's in a Name? Are BERT Named Entity Representations just as Good for
any other Name? | We evaluate named entity representations of BERT-based NLP models by
investigating their robustness to replacements from the same typed class in the
input. We highlight that on several tasks while such perturbations are natural,
state of the art trained models are surprisingly brittle. The brittleness
continues even with the recent entity-aware BERT models. We also try to discern
the cause of this non-robustness, considering factors such as tokenization and
frequency of occurrence. Then we provide a simple method that ensembles
predictions from multiple replacements while jointly modeling the uncertainty
of type annotations and label predictions. Experiments on three NLP tasks show
that our method enhances robustness and increases accuracy on both natural and
adversarial datasets.
| 2,020 | Computation and Language |
Our Evaluation Metric Needs an Update to Encourage Generalization | Models that surpass human performance on several popular benchmarks display
significant degradation in performance on exposure to Out of Distribution (OOD)
data. Recent research has shown that models overfit to spurious biases and
`hack' datasets, in lieu of learning generalizable features like humans. In
order to stop the inflation in model performance -- and thus overestimation in
AI systems' capabilities -- we propose a simple and novel evaluation metric,
WOOD Score, that encourages generalization during evaluation.
| 2,020 | Computation and Language |
Language, communication and society: a gender based linguistics analysis | The purpose of this study is to find evidence for supporting the hypothesis
that language is the mirror of our thinking, our prejudices and cultural
stereotypes. In this analysis, a questionnaire was administered to 537 people.
The answers have been analysed to see if gender stereotypes were present such
as the attribution of psychological and behavioural characteristics. In
particular, the aim was to identify, if any, what are the stereotyped images,
which emerge in defining the roles of men and women in modern society.
Moreover, the results given can be a good starting point to understand if
gender stereotypes, and the expectations they produce, can result in
penalization or inequality. If so, the language and its use would create
inherently a gender bias, which influences evaluations both in work settings
both in everyday life.
| 2,015 | Computation and Language |
Questionnaire analysis to define the most suitable survey for port-noise
investigation | The high level of noise pollution affecting the areas between ports and
logistic platforms represents a problem that can be faced from different points
of view. Acoustic monitoring, mapping, short-term measurements, port and road
traffic flows analyses can give useful indications on the strategies to be
proposed for a better management of the problem. A survey campaign through the
preparation of questionnaires to be submitted to the population exposed to
noise in the back-port areas will help to better understand the subjective
point of view. The paper analyses a sample of questions suitable for the
specific research, chosen as part of the wide database of questionnaires
internationally proposed for subjective investigations. The preliminary results
of a first data collection campaign are considered to verify the adequacy of
the number, the type of questions, and the type of sample noise used for the
survey. The questionnaire will be optimized to be distributed in the TRIPLO
project (TRansports and Innovative sustainable connections between Ports and
LOgistic platforms). The results of this survey will be the starting point for
the linguistic investigation carried out in combination with the acoustic
monitoring, to improve understanding the connections between personal feeling
and technical aspects.
| 2,020 | Computation and Language |
CoreGen: Contextualized Code Representation Learning for Commit Message
Generation | Automatic generation of high-quality commit messages for code commits can
substantially facilitate software developers' works and coordination. However,
the semantic gap between source code and natural language poses a major
challenge for the task. Several studies have been proposed to alleviate the
challenge but none explicitly involves code contextual information during
commit message generation. Specifically, existing research adopts static
embedding for code tokens, which maps a token to the same vector regardless of
its context. In this paper, we propose a novel Contextualized code
representation learning strategy for commit message Generation (CoreGen).
CoreGen first learns contextualized code representations which exploit the
contextual information behind code commit sequences. The learned
representations of code commits built upon Transformer are then fine-tuned for
downstream commit message generation. Experiments on the benchmark dataset
demonstrate the superior effectiveness of our model over the baseline models
with at least 28.18% improvement in terms of BLEU-4 score. Furthermore, we also
highlight the future opportunities in training contextualized code
representations on larger code corpus as a solution to low-resource tasks and
adapting the contextualized code representation framework to other code-to-text
generation tasks.
| 2,021 | Computation and Language |
Modeling Voting for System Combination in Machine Translation | System combination is an important technique for combining the hypotheses of
different machine translation systems to improve translation performance.
Although early statistical approaches to system combination have been proven
effective in analyzing the consensus between hypotheses, they suffer from the
error propagation problem due to the use of pipelines. While this problem has
been alleviated by end-to-end training of multi-source sequence-to-sequence
models recently, these neural models do not explicitly analyze the relations
between hypotheses and fail to capture their agreement because the attention to
a word in a hypothesis is calculated independently, ignoring the fact that the
word might occur in multiple hypotheses. In this work, we propose an approach
to modeling voting for system combination in machine translation. The basic
idea is to enable words in hypotheses from different systems to vote on words
that are representative and should get involved in the generation process. This
can be done by quantifying the influence of each voter and its preference for
each candidate. Our approach combines the advantages of statistical and neural
methods since it can not only analyze the relations between hypotheses but also
allow for end-to-end training. Experiments show that our approach is capable of
better taking advantage of the consensus between hypotheses and achieves
significant improvements over state-of-the-art baselines on Chinese-English and
English-German machine translation tasks.
| 2,020 | Computation and Language |
COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions
Attributes | This paper describes a large global dataset on people's discourse and
responses to the COVID-19 pandemic over the Twitter platform. From 28 January
2020 to 1 June 2022, we collected and processed over 252 million Twitter posts
from more than 29 million unique users using four keywords: "corona", "wuhan",
"nCov" and "covid". Leveraging probabilistic topic modelling and pre-trained
machine learning-based emotion recognition algorithms, we labelled each tweet
with seventeen attributes, including a) ten binary attributes indicating the
tweet's relevance (1) or irrelevance (0) to the top ten detected topics, b)
five quantitative emotion attributes indicating the degree of intensity of the
valence or sentiment (from 0: extremely negative to 1: extremely positive) and
the degree of intensity of fear, anger, sadness and happiness emotions (from 0:
not at all to 1: extremely intense), and c) two categorical attributes
indicating the sentiment (very negative, negative, neutral or mixed, positive,
very positive) and the dominant emotion (fear, anger, sadness, happiness, no
specific emotion) the tweet is mainly expressing. We discuss the technical
validity and report the descriptive statistics of these attributes, their
temporal distribution, and geographic representation. The paper concludes with
a discussion of the dataset's usage in communication, psychology, public
health, economics, and epidemiology.
| 2,022 | Computation and Language |
Investigation of Sentiment Controllable Chatbot | Conventional seq2seq chatbot models attempt only to find sentences with the
highest probabilities conditioned on the input sequences, without considering
the sentiment of the output sentences. In this paper, we investigate four
models to scale or adjust the sentiment of the chatbot response: a
persona-based model, reinforcement learning, a plug and play model, and
CycleGAN, all based on the seq2seq model. We also develop machine-evaluated
metrics to estimate whether the responses are reasonable given the input. These
metrics, together with human evaluation, are used to analyze the performance of
the four models in terms of different aspects; reinforcement learning and
CycleGAN are shown to be very attractive.
| 2,020 | Computation and Language |
Using Holographically Compressed Embeddings in Question Answering | Word vector representations are central to deep learning natural language
processing models. Many forms of these vectors, known as embeddings, exist,
including word2vec and GloVe. Embeddings are trained on large corpora and learn
the word's usage in context, capturing the semantic relationship between words.
However, the semantics from such training are at the level of distinct words
(known as word types), and can be ambiguous when, for example, a word type can
be either a noun or a verb. In question answering, parts-of-speech and named
entity types are important, but encoding these attributes in neural models
expands the size of the input. This research employs holographic compression of
pre-trained embeddings, to represent a token, its part-of-speech, and named
entity type, in the same dimension as representing only the token. The
implementation, in a modified question answering recurrent deep learning
network, shows that semantic relationships are preserved, and yields strong
performance.
| 2,020 | Computation and Language |
Deep learning models for representing out-of-vocabulary words | Communication has become increasingly dynamic with the popularization of
social networks and applications that allow people to express themselves and
communicate instantly. In this scenario, distributed representation models have
their quality impacted by new words that appear frequently or that are derived
from spelling errors. These words that are unknown by the models, known as
out-of-vocabulary (OOV) words, need to be properly handled to not degrade the
quality of the natural language processing (NLP) applications, which depend on
the appropriate vector representation of the texts. To better understand this
problem and finding the best techniques to handle OOV words, in this study, we
present a comprehensive performance evaluation of deep learning models for
representing OOV words. We performed an intrinsic evaluation using a benchmark
dataset and an extrinsic evaluation using different NLP tasks: text
categorization, named entity recognition, and part-of-speech tagging. Although
the results indicated that the best technique for handling OOV words is
different for each task, Comick, a deep learning method that infers the
embedding based on the context and the morphological structure of the OOV word,
obtained promising results.
| 2,020 | Computation and Language |
Emoji Prediction: Extensions and Benchmarking | Emojis are a succinct form of language which can express concrete meanings,
emotions, and intentions. Emojis also carry signals that can be used to better
understand communicative intent. They have become a ubiquitous part of our
daily lives, making them an important part of understanding user-generated
content. The emoji prediction task aims at predicting the proper set of emojis
associated with a piece of text. Through emoji prediction, models can learn
rich representations of the communicative intent of the written text. While
existing research on the emoji prediction task focus on a small subset of emoji
types closely related to certain emotions, this setting oversimplifies the task
and wastes the expressive power of emojis. In this paper, we extend the
existing setting of the emoji prediction task to include a richer set of emojis
and to allow multi-label classification on the task. We propose novel models
for multi-class and multi-label emoji prediction based on Transformer networks.
We also construct multiple emoji prediction datasets from Twitter using
heuristics. The BERT models achieve state-of-the-art performances on all our
datasets under all the settings, with relative improvements of 27.21% to
236.36% in accuracy, 2.01% to 88.28% in top-5 accuracy and 65.19% to 346.79% in
F-1 score, compared to the prior state-of-the-art. Our results demonstrate the
efficacy of deep Transformer-based models on the emoji prediction task. We also
release our datasets at
https://github.com/hikari-NYU/Emoji_Prediction_Datasets_MMS for future
researchers.
| 2,020 | Computation and Language |
Modeling Coherency in Generated Emails by Leveraging Deep Neural
Learners | Advanced machine learning and natural language techniques enable attackers to
launch sophisticated and targeted social engineering-based attacks. To counter
the active attacker issue, researchers have since resorted to proactive methods
of detection. Email masquerading using targeted emails to fool the victim is an
advanced attack method. However automatic text generation requires controlling
the context and coherency of the generated content, which has been identified
as an increasingly difficult problem. The method used leverages a hierarchical
deep neural model which uses a learned representation of the sentences in the
input document to generate structured written emails. We demonstrate the
generation of short and targeted text messages using the deep model. The global
coherency of the synthesized text is evaluated using a qualitative study as
well as multiple quantitative measures.
| 2,020 | Computation and Language |
Are We There Yet? Evaluating State-of-the-Art Neural Network based
Geoparsers Using EUPEG as a Benchmarking Platform | Geoparsing is an important task in geographic information retrieval. A
geoparsing system, known as a geoparser, takes some texts as the input and
outputs the recognized place mentions and their location coordinates. In June
2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was
held as one of the SemEval 2019 tasks. The winning teams developed neural
network based geoparsers that achieved outstanding performances (over 90%
precision, recall, and F1 score for toponym recognition). This exciting result
brings the question "are we there yet?", namely have we achieved high enough
performances to possibly consider the problem of geoparsing as solved? One
limitation of this competition is that the developed geoparsers were tested on
only one dataset which has 45 research articles collected from the particular
domain of Bio-medicine. It is known that the same geoparser can have very
different performances on different datasets. Thus, this work performs a
systematic evaluation of these state-of-the-art geoparsers using our recently
developed benchmarking platform EUPEG that has eight annotated datasets, nine
baseline geoparsers, and eight performance metrics. The evaluation result
suggests that these new geoparsers indeed improve the performances of
geoparsing on multiple datasets although some challenges remain.
| 2,020 | Computation and Language |
Predicting Clinical Diagnosis from Patients Electronic Health Records
Using BERT-based Neural Networks | In this paper we study the problem of predicting clinical diagnoses from
textual Electronic Health Records (EHR) data. We show the importance of this
problem in medical community and present comprehensive historical review of the
problem and proposed methods. As the main scientific contributions we present a
modification of Bidirectional Encoder Representations from Transformers (BERT)
model for sequence classification that implements a novel way of
Fully-Connected (FC) layer composition and a BERT model pretrained only on
domain data. To empirically validate our model, we use a large-scale Russian
EHR dataset consisting of about 4 million unique patient visits. This is the
largest such study for the Russian language and one of the largest globally. We
performed a number of comparative experiments with other text representation
models on the task of multiclass classification for 265 disease subset of
ICD-10. The experiments demonstrate improved performance of our models compared
to other baselines, including a fine-tuned Russian BERT (RuBERT) variant. We
also show comparable performance of our model with a panel of experienced
medical experts. This allows us to hope that implementation of this system will
reduce misdiagnosis.
| 2,020 | Computation and Language |
Logic Constrained Pointer Networks for Interpretable Textual Similarity | Systematically discovering semantic relationships in text is an important and
extensively studied area in Natural Language Processing, with various tasks
such as entailment, semantic similarity, etc. Decomposability of sentence-level
scores via subsequence alignments has been proposed as a way to make models
more interpretable. We study the problem of aligning components of sentences
leading to an interpretable model for semantic textual similarity. In this
paper, we introduce a novel pointer network based model with a sentinel gating
function to align constituent chunks, which are represented using BERT. We
improve this base model with a loss function to equally penalize misalignments
in both sentences, ensuring the alignments are bidirectional. Finally, to guide
the network with structured external knowledge, we introduce first-order logic
constraints based on ConceptNet and syntactic knowledge. The model achieves an
F1 score of 97.73 and 96.32 on the benchmark SemEval datasets for the chunk
alignment task, showing large improvements over the existing solutions. Source
code is available at
https://github.com/manishb89/interpretable_sentence_similarity
| 2,020 | Computation and Language |
UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual
Named Entity Recognition with Unlabeled Data | Prior works in cross-lingual named entity recognition (NER) with no/little
labeled data fall into two primary categories: model transfer based and data
transfer based methods. In this paper we find that both method types can
complement each other, in the sense that, the former can exploit context
information via language-independent features but sees no task-specific
information in the target language; while the latter generally generates pseudo
target-language training data via translation but its exploitation of context
information is weakened by inaccurate translations. Moreover, prior works
rarely leverage unlabeled data in the target language, which can be
effortlessly collected and potentially contains valuable information for
improved results. To handle both problems, we propose a novel approach termed
UniTrans to Unify both model and data Transfer for cross-lingual NER, and
furthermore, to leverage the available information from unlabeled
target-language data via enhanced knowledge distillation. We evaluate our
proposed UniTrans over 4 target languages on benchmark datasets. Our
experimental results show that it substantially outperforms the existing
state-of-the-art methods.
| 2,020 | Computation and Language |
A Multilingual Parallel Corpora Collection Effort for Indian Languages | We present sentence aligned parallel corpora across 10 Indian Languages -
Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi,
Punjabi, and English - many of which are categorized as low resource. The
corpora are compiled from online sources which have content shared across
languages. The corpora presented significantly extends present resources that
are either not large enough or are restricted to a specific domain (such as
health). We also provide a separate test corpus compiled from an independent
online source that can be independently used for validating the performance in
10 Indian languages. Alongside, we report on the methods of constructing such
corpora using tools enabled by recent advances in machine translation and
cross-lingual retrieval using deep neural network based methods.
| 2,020 | Computation and Language |
Dual Past and Future for Neural Machine Translation | Though remarkable successes have been achieved by Neural Machine Translation
(NMT) in recent years, it still suffers from the inadequate-translation
problem. Previous studies show that explicitly modeling the Past and Future
contents of the source sentence is beneficial for translation performance.
However, it is not clear whether the commonly used heuristic objective is good
enough to guide the Past and Future. In this paper, we present a novel dual
framework that leverages both source-to-target and target-to-source NMT models
to provide a more direct and accurate supervision signal for the Past and
Future modules. Experimental results demonstrate that our proposed method
significantly improves the adequacy of NMT predictions and surpasses previous
methods in two well-studied translation tasks.
| 2,020 | Computation and Language |
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