Titles
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Predict and Use Latent Patterns for Short-Text Conversation
|
Many neural network models nowadays have achieved promising performances in
Chit-chat settings. The majority of them rely on an encoder for understanding
the post and a decoder for generating the response. Without given assigned
semantics, the models lack the fine-grained control over responses as the
semantic mapping between posts and responses is hidden on the fly within the
end-to-end manners. Some previous works utilize sampled latent words as a
controllable semantic form to drive the generated response around the work, but
few works attempt to use more complex semantic patterns to guide the
generation. In this paper, we propose to use more detailed semantic forms,
including latent responses and part-of-speech sequences sampled from the
corresponding distributions, as the controllable semantics to guide the
generation. Our results show that the richer semantics are not only able to
provide informative and diverse responses, but also increase the overall
performance of response quality, including fluency and coherence.
| 2,020 |
Computation and Language
|
Interpretation of NLP models through input marginalization
|
To demystify the "black box" property of deep neural networks for natural
language processing (NLP), several methods have been proposed to interpret
their predictions by measuring the change in prediction probability after
erasing each token of an input. Since existing methods replace each token with
a predefined value (i.e., zero), the resulting sentence lies out of the
training data distribution, yielding misleading interpretations. In this study,
we raise the out-of-distribution problem induced by the existing interpretation
methods and present a remedy; we propose to marginalize each token out. We
interpret various NLP models trained for sentiment analysis and natural
language inference using the proposed method.
| 2,020 |
Computation and Language
|
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for
Self-supervised Speech Representation Learning
|
Self-supervised visual pretraining has shown significant progress recently.
Among those methods, SimCLR greatly advanced the state of the art in
self-supervised and semi-supervised learning on ImageNet. The input feature
representations for speech and visual tasks are both continuous, so it is
natural to consider applying similar objective on speech representation
learning. In this paper, we propose Speech SimCLR, a new self-supervised
objective for speech representation learning. During training, Speech SimCLR
applies augmentation on raw speech and its spectrogram. Its objective is the
combination of contrastive loss that maximizes agreement between differently
augmented samples in the latent space and reconstruction loss of input
representation. The proposed method achieved competitive results on speech
emotion recognition and speech recognition.
| 2,021 |
Computation and Language
|
Volctrans Parallel Corpus Filtering System for WMT 2020
|
In this paper, we describe our submissions to the WMT20 shared task on
parallel corpus filtering and alignment for low-resource conditions. The task
requires the participants to align potential parallel sentence pairs out of the
given document pairs, and score them so that low-quality pairs can be filtered.
Our system, Volctrans, is made of two modules, i.e., a mining module and a
scoring module. Based on the word alignment model, the mining module adopts an
iterative mining strategy to extract latent parallel sentences. In the scoring
module, an XLM-based scorer provides scores, followed by reranking mechanisms
and ensemble. Our submissions outperform the baseline by 3.x/2.x and 2.x/2.x
for km-en and ps-en on From Scratch/Fine-Tune conditions, which is the highest
among all submissions.
| 2,020 |
Computation and Language
|
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic
Semi-Supervised Approaches for Sequence Tagging
|
Leveraging large amounts of unlabeled data using Transformer-like
architectures, like BERT, has gained popularity in recent times owing to their
effectiveness in learning general representations that can then be further
fine-tuned for downstream tasks to much success. However, training these models
can be costly both from an economic and environmental standpoint. In this work,
we investigate how to effectively use unlabeled data: by exploring the
task-specific semi-supervised approach, Cross-View Training (CVT) and comparing
it with task-agnostic BERT in multiple settings that include domain and task
relevant English data. CVT uses a much lighter model architecture and we show
that it achieves similar performance to BERT on a set of sequence tagging
tasks, with lesser financial and environmental impact.
| 2,020 |
Computation and Language
|
Jointly Optimizing State Operation Prediction and Value Generation for
Dialogue State Tracking
|
We investigate the problem of multi-domain Dialogue State Tracking (DST) with
open vocabulary. Existing approaches exploit BERT encoder and copy-based RNN
decoder, where the encoder predicts the state operation, and the decoder
generates new slot values. However, in such a stacked encoder-decoder
structure, the operation prediction objective only affects the BERT encoder and
the value generation objective mainly affects the RNN decoder. In this paper,
we propose a purely Transformer-based framework, where a single BERT works as
both the encoder and the decoder. In so doing, the operation prediction
objective and the value generation objective can jointly optimize this BERT for
DST. At the decoding step, we re-use the hidden states of the encoder in the
self-attention mechanism of the corresponding decoder layers to construct a
flat encoder-decoder architecture for effective parameter updating.
Experimental results show that our approach substantially outperforms the
existing state-of-the-art framework, and it also achieves very competitive
performance to the best ontology-based approaches.
| 2,021 |
Computation and Language
|
Emotion recognition by fusing time synchronous and time asynchronous
representations
|
In this paper, a novel two-branch neural network model structure is proposed
for multimodal emotion recognition, which consists of a time synchronous branch
(TSB) and a time asynchronous branch (TAB). To capture correlations between
each word and its acoustic realisation, the TSB combines speech and text
modalities at each input window frame and then does pooling across time to form
a single embedding vector. The TAB, by contrast, provides cross-utterance
information by integrating sentence text embeddings from a number of context
utterances into another embedding vector. The final emotion classification uses
both the TSB and the TAB embeddings. Experimental results on the IEMOCAP
dataset demonstrate that the two-branch structure achieves state-of-the-art
results in 4-way classification with all common test setups. When using
automatic speech recognition (ASR) output instead of manually transcribed
reference text, it is shown that the cross-utterance information considerably
improves the robustness against ASR errors. Furthermore, by incorporating an
extra class for all the other emotions, the final 5-way classification system
with ASR hypotheses can be viewed as a prototype for more realistic emotion
recognition systems.
| 2,021 |
Computation and Language
|
Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph
Convolution Neural Networks
|
Recent studies on event detection (ED) haveshown that the syntactic
dependency graph canbe employed in graph convolution neural net-works (GCN) to
achieve state-of-the-art per-formance. However, the computation of thehidden
vectors in such graph-based models isagnostic to the trigger candidate words,
po-tentially leaving irrelevant information for thetrigger candidate for event
prediction. In addi-tion, the current models for ED fail to exploitthe overall
contextual importance scores of thewords, which can be obtained via the
depen-dency tree, to boost the performance. In thisstudy, we propose a novel
gating mechanismto filter noisy information in the hidden vec-tors of the GCN
models for ED based on theinformation from the trigger candidate. Wealso
introduce novel mechanisms to achievethe contextual diversity for the gates and
theimportance score consistency for the graphsand models in ED. The experiments
show thatthe proposed model achieves state-of-the-artperformance on two ED
datasets
| 2,020 |
Computation and Language
|
Global Sentiment Analysis Of COVID-19 Tweets Over Time
|
The Coronavirus pandemic has affected the normal course of life. People
around the world have taken to social media to express their opinions and
general emotions regarding this phenomenon that has taken over the world by
storm. The social networking site, Twitter showed an unprecedented increase in
tweets related to the novel Coronavirus in a very short span of time. This
paper presents the global sentiment analysis of tweets related to Coronavirus
and how the sentiment of people in different countries has changed over time.
Furthermore, to determine the impact of Coronavirus on daily aspects of life,
tweets related to Work From Home (WFH) and Online Learning were scraped and the
change in sentiment over time was observed. In addition, various Machine
Learning models such as Long Short Term Memory (LSTM) and Artificial Neural
Networks (ANN) were implemented for sentiment classification and their
accuracies were determined. Exploratory data analysis was also performed for a
dataset providing information about the number of confirmed cases on a per-day
basis in a few of the worst-hit countries to provide a comparison between the
change in sentiment with the change in cases since the start of this pandemic
till June 2020.
| 2,020 |
Computation and Language
|
Multi-XScience: A Large-scale Dataset for Extreme Multi-document
Summarization of Scientific Articles
|
Multi-document summarization is a challenging task for which there exists
little large-scale datasets. We propose Multi-XScience, a large-scale
multi-document summarization dataset created from scientific articles.
Multi-XScience introduces a challenging multi-document summarization task:
writing the related-work section of a paper based on its abstract and the
articles it references. Our work is inspired by extreme summarization, a
dataset construction protocol that favours abstractive modeling approaches.
Descriptive statistics and empirical results---using several state-of-the-art
models trained on the Multi-XScience dataset---reveal that Multi-XScience is
well suited for abstractive models.
| 2,020 |
Computation and Language
|
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning
and Hierarchical Relational Searching
|
Distant supervision (DS) aims to generate large-scale heuristic labeling
corpus, which is widely used for neural relation extraction currently. However,
it heavily suffers from noisy labeling and long-tail distributions problem.
Many advanced approaches usually separately address two problems, which ignore
their mutual interactions. In this paper, we propose a novel framework named
RH-Net, which utilizes Reinforcement learning and Hierarchical relational
searching module to improve relation extraction. We leverage reinforcement
learning to instruct the model to select high-quality instances. We then
propose the hierarchical relational searching module to share the semantics
from correlative instances between data-rich and data-poor classes. During the
iterative process, the two modules keep interacting to alleviate the noisy and
long-tail problem simultaneously. Extensive experiments on widely used NYT data
set clearly show that our method significant improvements over state-of-the-art
baselines.
| 2,021 |
Computation and Language
|
Cross-lingual Machine Reading Comprehension with Language Branch
Knowledge Distillation
|
Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging
problem due to the lack of large-scale annotated datasets in low-source
languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches use
translation data by translating from a rich-source language, such as English,
to low-source languages as auxiliary supervision. However, how to effectively
leverage translation data and reduce the impact of noise introduced by
translation remains onerous. In this paper, we tackle this challenge and
enhance the cross-lingual transferring performance by a novel augmentation
approach named Language Branch Machine Reading Comprehension (LBMRC). A
language branch is a group of passages in one single language paired with
questions in all target languages. We train multiple machine reading
comprehension (MRC) models proficient in individual language based on LBMRC.
Then, we devise a multilingual distillation approach to amalgamate knowledge
from multiple language branch models to a single model for all target
languages. Combining the LBMRC and multilingual distillation can be more robust
to the data noises, therefore, improving the model's cross-lingual ability.
Meanwhile, the produced single multilingual model is applicable to all target
languages, which saves the cost of training, inference, and maintenance for
multiple models. Extensive experiments on two CLMRC benchmarks clearly show the
effectiveness of our proposed method.
| 2,020 |
Computation and Language
|
Multitask Training with Text Data for End-to-End Speech Recognition
|
We propose a multitask training method for attention-based end-to-end speech
recognition models. We regularize the decoder in a listen, attend, and spell
model by multitask training it on both audio-text and text-only data. Trained
on the 100-hour subset of LibriSpeech, the proposed method, without requiring
an additional language model, leads to an 11% relative performance improvement
over the baseline and approaches the performance of language model shallow
fusion on the test-clean evaluation set. We observe a similar trend on the
whole 960-hour LibriSpeech training set. Analyses of different types of errors
and sample output sentences demonstrate that the proposed method can
incorporate language level information, suggesting its effectiveness in
real-world applications.
| 2,021 |
Computation and Language
|
Listener's Social Identity Matters in Personalised Response Generation
|
Personalised response generation enables generating human-like responses by
means of assigning the generator a social identity. However, pragmatics theory
suggests that human beings adjust the way of speaking based on not only who
they are but also whom they are talking to. In other words, when modelling
personalised dialogues, it might be favourable if we also take the listener's
social identity into consideration. To validate this idea, we use gender as a
typical example of a social variable to investigate how the listener's identity
influences the language used in Chinese dialogues on social media. Also, we
build personalised generators. The experiment results demonstrate that the
listener's identity indeed matters in the language use of responses and that
the response generator can capture such differences in language use. More
interestingly, by additionally modelling the listener's identity, the
personalised response generator performs better in its own identity.
| 2,020 |
Computation and Language
|
Differentiable Open-Ended Commonsense Reasoning
|
Current commonsense reasoning research focuses on developing models that use
commonsense knowledge to answer multiple-choice questions. However, systems
designed to answer multiple-choice questions may not be useful in applications
that do not provide a small list of candidate answers to choose from. As a step
towards making commonsense reasoning research more realistic, we propose to
study open-ended commonsense reasoning (OpenCSR) -- the task of answering a
commonsense question without any pre-defined choices -- using as a resource
only a corpus of commonsense facts written in natural language. OpenCSR is
challenging due to a large decision space, and because many questions require
implicit multi-hop reasoning. As an approach to OpenCSR, we propose DrFact, an
efficient Differentiable model for multi-hop Reasoning over knowledge Facts. To
evaluate OpenCSR methods, we adapt several popular commonsense reasoning
benchmarks, and collect multiple new answers for each test question via
crowd-sourcing. Experiments show that DrFact outperforms strong baseline
methods by a large margin.
| 2,021 |
Computation and Language
|
Discovering and Interpreting Biased Concepts in Online Communities
|
Language carries implicit human biases, functioning both as a reflection and
a perpetuation of stereotypes that people carry with them. Recently, ML-based
NLP methods such as word embeddings have been shown to learn such language
biases with striking accuracy. This capability of word embeddings has been
successfully exploited as a tool to quantify and study human biases. However,
previous studies only consider a predefined set of biased concepts to attest
(e.g., whether gender is more or less associated with particular jobs), or just
discover biased words without helping to understand their meaning at the
conceptual level. As such, these approaches can be either unable to find biased
concepts that have not been defined in advance, or the biases they find are
difficult to interpret and study. This could make existing approaches
unsuitable to discover and interpret biases in online communities, as such
communities may carry different biases than those in mainstream culture. This
paper improves upon, extends, and evaluates our previous data-driven method to
automatically discover and help interpret biased concepts encoded in word
embeddings. We apply this approach to study the biased concepts present in the
language used in online communities and experimentally show the validity and
stability of our method
| 2,022 |
Computation and Language
|
Evaluating Gender Bias in Speech Translation
|
The scientific community is increasingly aware of the necessity to embrace
pluralism and consistently represent major and minor social groups. Currently,
there are no standard evaluation techniques for different types of biases.
Accordingly, there is an urgent need to provide evaluation sets and protocols
to measure existing biases in our automatic systems. Evaluating the biases
should be an essential step towards mitigating them in the systems.
This paper introduces WinoST, a new freely available challenge set for
evaluating gender bias in speech translation. WinoST is the speech version of
WinoMT which is a MT challenge set and both follow an evaluation protocol to
measure gender accuracy. Using a state-of-the-art end-to-end speech translation
system, we report the gender bias evaluation on four language pairs and we show
that gender accuracy in speech translation is more than 23% lower than in MT.
| 2,022 |
Computation and Language
|
It's All in the Name: A Character Based Approach To Infer Religion
|
Demographic inference from text has received a surge of attention in the
field of natural language processing in the last decade. In this paper, we use
personal names to infer religion in South Asia - where religion is a salient
social division, and yet, disaggregated data on it remains scarce. Existing
work predicts religion using dictionary based method, and therefore, can not
classify unseen names. We use character based models which learn character
patterns and, therefore, can classify unseen names as well with high accuracy.
These models are also much faster and can easily be scaled to large data sets.
We improve our classifier by combining the name of an individual with that of
their parent/spouse and achieve remarkably high accuracy. Finally, we trace the
classification decisions of a convolutional neural network model using
layer-wise relevance propagation which can explain the predictions of complex
non-linear classifiers and circumvent their purported black box nature. We show
how character patterns learned by the classifier are rooted in the linguistic
origins of names.
| 2,020 |
Computation and Language
|
Fast Interleaved Bidirectional Sequence Generation
|
Independence assumptions during sequence generation can speed up inference,
but parallel generation of highly inter-dependent tokens comes at a cost in
quality. Instead of assuming independence between neighbouring tokens
(semi-autoregressive decoding, SA), we take inspiration from bidirectional
sequence generation and introduce a decoder that generates target words from
the left-to-right and right-to-left directions simultaneously. We show that we
can easily convert a standard architecture for unidirectional decoding into a
bidirectional decoder by simply interleaving the two directions and adapting
the word positions and self-attention masks. Our interleaved bidirectional
decoder (IBDecoder) retains the model simplicity and training efficiency of the
standard Transformer, and on five machine translation tasks and two document
summarization tasks, achieves a decoding speedup of ~2X compared to
autoregressive decoding with comparable quality. Notably, it outperforms
left-to-right SA because the independence assumptions in IBDecoder are more
felicitous. To achieve even higher speedups, we explore hybrid models where we
either simultaneously predict multiple neighbouring tokens per direction, or
perform multi-directional decoding by partitioning the target sequence. These
methods achieve speedups to 4X-11X across different tasks at the cost of <1
BLEU or <0.5 ROUGE (on average). Source code is released at
https://github.com/bzhangGo/zero.
| 2,020 |
Computation and Language
|
Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender
Bias
|
Contextualized word embeddings have been replacing standard embeddings as the
representational knowledge source of choice in NLP systems. Since a variety of
biases have previously been found in standard word embeddings, it is crucial to
assess biases encoded in their replacements as well. Focusing on BERT (Devlin
et al., 2018), we measure gender bias by studying associations between
gender-denoting target words and names of professions in English and German,
comparing the findings with real-world workforce statistics. We mitigate bias
by fine-tuning BERT on the GAP corpus (Webster et al., 2018), after applying
Counterfactual Data Substitution (CDS) (Maudslay et al., 2019). We show that
our method of measuring bias is appropriate for languages such as English, but
not for languages with a rich morphology and gender-marking, such as German.
Our results highlight the importance of investigating bias and mitigation
techniques cross-linguistically, especially in view of the current emphasis on
large-scale, multilingual language models.
| 2,020 |
Computation and Language
|
DGST: a Dual-Generator Network for Text Style Transfer
|
We propose DGST, a novel and simple Dual-Generator network architecture for
text Style Transfer. Our model employs two generators only, and does not rely
on any discriminators or parallel corpus for training. Both quantitative and
qualitative experiments on the Yelp and IMDb datasets show that our model gives
competitive performance compared to several strong baselines with more
complicated architecture designs.
| 2,020 |
Computation and Language
|
Strongly Incremental Constituency Parsing with Graph Neural Networks
|
Parsing sentences into syntax trees can benefit downstream applications in
NLP. Transition-based parsers build trees by executing actions in a state
transition system. They are computationally efficient, and can leverage machine
learning to predict actions based on partial trees. However, existing
transition-based parsers are predominantly based on the shift-reduce transition
system, which does not align with how humans are known to parse sentences.
Psycholinguistic research suggests that human parsing is strongly incremental:
humans grow a single parse tree by adding exactly one token at each step. In
this paper, we propose a novel transition system called attach-juxtapose. It is
strongly incremental; it represents a partial sentence using a single tree;
each action adds exactly one token into the partial tree. Based on our
transition system, we develop a strongly incremental parser. At each step, it
encodes the partial tree using a graph neural network and predicts an action.
We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On
PTB, it outperforms existing parsers trained with only constituency trees; and
it performs on par with state-of-the-art parsers that use dependency trees as
additional training data. On CTB, our parser establishes a new state of the
art. Code is available at
https://github.com/princeton-vl/attach-juxtapose-parser.
| 2,020 |
Computation and Language
|
Language ID in the Wild: Unexpected Challenges on the Path to a
Thousand-Language Web Text Corpus
|
Large text corpora are increasingly important for a wide variety of Natural
Language Processing (NLP) tasks, and automatic language identification (LangID)
is a core technology needed to collect such datasets in a multilingual context.
LangID is largely treated as solved in the literature, with models reported
that achieve over 90% average F1 on as many as 1,366 languages. We train LangID
models on up to 1,629 languages with comparable quality on held-out test sets,
but find that human-judged LangID accuracy for web-crawl text corpora created
using these models is only around 5% for many lower-resource languages,
suggesting a need for more robust evaluation. Further analysis revealed a
variety of error modes, arising from domain mismatch, class imbalance, language
similarity, and insufficiently expressive models. We propose two classes of
techniques to mitigate these errors: wordlist-based tunable-precision filters
(for which we release curated lists in about 500 languages) and
transformer-based semi-supervised LangID models, which increase median dataset
precision from 5.5% to 71.2%. These techniques enable us to create an initial
data set covering 100K or more relatively clean sentences in each of 500+
languages, paving the way towards a 1,000-language web text corpus.
| 2,020 |
Computation and Language
|
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet
Lab Protocols
|
This paper presents the results of the wet lab information extraction task at
WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition
(NER) task with 13 participants and (2) a Relation Extraction (RE) task with 2
participants. We outline the task, data annotation process, corpus statistics,
and provide a high-level overview of the participating systems for each sub
task.
| 2,020 |
Computation and Language
|
Predicting Themes within Complex Unstructured Texts: A Case Study on
Safeguarding Reports
|
The task of text and sentence classification is associated with the need for
large amounts of labelled training data. The acquisition of high volumes of
labelled datasets can be expensive or unfeasible, especially for
highly-specialised domains for which documents are hard to obtain. Research on
the application of supervised classification based on small amounts of training
data is limited. In this paper, we address the combination of state-of-the-art
deep learning and classification methods and provide an insight into what
combination of methods fit the needs of small, domain-specific, and
terminologically-rich corpora. We focus on a real-world scenario related to a
collection of safeguarding reports comprising learning experiences and
reflections on tackling serious incidents involving children and vulnerable
adults. The relatively small volume of available reports and their use of
highly domain-specific terminology makes the application of automated
approaches difficult. We focus on the problem of automatically identifying the
main themes in a safeguarding report using supervised classification
approaches. Our results show the potential of deep learning models to simulate
subject-expert behaviour even for complex tasks with limited labelled data.
| 2,021 |
Computation and Language
|
On the diminishing return of labeling clinical reports
|
Ample evidence suggests that better machine learning models may be steadily
obtained by training on increasingly larger datasets on natural language
processing (NLP) problems from non-medical domains. Whether the same holds true
for medical NLP has by far not been thoroughly investigated. This work shows
that this is indeed not always the case. We reveal the somehow
counter-intuitive observation that performant medical NLP models may be
obtained with small amount of labeled data, quite the opposite to the common
belief, most likely due to the domain specificity of the problem. We show
quantitatively the effect of training data size on a fixed test set composed of
two of the largest public chest x-ray radiology report datasets on the task of
abnormality classification. The trained models not only make use of the
training data efficiently, but also outperform the current state-of-the-art
rule-based systems by a significant margin.
| 2,020 |
Computation and Language
|
Learning Contextualised Cross-lingual Word Embeddings and Alignments for
Extremely Low-Resource Languages Using Parallel Corpora
|
We propose a new approach for learning contextualised cross-lingual word
embeddings based on a small parallel corpus (e.g. a few hundred sentence
pairs). Our method obtains word embeddings via an LSTM encoder-decoder model
that simultaneously translates and reconstructs an input sentence. Through
sharing model parameters among different languages, our model jointly trains
the word embeddings in a common cross-lingual space. We also propose to combine
word and subword embeddings to make use of orthographic similarities across
different languages. We base our experiments on real-world data from endangered
languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on
bilingual lexicon induction and word alignment tasks show that our model
outperforms existing methods by a large margin for most language pairs. These
results demonstrate that, contrary to common belief, an encoder-decoder
translation model is beneficial for learning cross-lingual representations even
in extremely low-resource conditions. Furthermore, our model also works well on
high-resource conditions, achieving state-of-the-art performance on a
German-English word-alignment task.
| 2,021 |
Computation and Language
|
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
|
In this work, we present a dual learning approach for unsupervised text to
path and path to text transfers in Commonsense Knowledge Bases (KBs). We
investigate the impact of weak supervision by creating a weakly supervised
dataset and show that even a slight amount of supervision can significantly
improve the model performance and enable better-quality transfers. We examine
different model architectures, and evaluation metrics, proposing a novel
Commonsense KB completion metric tailored for generative models. Extensive
experimental results show that the proposed method compares very favorably to
the existing baselines. This approach is a viable step towards a more advanced
system for automatic KB construction/expansion and the reverse operation of KB
conversion to coherent textual descriptions.
| 2,020 |
Computation and Language
|
Transformer in action: a comparative study of transformer-based acoustic
models for large scale speech recognition applications
|
In this paper, we summarize the application of transformer and its streamable
variant, Emformer based acoustic model for large scale speech recognition
applications. We compare the transformer based acoustic models with their LSTM
counterparts on industrial scale tasks. Specifically, we compare Emformer with
latency-controlled BLSTM (LCBLSTM) on medium latency tasks and LSTM on low
latency tasks. On a low latency voice assistant task, Emformer gets 24% to 26%
relative word error rate reductions (WERRs). For medium latency scenarios,
comparing with LCBLSTM with similar model size and latency, Emformer gets
significant WERR across four languages in video captioning datasets with 2-3
times inference real-time factors reduction.
| 2,020 |
Computation and Language
|
What Does This Acronym Mean? Introducing a New Dataset for Acronym
Identification and Disambiguation
|
Acronyms are the short forms of phrases that facilitate conveying lengthy
sentences in documents and serve as one of the mainstays of writing. Due to
their importance, identifying acronyms and corresponding phrases (i.e., acronym
identification (AI)) and finding the correct meaning of each acronym (i.e.,
acronym disambiguation (AD)) are crucial for text understanding. Despite the
recent progress on this task, there are some limitations in the existing
datasets which hinder further improvement. More specifically, limited size of
manually annotated AI datasets or noises in the automatically created acronym
identification datasets obstruct designing advanced high-performing acronym
identification models. Moreover, the existing datasets are mostly limited to
the medical domain and ignore other domains. In order to address these two
limitations, we first create a manually annotated large AI dataset for
scientific domain. This dataset contains 17,506 sentences which is
substantially larger than previous scientific AI datasets. Next, we prepare an
AD dataset for scientific domain with 62,441 samples which is significantly
larger than the previous scientific AD dataset. Our experiments show that the
existing state-of-the-art models fall far behind human-level performance on
both datasets proposed by this work. In addition, we propose a new deep
learning model that utilizes the syntactical structure of the sentence to
expand an ambiguous acronym in a sentence. The proposed model outperforms the
state-of-the-art models on the new AD dataset, providing a strong baseline for
future research on this dataset.
| 2,020 |
Computation and Language
|
Character Entropy in Modern and Historical Texts: Comparison Metrics for
an Undeciphered Manuscript
|
This paper outlines the creation of three corpora for multilingual comparison
and analysis of the Voynich manuscript: a corpus of Voynich texts partitioned
by Currier language, scribal hand, and transcription system, a corpus of 294
language samples compiled from Wikipedia, and a corpus of eighteen transcribed
historical texts in eight languages. These corpora will be utilized in
subsequent work by the Voynich Working Group at Yale University.
We demonstrate the utility of these corpora for studying characteristics of
the Voynich script and language, with an analysis of conditional character
entropy in Voynichese. We discuss the interaction between character entropy and
language, script size and type, glyph compositionality, scribal conventions and
abbreviations, positional character variants, and bigram frequency.
This analysis characterizes the interaction between script compositionality,
character size, and predictability. We show that substantial manipulations of
glyph composition are not sufficient to align conditional entropy levels with
natural languages. The unusually predictable nature of the Voynichese script is
not attributable to a particular script or transcription system, underlying
language, or substitution cipher. Voynichese is distinct from every comparison
text in our corpora because character placement is highly constrained within
the word, and this may indicate the loss of phonemic distinctions from the
underlying language.
| 2,021 |
Computation and Language
|
TopicModel4J: A Java Package for Topic Models
|
Topic models provide a flexible and principled framework for exploring hidden
structure in high-dimensional co-occurrence data and are commonly used natural
language processing (NLP) of text. In this paper, we design and implement a
Java package, TopicModel4J, which contains 13 kinds of representative
algorithms for fitting topic models. The TopicModel4J in the Java programming
environment provides an easy-to-use interface for data analysts to run the
algorithms, and allow to easily input and output data. In addition, this
package provides a few unstructured text preprocessing techniques, such as
splitting textual data into words, lowercasing the words, preforming
lemmatization and removing the useless characters, URLs and stop words.
| 2,020 |
Computation and Language
|
Second-Order Unsupervised Neural Dependency Parsing
|
Most of the unsupervised dependency parsers are based on first-order
probabilistic generative models that only consider local parent-child
information. Inspired by second-order supervised dependency parsing, we
proposed a second-order extension of unsupervised neural dependency models that
incorporate grandparent-child or sibling information. We also propose a novel
design of the neural parameterization and optimization methods of the
dependency models. In second-order models, the number of grammar rules grows
cubically with the increase of vocabulary size, making it difficult to train
lexicalized models that may contain thousands of words. To circumvent this
problem while still benefiting from both second-order parsing and
lexicalization, we use the agreement-based learning framework to jointly train
a second-order unlexicalized model and a first-order lexicalized model.
Experiments on multiple datasets show the effectiveness of our second-order
models compared with recent state-of-the-art methods. Our joint model achieves
a 10% improvement over the previous state-of-the-art parser on the full WSJ
test set
| 2,020 |
Computation and Language
|
DisenE: Disentangling Knowledge Graph Embeddings
|
Knowledge graph embedding (KGE), aiming to embed entities and relations into
low-dimensional vectors, has attracted wide attention recently. However, the
existing research is mainly based on the black-box neural models, which makes
it difficult to interpret the learned representation. In this paper, we
introduce DisenE, an end-to-end framework to learn disentangled knowledge graph
embeddings. Specially, we introduce an attention-based mechanism that enables
the model to explicitly focus on relevant components of entity embeddings
according to a given relation. Furthermore, we introduce two novel regularizers
to encourage each component of the entity representation to independently
reflect an isolated semantic aspect. Experimental results demonstrate that our
proposed DisenE investigates a perspective to address the interpretability of
KGE and is proved to be an effective way to improve the performance of link
prediction tasks.
| 2,020 |
Computation and Language
|
Fine-grained Information Status Classification Using Discourse
Context-Aware BERT
|
Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the
problem as a subtask of learning fine-grained information status (IS). However,
these systems heavily depend on many hand-crafted linguistic features. In this
paper, we propose a simple discourse context-aware BERT model for fine-grained
IS classification. On the ISNotes corpus (Markert et al., 2012), our model
achieves new state-of-the-art performance on fine-grained IS classification,
obtaining a 4.8 absolute overall accuracy improvement compared to Hou et al.
(2013a). More importantly, we also show an improvement of 10.5 F1 points for
bridging anaphora recognition without using any complex hand-crafted semantic
features designed for capturing the bridging phenomenon. We further analyze the
trained model and find that the most attended signals for each IS category
correspond well to linguistic notions of information status.
| 2,020 |
Computation and Language
|
A Chinese Text Classification Method With Low Hardware Requirement Based
on Improved Model Concatenation
|
In order to improve the accuracy performance of Chinese text classification
models with low hardware requirements, an improved concatenation-based model is
designed in this paper, which is a concatenation of 5 different sub-models,
including TextCNN, LSTM, and Bi-LSTM. Compared with the existing ensemble
learning method, for a text classification mission, this model's accuracy is 2%
higher. Meanwhile, the hardware requirements of this model are much lower than
the BERT-based model.
| 2,021 |
Computation and Language
|
The Volctrans Machine Translation System for WMT20
|
This paper describes our VolcTrans system on WMT20 shared news translation
task. We participated in 8 translation directions. Our basic systems are based
on Transformer, with several variants (wider or deeper Transformers, dynamic
convolutions). The final system includes text pre-process, data selection,
synthetic data generation, advanced model ensemble, and multilingual
pre-training.
| 2,020 |
Computation and Language
|
Bayesian Methods for Semi-supervised Text Annotation
|
Human annotations are an important source of information in the development
of natural language understanding approaches. As under the pressure of
productivity annotators can assign different labels to a given text, the
quality of produced annotations frequently varies. This is especially the case
if decisions are difficult, with high cognitive load, requires awareness of
broader context, or careful consideration of background knowledge. To alleviate
the problem, we propose two semi-supervised methods to guide the annotation
process: a Bayesian deep learning model and a Bayesian ensemble method. Using a
Bayesian deep learning method, we can discover annotations that cannot be
trusted and might require reannotation. A recently proposed Bayesian ensemble
method helps us to combine the annotators' labels with predictions of trained
models. According to the results obtained from three hate speech detection
experiments, the proposed Bayesian methods can improve the annotations and
prediction performance of BERT models.
| 2,020 |
Computation and Language
|
Bridging the Modality Gap for Speech-to-Text Translation
|
End-to-end speech translation aims to translate speech in one language into
text in another language via an end-to-end way. Most existing methods employ an
encoder-decoder structure with a single encoder to learn acoustic
representation and semantic information simultaneously, which ignores the
speech-and-text modality differences and makes the encoder overloaded, leading
to great difficulty in learning such a model. To address these issues, we
propose a Speech-to-Text Adaptation for Speech Translation (STAST) model which
aims to improve the end-to-end model performance by bridging the modality gap
between speech and text. Specifically, we decouple the speech translation
encoder into three parts and introduce a shrink mechanism to match the length
of speech representation with that of the corresponding text transcription. To
obtain better semantic representation, we completely integrate a text-based
translation model into the STAST so that two tasks can be trained in the same
latent space. Furthermore, we introduce a cross-modal adaptation method to
close the distance between speech and text representation. Experimental results
on English-French and English-German speech translation corpora have shown that
our model significantly outperforms strong baselines, and achieves the new
state-of-the-art performance.
| 2,020 |
Computation and Language
|
Towards Ethics by Design in Online Abusive Content Detection
|
To support safety and inclusion in online communications, significant efforts
in NLP research have been put towards addressing the problem of abusive content
detection, commonly defined as a supervised classification task. The research
effort has spread out across several closely related sub-areas, such as
detection of hate speech, toxicity, cyberbullying, etc. There is a pressing
need to consolidate the field under a common framework for task formulation,
dataset design and performance evaluation. Further, despite current
technologies achieving high classification accuracies, several ethical issues
have been revealed. We bring ethical issues to forefront and propose a unified
framework as a two-step process. First, online content is categorized around
personal and identity-related subject matters. Second, severity of abuse is
identified through comparative annotation within each category. The novel
framework is guided by the Ethics by Design principle and is a step towards
building more accurate and trusted models.
| 2,020 |
Computation and Language
|
A Comprehensive Survey on Word Representation Models: From Classical to
State-Of-The-Art Word Representation Language Models
|
Word representation has always been an important research area in the history
of natural language processing (NLP). Understanding such complex text data is
imperative, given that it is rich in information and can be used widely across
various applications. In this survey, we explore different word representation
models and its power of expression, from the classical to modern-day
state-of-the-art word representation language models (LMS). We describe a
variety of text representation methods, and model designs have blossomed in the
context of NLP, including SOTA LMs. These models can transform large volumes of
text into effective vector representations capturing the same semantic
information. Further, such representations can be utilized by various machine
learning (ML) algorithms for a variety of NLP related tasks. In the end, this
survey briefly discusses the commonly used ML and DL based classifiers,
evaluation metrics and the applications of these word embeddings in different
NLP tasks.
| 2,020 |
Computation and Language
|
Graph-based Topic Extraction from Vector Embeddings of Text Documents:
Application to a Corpus of News Articles
|
Production of news content is growing at an astonishing rate. To help manage
and monitor the sheer amount of text, there is an increasing need to develop
efficient methods that can provide insights into emerging content areas, and
stratify unstructured corpora of text into `topics' that stem intrinsically
from content similarity. Here we present an unsupervised framework that brings
together powerful vector embeddings from natural language processing with tools
from multiscale graph partitioning that can reveal natural partitions at
different resolutions without making a priori assumptions about the number of
clusters in the corpus. We show the advantages of graph-based clustering
through end-to-end comparisons with other popular clustering and topic
modelling methods, and also evaluate different text vector embeddings, from
classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert.
This comparative work is showcased through an analysis of a corpus of US news
coverage during the presidential election year of 2016.
| 2,020 |
Computation and Language
|
Handling Class Imbalance in Low-Resource Dialogue Systems by Combining
Few-Shot Classification and Interpolation
|
Utterance classification performance in low-resource dialogue systems is
constrained by an inevitably high degree of data imbalance in class labels. We
present a new end-to-end pairwise learning framework that is designed
specifically to tackle this phenomenon by inducing a few-shot classification
capability in the utterance representations and augmenting data through an
interpolation of utterance representations. Our approach is a general purpose
training methodology, agnostic to the neural architecture used for encoding
utterances. We show significant improvements in macro-F1 score over standard
cross-entropy training for three different neural architectures, demonstrating
improvements on a Virtual Patient dialogue dataset as well as a low-resourced
emulation of the Switchboard dialogue act classification dataset.
| 2,020 |
Computation and Language
|
Detecting Stance in Media on Global Warming
|
Citing opinions is a powerful yet understudied strategy in argumentation. For
example, an environmental activist might say, "Leading scientists agree that
global warming is a serious concern," framing a clause which affirms their own
stance ("that global warming is serious") as an opinion endorsed ("[scientists]
agree") by a reputable source ("leading"). In contrast, a global warming denier
might frame the same clause as the opinion of an untrustworthy source with a
predicate connoting doubt: "Mistaken scientists claim [...]." Our work studies
opinion-framing in the global warming (GW) debate, an increasingly partisan
issue that has received little attention in NLP. We introduce Global Warming
Stance Dataset (GWSD), a dataset of stance-labeled GW sentences, and train a
BERT classifier to study novel aspects of argumentation in how different sides
of a debate represent their own and each other's opinions. From 56K news
articles, we find that similar linguistic devices for self-affirming and
opponent-doubting discourse are used across GW-accepting and skeptic media,
though GW-skeptical media shows more opponent-doubt. We also find that authors
often characterize sources as hypocritical, by ascribing opinions expressing
the author's own view to source entities known to publicly endorse the opposing
view. We release our stance dataset, model, and lexicons of framing devices for
future work on opinion-framing and the automatic detection of GW stance.
| 2,020 |
Computation and Language
|
A Visuospatial Dataset for Naturalistic Verb Learning
|
We introduce a new dataset for training and evaluating grounded language
models. Our data is collected within a virtual reality environment and is
designed to emulate the quality of language data to which a pre-verbal child is
likely to have access: That is, naturalistic, spontaneous speech paired with
richly grounded visuospatial context. We use the collected data to compare
several distributional semantics models for verb learning. We evaluate neural
models based on 2D (pixel) features as well as feature-engineered models based
on 3D (symbolic, spatial) features, and show that neither modeling approach
achieves satisfactory performance. Our results are consistent with evidence
from child language acquisition that emphasizes the difficulty of learning
verbs from naive distributional data. We discuss avenues for future work on
cognitively-inspired grounded language learning, and release our corpus with
the intent of facilitating research on the topic.
| 2,020 |
Computation and Language
|
CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence
Models
|
Copy mechanisms are employed in sequence to sequence models (seq2seq) to
generate reproductions of words from the input to the output. These frameworks,
operating at the lexical type level, fail to provide an explicit alignment that
records where each token was copied from. Further, they require contiguous
token sequences from the input (spans) to be copied individually. We present a
model with an explicit token-level copy operation and extend it to copying
entire spans. Our model provides hard alignments between spans in the input and
output, allowing for nontraditional applications of seq2seq, like information
extraction. We demonstrate the approach on Nested Named Entity Recognition,
achieving near state-of-the-art accuracy with an order of magnitude increase in
decoding speed.
| 2,020 |
Computation and Language
|
Uncovering Latent Biases in Text: Method and Application to Peer Review
|
Quantifying systematic disparities in numerical quantities such as employment
rates and wages between population subgroups provides compelling evidence for
the existence of societal biases. However, biases in the text written for
members of different subgroups (such as in recommendation letters for male and
non-male candidates), though widely reported anecdotally, remain challenging to
quantify. In this work, we introduce a novel framework to quantify bias in text
caused by the visibility of subgroup membership indicators. We develop a
nonparametric estimation and inference procedure to estimate this bias. We then
formalize an identification strategy to causally link the estimated bias to the
visibility of subgroup membership indicators, provided observations from time
periods both before and after an identity-hiding policy change. We identify an
application wherein "ground truth" bias can be inferred to evaluate our
framework, instead of relying on synthetic or secondary data. Specifically, we
apply our framework to quantify biases in the text of peer reviews from a
reputed machine learning conference before and after the conference adopted a
double-blind reviewing policy. We show evidence of biases in the review ratings
that serves as "ground truth", and show that our proposed framework accurately
detects these biases from the review text without having access to the review
ratings.
| 2,020 |
Computation and Language
|
"where is this relationship going?": Understanding Relationship
Trajectories in Narrative Text
|
We examine a new commonsense reasoning task: given a narrative describing a
social interaction that centers on two protagonists, systems make inferences
about the underlying relationship trajectory. Specifically, we propose two
evaluation tasks: Relationship Outlook Prediction MCQ and Resolution Prediction
MCQ. In Relationship Outlook Prediction, a system maps an interaction to a
relationship outlook that captures how the interaction is expected to change
the relationship. In Resolution Prediction, a system attributes a given
relationship outlook to a particular resolution that explains the outcome.
These two tasks parallel two real-life questions that people frequently ponder
upon as they navigate different social situations: "where is this relationship
going?" and "how did we end up here?". To facilitate the investigation of human
social relationships through these two tasks, we construct a new dataset,
Social Narrative Tree, which consists of 1250 stories documenting a variety of
daily social interactions. The narratives encode a multitude of social elements
that interweave to give rise to rich commonsense knowledge of how relationships
evolve with respect to social interactions. We establish baseline performances
using language models and the accuracies are significantly lower than human
performance. The results demonstrate that models need to look beyond syntactic
and semantic signals to comprehend complex human relationships.
| 2,020 |
Computation and Language
|
Multiple Sclerosis Severity Classification From Clinical Text
|
Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative
neurological disease, which is monitored by a specialist using the Expanded
Disability Status Scale (EDSS) and recorded in unstructured text in the form of
a neurology consult note. An EDSS measurement contains an overall "EDSS" score
and several functional subscores. Typically, expert knowledge is required to
interpret consult notes and generate these scores. Previous approaches used
limited context length Word2Vec embeddings and keyword searches to predict
scores given a consult note, but often failed when scores were not explicitly
stated. In this work, we present MS-BERT, the first publicly available
transformer model trained on real clinical data other than MIMIC. Next, we
present MSBC, a classifier that applies MS-BERT to generate embeddings and
predict EDSS and functional subscores. Lastly, we explore combining MSBC with
other models through the use of Snorkel to generate scores for unlabelled
consult notes. MSBC achieves state-of-the-art performance on all metrics and
prediction tasks and outperforms the models generated from the Snorkel
ensemble. We improve Macro-F1 by 0.12 (to 0.88) for predicting EDSS and on
average by 0.29 (to 0.63) for predicting functional subscores over previous
Word2Vec CNN and rule-based approaches.
| 2,020 |
Computation and Language
|
Combining Self-Training and Self-Supervised Learning for Unsupervised
Disfluency Detection
|
Most existing approaches to disfluency detection heavily rely on
human-annotated corpora, which is expensive to obtain in practice. There have
been several proposals to alleviate this issue with, for instance,
self-supervised learning techniques, but they still require human-annotated
corpora. In this work, we explore the unsupervised learning paradigm which can
potentially work with unlabeled text corpora that are cheaper and easier to
obtain. Our model builds upon the recent work on Noisy Student Training, a
semi-supervised learning approach that extends the idea of self-training.
Experimental results on the commonly used English Switchboard test set show
that our approach achieves competitive performance compared to the previous
state-of-the-art supervised systems using contextualized word embeddings (e.g.
BERT and ELECTRA).
| 2,020 |
Computation and Language
|
Conversation Graph: Data Augmentation, Training and Evaluation for
Non-Deterministic Dialogue Management
|
Task-oriented dialogue systems typically rely on large amounts of
high-quality training data or require complex handcrafted rules. However,
existing datasets are often limited in size considering the complexity of the
dialogues. Additionally, conventional training signal inference is not suitable
for non-deterministic agent behaviour, i.e. considering multiple actions as
valid in identical dialogue states. We propose the Conversation Graph
(ConvGraph), a graph-based representation of dialogues that can be exploited
for data augmentation, multi-reference training and evaluation of
non-deterministic agents. ConvGraph generates novel dialogue paths to augment
data volume and diversity. Intrinsic and extrinsic evaluation across three
datasets shows that data augmentation and/or multi-reference training with
ConvGraph can improve dialogue success rates by up to 6.4%.
| 2,020 |
Computation and Language
|
Tilde at WMT 2020: News Task Systems
|
This paper describes Tilde's submission to the WMT2020 shared task on news
translation for both directions of the English-Polish language pair in both the
constrained and the unconstrained tracks. We follow our submissions from the
previous years and build our baseline systems to be morphologically motivated
sub-word unit-based Transformer base models that we train using the Marian
machine translation toolkit. Additionally, we experiment with different
parallel and monolingual data selection schemes, as well as sampled
back-translation. Our final models are ensembles of Transformer base and
Transformer big models that feature right-to-left re-ranking.
| 2,020 |
Computation and Language
|
Memory Attentive Fusion: External Language Model Integration for
Transformer-based Sequence-to-Sequence Model
|
This paper presents a novel fusion method for integrating an external
language model (LM) into the Transformer based sequence-to-sequence (seq2seq)
model. While paired data are basically required to train the seq2seq model, the
external LM can be trained with only unpaired data. Thus, it is important to
leverage memorized knowledge in the external LM for building the seq2seq model,
since it is hard to prepare a large amount of paired data. However, the
existing fusion methods assume that the LM is integrated with recurrent neural
network-based seq2seq models instead of the Transformer. Therefore, this paper
proposes a fusion method that can explicitly utilize network structures in the
Transformer. The proposed method, called {\bf memory attentive fusion},
leverages the Transformer-style attention mechanism that repeats source-target
attention in a multi-hop manner for reading the memorized knowledge in the LM.
Our experiments on two text-style conversion tasks demonstrate that the
proposed method performs better than conventional fusion methods.
| 2,020 |
Computation and Language
|
Named Entity Recognition for Social Media Texts with Semantic
Augmentation
|
Existing approaches for named entity recognition suffer from data sparsity
problems when conducted on short and informal texts, especially user-generated
social media content. Semantic augmentation is a potential way to alleviate
this problem. Given that rich semantic information is implicitly preserved in
pre-trained word embeddings, they are potential ideal resources for semantic
augmentation. In this paper, we propose a neural-based approach to NER for
social media texts where both local (from running text) and augmented semantics
are taken into account. In particular, we obtain the augmented semantic
information from a large-scale corpus, and propose an attentive semantic
augmentation module and a gate module to encode and aggregate such information,
respectively. Extensive experiments are performed on three benchmark datasets
collected from English and Chinese social media platforms, where the results
demonstrate the superiority of our approach to previous studies across all
three datasets.
| 2,020 |
Computation and Language
|
Improving Named Entity Recognition with Attentive Ensemble of Syntactic
Information
|
Named entity recognition (NER) is highly sensitive to sentential syntactic
and semantic properties where entities may be extracted according to how they
are used and placed in the running text. To model such properties, one could
rely on existing resources to providing helpful knowledge to the NER task; some
existing studies proved the effectiveness of doing so, and yet are limited in
appropriately leveraging the knowledge such as distinguishing the important
ones for particular context. In this paper, we improve NER by leveraging
different types of syntactic information through attentive ensemble, which
functionalizes by the proposed key-value memory networks, syntax attention, and
the gate mechanism for encoding, weighting and aggregating such syntactic
information, respectively. Experimental results on six English and Chinese
benchmark datasets suggest the effectiveness of the proposed model and show
that it outperforms previous studies on all experiment datasets.
| 2,020 |
Computation and Language
|
Unbabel's Participation in the WMT20 Metrics Shared Task
|
We present the contribution of the Unbabel team to the WMT 2020 Shared Task
on Metrics. We intend to participate on the segment-level, document-level and
system-level tracks on all language pairs, as well as the 'QE as a Metric'
track. Accordingly, we illustrate results of our models in these tracks with
reference to test sets from the previous year. Our submissions build upon the
recently proposed COMET framework: We train several estimator models to regress
on different human-generated quality scores and a novel ranking model trained
on relative ranks obtained from Direct Assessments. We also propose a simple
technique for converting segment-level predictions into a document-level score.
Overall, our systems achieve strong results for all language pairs on previous
test sets and in many cases set a new state-of-the-art.
| 2,020 |
Computation and Language
|
May I Ask Who's Calling? Named Entity Recognition on Call Center
Transcripts for Privacy Law Compliance
|
We investigate using Named Entity Recognition on a new type of user-generated
text: a call center conversation. These conversations combine problems from
spontaneous speech with problems novel to conversational Automated Speech
Recognition, including incorrect recognition, alongside other common problems
from noisy user-generated text. Using our own corpus with new annotations,
training custom contextual string embeddings, and applying a BiLSTM-CRF, we
match state-of-the-art results on our novel task.
| 2,020 |
Computation and Language
|
Explainable Automated Coding of Clinical Notes using Hierarchical
Label-wise Attention Networks and Label Embedding Initialisation
|
Diagnostic or procedural coding of clinical notes aims to derive a coded
summary of disease-related information about patients. Such coding is usually
done manually in hospitals but could potentially be automated to improve the
efficiency and accuracy of medical coding. Recent studies on deep learning for
automated medical coding achieved promising performances. However, the
explainability of these models is usually poor, preventing them to be used
confidently in supporting clinical practice. Another limitation is that these
models mostly assume independence among labels, ignoring the complex
correlation among medical codes which can potentially be exploited to improve
the performance. We propose a Hierarchical Label-wise Attention Network (HLAN),
which aimed to interpret the model by quantifying importance (as attention
weights) of words and sentences related to each of the labels. Secondly, we
propose to enhance the major deep learning models with a label embedding (LE)
initialisation approach, which learns a dense, continuous vector representation
and then injects the representation into the final layers and the label-wise
attention layers in the models. We evaluated the methods using three settings
on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS
COVID-19 shielding codes. Experiments were conducted to compare HLAN and LE
initialisation to the state-of-the-art neural network based methods. HLAN
achieved the best Micro-level AUC and $F_1$ on the top-50 code prediction and
comparable results on the NHS COVID-19 shielding code prediction to other
models. By highlighting the most salient words and sentences for each label,
HLAN showed more meaningful and comprehensive model interpretation compared to
its downgraded baselines and the CNN-based models. LE initialisation
consistently boosted most deep learning models for automated medical coding.
| 2,021 |
Computation and Language
|
Contextual BERT: Conditioning the Language Model Using a Global State
|
BERT is a popular language model whose main pre-training task is to fill in
the blank, i.e., predicting a word that was masked out of a sentence, based on
the remaining words. In some applications, however, having an additional
context can help the model make the right prediction, e.g., by taking the
domain or the time of writing into account. This motivates us to advance the
BERT architecture by adding a global state for conditioning on a fixed-sized
context. We present our two novel approaches and apply them to an industry
use-case, where we complete fashion outfits with missing articles, conditioned
on a specific customer. An experimental comparison to other methods from the
literature shows that our methods improve personalization significantly.
| 2,020 |
Computation and Language
|
Few-Shot Complex Knowledge Base Question Answering via Meta
Reinforcement Learning
|
Complex question-answering (CQA) involves answering complex natural-language
questions on a knowledge base (KB). However, the conventional neural program
induction (NPI) approach exhibits uneven performance when the questions have
different types, harboring inherently different characteristics, e.g.,
difficulty level. This paper proposes a meta-reinforcement learning approach to
program induction in CQA to tackle the potential distributional bias in
questions. Our method quickly and effectively adapts the meta-learned
programmer to new questions based on the most similar questions retrieved from
the training data. The meta-learned policy is then used to learn a good
programming policy, utilizing the trial trajectories and their rewards for
similar questions in the support set. Our method achieves state-of-the-art
performance on the CQA dataset (Saha et al., 2018) while using only five trial
trajectories for the top-5 retrieved questions in each support set, and
metatraining on tasks constructed from only 1% of the training set. We have
released our code at https://github.com/DevinJake/MRL-CQA.
| 2,020 |
Computation and Language
|
Less is More: Data-Efficient Complex Question Answering over Knowledge
Bases
|
Question answering is an effective method for obtaining information from
knowledge bases (KB). In this paper, we propose the Neural-Symbolic Complex
Question Answering (NS-CQA) model, a data-efficient reinforcement learning
framework for complex question answering by using only a modest number of
training samples. Our framework consists of a neural generator and a symbolic
executor that, respectively, transforms a natural-language question into a
sequence of primitive actions, and executes them over the knowledge base to
compute the answer. We carefully formulate a set of primitive symbolic actions
that allows us to not only simplify our neural network design but also
accelerate model convergence. To reduce search space, we employ the copy and
masking mechanisms in our encoder-decoder architecture to drastically reduce
the decoder output vocabulary and improve model generalizability. We equip our
model with a memory buffer that stores high-reward promising programs. Besides,
we propose an adaptive reward function. By comparing the generated trial with
the trials stored in the memory buffer, we derive the curriculum-guided reward
bonus, i.e., the proximity and the novelty. To mitigate the sparse reward
problem, we combine the adaptive reward and the reward bonus, reshaping the
sparse reward into dense feedback. Also, we encourage the model to generate new
trials to avoid imitating the spurious trials while making the model remember
the past high-reward trials to improve data efficiency. Our NS-CQA model is
evaluated on two datasets: CQA, a recent large-scale complex question answering
dataset, and WebQuestionsSP, a multi-hop question answering dataset. On both
datasets, our model outperforms the state-of-the-art models. Notably, on CQA,
NS-CQA performs well on questions with higher complexity, while only using
approximately 1% of the total training samples.
| 2,020 |
Computation and Language
|
Learning as Abduction: Trainable Natural Logic Theorem Prover for
Natural Language Inference
|
Tackling Natural Language Inference with a logic-based method is becoming
less and less common. While this might have been counterintuitive several
decades ago, nowadays it seems pretty obvious. The main reasons for such a
conception are that (a) logic-based methods are usually brittle when it comes
to processing wide-coverage texts, and (b) instead of automatically learning
from data, they require much of manual effort for development. We make a step
towards to overcome such shortcomings by modeling learning from data as
abduction: reversing a theorem-proving procedure to abduce semantic relations
that serve as the best explanation for the gold label of an inference problem.
In other words, instead of proving sentence-level inference relations with the
help of lexical relations, the lexical relations are proved taking into account
the sentence-level inference relations. We implement the learning method in a
tableau theorem prover for natural language and show that it improves the
performance of the theorem prover on the SICK dataset by 1.4% while still
maintaining high precision (>94%). The obtained results are competitive with
the state of the art among logic-based systems.
| 2,020 |
Computation and Language
|
How Many Pages? Paper Length Prediction from the Metadata
|
Being able to predict the length of a scientific paper may be helpful in
numerous situations. This work defines the paper length prediction task as a
regression problem and reports several experimental results using popular
machine learning models. We also create a huge dataset of publication metadata
and the respective lengths in number of pages. The dataset will be freely
available and is intended to foster research in this domain. As future work, we
would like to explore more advanced regressors based on neural networks and big
pretrained language models.
| 2,020 |
Computation and Language
|
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
|
In this paper, we introduce an advanced Russian general language
understanding evaluation benchmark -- RussianGLUE. Recent advances in the field
of universal language models and transformers require the development of a
methodology for their broad diagnostics and testing for general intellectual
skills - detection of natural language inference, commonsense reasoning,
ability to perform simple logical operations regardless of text subject or
lexicon. For the first time, a benchmark of nine tasks, collected and organized
analogically to the SuperGLUE methodology, was developed from scratch for the
Russian language. We provide baselines, human level evaluation, an open-source
framework for evaluating models
(https://github.com/RussianNLP/RussianSuperGLUE), and an overall leaderboard of
transformer models for the Russian language. Besides, we present the first
results of comparing multilingual models in the adapted diagnostic test set and
offer the first steps to further expanding or assessing state-of-the-art models
independently of language.
| 2,023 |
Computation and Language
|
RuREBus: a Case Study of Joint Named Entity Recognition and Relation
Extraction from e-Government Domain
|
We show-case an application of information extraction methods, such as named
entity recognition (NER) and relation extraction (RE) to a novel corpus,
consisting of documents, issued by a state agency. The main challenges of this
corpus are: 1) the annotation scheme differs greatly from the one used for the
general domain corpora, and 2) the documents are written in a language other
than English. Unlike expectations, the state-of-the-art transformer-based
models show modest performance for both tasks, either when approached
sequentially, or in an end-to-end fashion. Our experiments have demonstrated
that fine-tuning on a large unlabeled corpora does not automatically yield
significant improvement and thus we may conclude that more sophisticated
strategies of leveraging unlabelled texts are demanded. In this paper, we
describe the whole developed pipeline, starting from text annotation, baseline
development, and designing a shared task in hopes of improving the baseline.
Eventually, we realize that the current NER and RE technologies are far from
being mature and do not overcome so far challenges like ours.
| 2,020 |
Computation and Language
|
AutoPrompt: Eliciting Knowledge from Language Models with Automatically
Generated Prompts
|
The remarkable success of pretrained language models has motivated the study
of what kinds of knowledge these models learn during pretraining. Reformulating
tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach
for gauging such knowledge, however, its usage is limited by the manual effort
and guesswork required to write suitable prompts. To address this, we develop
AutoPrompt, an automated method to create prompts for a diverse set of tasks,
based on a gradient-guided search. Using AutoPrompt, we show that masked
language models (MLMs) have an inherent capability to perform sentiment
analysis and natural language inference without additional parameters or
finetuning, sometimes achieving performance on par with recent state-of-the-art
supervised models. We also show that our prompts elicit more accurate factual
knowledge from MLMs than the manually created prompts on the LAMA benchmark,
and that MLMs can be used as relation extractors more effectively than
supervised relation extraction models. These results demonstrate that
automatically generated prompts are a viable parameter-free alternative to
existing probing methods, and as pretrained LMs become more sophisticated and
capable, potentially a replacement for finetuning.
| 2,020 |
Computation and Language
|
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of
Clinical Question Answering
|
Clinical question answering (QA) aims to automatically answer questions from
medical professionals based on clinical texts. Studies show that neural QA
models trained on one corpus may not generalize well to new clinical texts from
a different institute or a different patient group, where large-scale QA pairs
are not readily available for model retraining. To address this challenge, we
propose a simple yet effective framework, CliniQG4QA, which leverages question
generation (QG) to synthesize QA pairs on new clinical contexts and boosts QA
models without requiring manual annotations. In order to generate diverse types
of questions that are essential for training QA models, we further introduce a
seq2seq-based question phrase prediction (QPP) module that can be used together
with most existing QG models to diversify the generation. Our comprehensive
experiment results show that the QA corpus generated by our framework can
improve QA models on the new contexts (up to 8% absolute gain in terms of Exact
Match), and that the QPP module plays a crucial role in achieving the gain.
| 2,021 |
Computation and Language
|
VECO: Variable and Flexible Cross-lingual Pre-training for Language
Understanding and Generation
|
Existing work in multilingual pretraining has demonstrated the potential of
cross-lingual transferability by training a unified Transformer encoder for
multiple languages. However, much of this work only relies on the shared
vocabulary and bilingual contexts to encourage the correlation across
languages, which is loose and implicit for aligning the contextual
representations between languages. In this paper, we plug a cross-attention
module into the Transformer encoder to explicitly build the interdependence
between languages. It can effectively avoid the degeneration of predicting
masked words only conditioned on the context in its own language. More
importantly, when fine-tuning on downstream tasks, the cross-attention module
can be plugged in or out on-demand, thus naturally benefiting a wider range of
cross-lingual tasks, from language understanding to generation.
As a result, the proposed cross-lingual model delivers new state-of-the-art
results on various cross-lingual understanding tasks of the XTREME benchmark,
covering text classification, sequence labeling, question answering, and
sentence retrieval. For cross-lingual generation tasks, it also outperforms all
existing cross-lingual models and state-of-the-art Transformer variants on
WMT14 English-to-German and English-to-French translation datasets, with gains
of up to 1~2 BLEU.
| 2,021 |
Computation and Language
|
Generating Radiology Reports via Memory-driven Transformer
|
Medical imaging is frequently used in clinical practice and trials for
diagnosis and treatment. Writing imaging reports is time-consuming and can be
error-prone for inexperienced radiologists. Therefore, automatically generating
radiology reports is highly desired to lighten the workload of radiologists and
accordingly promote clinical automation, which is an essential task to apply
artificial intelligence to the medical domain. In this paper, we propose to
generate radiology reports with memory-driven Transformer, where a relational
memory is designed to record key information of the generation process and a
memory-driven conditional layer normalization is applied to incorporating the
memory into the decoder of Transformer. Experimental results on two prevailing
radiology report datasets, IU X-Ray and MIMIC-CXR, show that our proposed
approach outperforms previous models with respect to both language generation
metrics and clinical evaluations. Particularly, this is the first work
reporting the generation results on MIMIC-CXR to the best of our knowledge.
Further analyses also demonstrate that our approach is able to generate long
reports with necessary medical terms as well as meaningful image-text attention
mappings.
| 2,022 |
Computation and Language
|
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot
Relational Triple Extraction
|
Current supervised relational triple extraction approaches require huge
amounts of labeled data and thus suffer from poor performance in few-shot
settings. However, people can grasp new knowledge by learning a few instances.
To this end, we take the first step to study the few-shot relational triple
extraction, which has not been well understood. Unlike previous single-task
few-shot problems, relational triple extraction is more challenging as the
entities and relations have implicit correlations. In this paper, We propose a
novel multi-prototype embedding network model to jointly extract the
composition of relational triples, namely, entity pairs and corresponding
relations. To be specific, we design a hybrid prototypical learning mechanism
that bridges text and knowledge concerning both entities and relations. Thus,
implicit correlations between entities and relations are injected.
Additionally, we propose a prototype-aware regularization to learn more
representative prototypes. Experimental results demonstrate that the proposed
method can improve the performance of the few-shot triple extraction.
| 2,023 |
Computation and Language
|
Logic-guided Semantic Representation Learning for Zero-Shot Relation
Classification
|
Relation classification aims to extract semantic relations between entity
pairs from the sentences. However, most existing methods can only identify seen
relation classes that occurred during training. To recognize unseen relations
at test time, we explore the problem of zero-shot relation classification.
Previous work regards the problem as reading comprehension or textual
entailment, which have to rely on artificial descriptive information to improve
the understandability of relation types. Thus, rich semantic knowledge of the
relation labels is ignored. In this paper, we propose a novel logic-guided
semantic representation learning model for zero-shot relation classification.
Our approach builds connections between seen and unseen relations via implicit
and explicit semantic representations with knowledge graph embeddings and logic
rules. Extensive experimental results demonstrate that our method can
generalize to unseen relation types and achieve promising improvements.
| 2,020 |
Computation and Language
|
Cross-Domain Sentiment Classification with Contrastive Learning and
Mutual Information Maximization
|
Contrastive learning (CL) has been successful as a powerful representation
learning method. In this work we propose CLIM: Contrastive Learning with mutual
Information Maximization, to explore the potential of CL on cross-domain
sentiment classification. To the best of our knowledge, CLIM is the first to
adopt contrastive learning for natural language processing (NLP) tasks across
domains. Due to scarcity of labels on the target domain, we introduce mutual
information maximization (MIM) apart from CL to exploit the features that best
support the final prediction. Furthermore, MIM is able to maintain a relatively
balanced distribution of the model's prediction, and enlarges the margin
between classes on the target domain. The larger margin increases our model's
robustness and enables the same classifier to be optimal across domains.
Consequently, we achieve new state-of-the-art results on the Amazon-review
dataset as well as the airlines dataset, showing the efficacy of our proposed
method CLIM.
| 2,020 |
Computation and Language
|
Target Word Masking for Location Metonymy Resolution
|
Existing metonymy resolution approaches rely on features extracted from
external resources like dictionaries and hand-crafted lexical resources. In
this paper, we propose an end-to-end word-level classification approach based
only on BERT, without dependencies on taggers, parsers, curated dictionaries of
place names, or other external resources. We show that our approach achieves
the state-of-the-art on 5 datasets, surpassing conventional BERT models and
benchmarks by a large margin. We also show that our approach generalises well
to unseen data.
| 2,022 |
Computation and Language
|
HyperText: Endowing FastText with Hyperbolic Geometry
|
Natural language data exhibit tree-like hierarchical structures such as the
hypernym-hyponym relations in WordNet. FastText, as the state-of-the-art text
classifier based on shallow neural network in Euclidean space, may not model
such hierarchies precisely with limited representation capacity. Considering
that hyperbolic space is naturally suitable for modeling tree-like hierarchical
data, we propose a new model named HyperText for efficient text classification
by endowing FastText with hyperbolic geometry. Empirically, we show that
HyperText outperforms FastText on a range of text classification tasks with
much reduced parameters.
| 2,021 |
Computation and Language
|
Biomedical Concept Relatedness -- A large EHR-based benchmark
|
A promising application of AI to healthcare is the retrieval of information
from electronic health records (EHRs), e.g. to aid clinicians in finding
relevant information for a consultation or to recruit suitable patients for a
study. This requires search capabilities far beyond simple string matching,
including the retrieval of concepts (diagnoses, symptoms, medications, etc.)
related to the one in question. The suitability of AI methods for such
applications is tested by predicting the relatedness of concepts with known
relatedness scores. However, all existing biomedical concept relatedness
datasets are notoriously small and consist of hand-picked concept pairs. We
open-source a novel concept relatedness benchmark overcoming these issues: it
is six times larger than existing datasets and concept pairs are chosen based
on co-occurrence in EHRs, ensuring their relevance for the application of
interest. We present an in-depth analysis of our new dataset and compare it to
existing ones, highlighting that it is not only larger but also complements
existing datasets in terms of the types of concepts included. Initial
experiments with state-of-the-art embedding methods show that our dataset is a
challenging new benchmark for testing concept relatedness models.
| 2,020 |
Computation and Language
|
"Thy algorithm shalt not bear false witness": An Evaluation of
Multiclass Debiasing Methods on Word Embeddings
|
With the vast development and employment of artificial intelligence
applications, research into the fairness of these algorithms has been
increased. Specifically, in the natural language processing domain, it has been
shown that social biases persist in word embeddings and are thus in danger of
amplifying these biases when used. As an example of social bias, religious
biases are shown to persist in word embeddings and the need for its removal is
highlighted. This paper investigates the state-of-the-art multiclass debiasing
techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It
evaluates their performance when removing religious bias on a common basis by
quantifying bias removal via the Word Embedding Association Test (WEAT), Mean
Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias
(RNSB). By investigating the religious bias removal on three widely used word
embeddings, namely: Word2Vec, GloVe, and ConceptNet, it is shown that the
preferred method is ConceptorDebiasing. Specifically, this technique manages to
decrease the measured religious bias on average by 82,42%, 96,78% and 54,76%
for the three word embedding sets respectively.
| 2,020 |
Computation and Language
|
SLM: Learning a Discourse Language Representation with Sentence
Unshuffling
|
We introduce Sentence-level Language Modeling, a new pre-training objective
for learning a discourse language representation in a fully self-supervised
manner. Recent pre-training methods in NLP focus on learning either bottom or
top-level language representations: contextualized word representations derived
from language model objectives at one extreme and a whole sequence
representation learned by order classification of two given textual segments at
the other. However, these models are not directly encouraged to capture
representations of intermediate-size structures that exist in natural languages
such as sentences and the relationships among them. To that end, we propose a
new approach to encourage learning of a contextualized sentence-level
representation by shuffling the sequence of input sentences and training a
hierarchical transformer model to reconstruct the original ordering. Through
experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show
that this feature of our model improves the performance of the original BERT by
large margins.
| 2,020 |
Computation and Language
|
Towards Accurate and Consistent Evaluation: A Dataset for
Distantly-Supervised Relation Extraction
|
In recent years, distantly-supervised relation extraction has achieved a
certain success by using deep neural networks. Distant Supervision (DS) can
automatically generate large-scale annotated data by aligning entity pairs from
Knowledge Bases (KB) to sentences. However, these DS-generated datasets
inevitably have wrong labels that result in incorrect evaluation scores during
testing, which may mislead the researchers. To solve this problem, we build a
new dataset NYTH, where we use the DS-generated data as training data and hire
annotators to label test data. Compared with the previous datasets, NYT-H has a
much larger test set and then we can perform more accurate and consistent
evaluation. Finally, we present the experimental results of several widely used
systems on NYT-H. The experimental results show that the ranking lists of the
comparison systems on the DS-labelled test data and human-annotated test data
are different. This indicates that our human-annotated data is necessary for
evaluation of distantly-supervised relation extraction.
| 2,020 |
Computation and Language
|
A Critical Assessment of State-of-the-Art in Entity Alignment
|
In this work, we perform an extensive investigation of two state-of-the-art
(SotA) methods for the task of Entity Alignment in Knowledge Graphs. Therefore,
we first carefully examine the benchmarking process and identify several
shortcomings, which make the results reported in the original works not always
comparable. Furthermore, we suspect that it is a common practice in the
community to make the hyperparameter optimization directly on a test set,
reducing the informative value of reported performance. Thus, we select a
representative sample of benchmarking datasets and describe their properties.
We also examine different initializations for entity representations since they
are a decisive factor for model performance. Furthermore, we use a shared
train/validation/test split for a fair evaluation setting in which we evaluate
all methods on all datasets. In our evaluation, we make several interesting
findings. While we observe that most of the time SotA approaches perform better
than baselines, they have difficulties when the dataset contains noise, which
is the case in most real-life applications. Moreover, we find out in our
ablation study that often different features of SotA methods are crucial for
good performance than previously assumed. The code is available at
https://github.com/mberr/ea-sota-comparison.
| 2,021 |
Computation and Language
|
Topic-Preserving Synthetic News Generation: An Adversarial Deep
Reinforcement Learning Approach
|
Nowadays, there exist powerful language models such as OpenAI's GPT-2 that
can generate readable text and can be fine-tuned to generate text for a
specific domain. Considering GPT-2, it cannot directly generate synthetic news
with respect to a given topic and the output of the language model cannot be
explicitly controlled. In this paper, we study the novel problem of
topic-preserving synthetic news generation. We propose a novel deep
reinforcement learning-based method to control the output of GPT-2 with respect
to a given news topic. When generating text using GPT-2, by default, the most
probable word is selected from the vocabulary. Instead of selecting the best
word each time from GPT-2's output, an RL agent tries to select words that
optimize the matching of a given topic. In addition, using a fake news detector
as an adversary, we investigate generating realistic news using our proposed
method. In this paper, we consider realistic news as news that cannot be easily
detected by a fake news classifier. Experimental results demonstrate the
effectiveness of the proposed framework on generating topic-preserving news
content than state-of-the-art baselines.
| 2,020 |
Computation and Language
|
A Cross-lingual Natural Language Processing Framework for Infodemic
Management
|
The COVID-19 pandemic has put immense pressure on health systems which are
further strained due to the misinformation surrounding it. Under such a
situation, providing the right information at the right time is crucial. There
is a growing demand for the management of information spread using Artificial
Intelligence. Hence, we have exploited the potential of Natural Language
Processing for identifying relevant information that needs to be disseminated
amongst the masses. In this work, we present a novel Cross-lingual Natural
Language Processing framework to provide relevant information by matching daily
news with trusted guidelines from the World Health Organization. The proposed
pipeline deploys various techniques of NLP such as summarizers, word
embeddings, and similarity metrics to provide users with news articles along
with a corresponding healthcare guideline. A total of 36 models were evaluated
and a combination of LexRank based summarizer on Word2Vec embedding with Word
Mover distance metric outperformed all other models. This novel open-source
approach can be used as a template for proactive dissemination of relevant
healthcare information in the midst of misinformation spread associated with
epidemics.
| 2,020 |
Computation and Language
|
Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents
|
Images can give us insights into the contextual meanings of words, but
current image-text grounding approaches require detailed annotations. Such
granular annotation is rare, expensive, and unavailable in most domain-specific
contexts. In contrast, unlabeled multi-image, multi-sentence documents are
abundant. Can lexical grounding be learned from such documents, even though
they have significant lexical and visual overlap? Working with a case study
dataset of real estate listings, we demonstrate the challenge of distinguishing
highly correlated grounded terms, such as "kitchen" and "bedroom", and
introduce metrics to assess this document similarity. We present a simple
unsupervised clustering-based method that increases precision and recall beyond
object detection and image tagging baselines when evaluated on labeled subsets
of the dataset. The proposed method is particularly effective for local
contextual meanings of a word, for example associating "granite" with
countertops in the real estate dataset and with rocky landscapes in a Wikipedia
dataset.
| 2,020 |
Computation and Language
|
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition
|
To join the advantages of classical and end-to-end approaches for speech
recognition, we present a simple, novel and competitive approach for
phoneme-based neural transducer modeling. Different alignment label topologies
are compared and word-end-based phoneme label augmentation is proposed to
improve performance. Utilizing the local dependency of phonemes, we adopt a
simplified neural network structure and a straightforward integration with the
external word-level language model to preserve the consistency of seq-to-seq
modeling. We also present a simple, stable and efficient training procedure
using frame-wise cross-entropy loss. A phonetic context size of one is shown to
be sufficient for the best performance. A simplified scheduled sampling
approach is applied for further improvement and different decoding approaches
are briefly compared. The overall performance of our best model is comparable
to state-of-the-art (SOTA) results for the TED-LIUM Release 2 and Switchboard
corpora.
| 2,021 |
Computation and Language
|
TopicBERT for Energy Efficient Document Classification
|
Prior research notes that BERT's computational cost grows quadratically with
sequence length thus leading to longer training times, higher GPU memory
constraints and carbon emissions. While recent work seeks to address these
scalability issues at pre-training, these issues are also prominent in
fine-tuning especially for long sequence tasks like document classification.
Our work thus focuses on optimizing the computational cost of fine-tuning for
document classification. We achieve this by complementary learning of both
topic and language models in a unified framework, named TopicBERT. This
significantly reduces the number of self-attention operations - a main
performance bottleneck. Consequently, our model achieves a 1.4x ($\sim40\%$)
speedup with $\sim40\%$ reduction in $CO_2$ emission while retaining $99.9\%$
performance over 5 datasets.
| 2,020 |
Computation and Language
|
Sentiment Analysis for Roman Urdu Text over Social Media, a Comparative
Study
|
In present century, data volume is increasing enormously. The data could be
in form for image, text, voice, and video. One factor in this huge growth of
data is usage of social media where everyone is posting data on daily basis
during chatting, exchanging information, and uploading their personal and
official credential. Research of sentiments seeks to uncover abstract knowledge
in Published texts in which users communicate their emotions and thoughts about
shared content, including blogs, news and social networks. Roman Urdu is the
one of most dominant language on social networks in Pakistan and India. Roman
Urdu is among the varieties of the world's third largest Urdu language but yet
not sufficient work has been done in this language. In this article we
addressed the prior concepts and strategies used to examine the sentiment of
the roman Urdu text and reported their results as well.
| 2,020 |
Computation and Language
|
Semi-supervised Relation Extraction via Incremental Meta Self-Training
|
To alleviate human efforts from obtaining large-scale annotations,
Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in
addition to learning from limited samples. Existing self-training methods
suffer from the gradual drift problem, where noisy pseudo labels on unlabeled
data are incorporated during training. To alleviate the noise in pseudo labels,
we propose a method called MetaSRE, where a Relation Label Generation Network
generates quality assessment on pseudo labels by (meta) learning from the
successful and failed attempts on Relation Classification Network as an
additional meta-objective. To reduce the influence of noisy pseudo labels,
MetaSRE adopts a pseudo label selection and exploitation scheme which assesses
pseudo label quality on unlabeled samples and only exploits high-quality pseudo
labels in a self-training fashion to incrementally augment labeled samples for
both robustness and accuracy. Experimental results on two public datasets
demonstrate the effectiveness of the proposed approach.
| 2,021 |
Computation and Language
|
Mere account mein kitna balance hai? -- On building voice enabled
Banking Services for Multilingual Communities
|
Tremendous progress in speech and language processing has brought language
technologies closer to daily human life. Voice technology has the potential to
act as a horizontal enabling layer across all aspects of digitization. It is
especially beneficial to rural communities in scenarios like a pandemic. In
this work we present our initial exploratory work towards one such direction --
building voice enabled banking services for multilingual societies. Speech
interaction for typical banking transactions in multilingual communities
involves the presence of filled pauses and is characterized by Code Mixing.
Code Mixing is a phenomenon where lexical items from one language are embedded
in the utterance of another. Therefore speech systems deployed for banking
applications should be able to process such content. In our work we investigate
various training strategies for building speech based intent recognition
systems. We present our results using a Naive Bayes classifier on approximate
acoustic phone units using the Allosaurus library.
| 2,020 |
Computation and Language
|
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic
with Natural Language Processing (NLP)
|
The COVID-19 pandemic has had a significant impact on society, both because
of the serious health effects of COVID-19 and because of public health measures
implemented to slow its spread. Many of these difficulties are fundamentally
information needs; attempts to address these needs have caused an information
overload for both researchers and the public. Natural language processing
(NLP), the branch of artificial intelligence that interprets human language,
can be applied to address many of the information needs made urgent by the
COVID-19 pandemic. This review surveys approximately 150 NLP studies and more
than 50 systems and datasets addressing the COVID-19 pandemic. We detail work
on four core NLP tasks: information retrieval, named entity recognition,
literature-based discovery, and question answering. We also describe work that
directly addresses aspects of the pandemic through four additional tasks: topic
modeling, sentiment and emotion analysis, caseload forecasting, and
misinformation detection. We conclude by discussing observable trends and
remaining challenges.
| 2,021 |
Computation and Language
|
Streaming Simultaneous Speech Translation with Augmented Memory
Transformer
|
Transformer-based models have achieved state-of-the-art performance on speech
translation tasks. However, the model architecture is not efficient enough for
streaming scenarios since self-attention is computed over an entire input
sequence and the computational cost grows quadratically with the length of the
input sequence. Nevertheless, most of the previous work on simultaneous speech
translation, the task of generating translations from partial audio input,
ignores the time spent in generating the translation when analyzing the
latency. With this assumption, a system may have good latency quality
trade-offs but be inapplicable in real-time scenarios. In this paper, we focus
on the task of streaming simultaneous speech translation, where the systems are
not only capable of translating with partial input but are also able to handle
very long or continuous input. We propose an end-to-end transformer-based
sequence-to-sequence model, equipped with an augmented memory transformer
encoder, which has shown great success on the streaming automatic speech
recognition task with hybrid or transducer-based models. We conduct an
empirical evaluation of the proposed model on segment, context and memory sizes
and we compare our approach to a transformer with a unidirectional mask.
| 2,020 |
Computation and Language
|
A Sui Generis QA Approach using RoBERTa for Adverse Drug Event
Identification
|
Extraction of adverse drug events from biomedical literature and other
textual data is an important component to monitor drug-safety and this has
attracted attention of many researchers in healthcare. Existing works are more
pivoted around entity-relation extraction using bidirectional long short term
memory networks (Bi-LSTM) which does not attain the best feature
representations. In this paper, we introduce a question answering framework
that exploits the robustness, masking and dynamic attention capabilities of
RoBERTa by a technique of domain adaptation and attempt to overcome the
aforementioned limitations. Our model outperforms the prior work by 9.53%
F1-Score.
| 2,021 |
Computation and Language
|
A New Neural Search and Insights Platform for Navigating and Organizing
AI Research
|
To provide AI researchers with modern tools for dealing with the explosive
growth of the research literature in their field, we introduce a new platform,
AI Research Navigator, that combines classical keyword search with neural
retrieval to discover and organize relevant literature. The system provides
search at multiple levels of textual granularity, from sentences to
aggregations across documents, both in natural language and through navigation
in a domain-specific Knowledge Graph. We give an overview of the overall
architecture of the system and of the components for document analysis,
question answering, search, analytics, expert search, and recommendations.
| 2,020 |
Computation and Language
|
Dynamic Data Selection for Curriculum Learning via Ability Estimation
|
Curriculum learning methods typically rely on heuristics to estimate the
difficulty of training examples or the ability of the model. In this work, we
propose replacing difficulty heuristics with learned difficulty parameters. We
also propose Dynamic Data selection for Curriculum Learning via Ability
Estimation (DDaCLAE), a strategy that probes model ability at each training
epoch to select the best training examples at that point. We show that models
using learned difficulty and/or ability outperform heuristic-based curriculum
learning models on the GLUE classification tasks.
| 2,020 |
Computation and Language
|
Analyzing Gender Bias within Narrative Tropes
|
Popular media reflects and reinforces societal biases through the use of
tropes, which are narrative elements, such as archetypal characters and plot
arcs, that occur frequently across media. In this paper, we specifically
investigate gender bias within a large collection of tropes. To enable our
study, we crawl tvtropes.org, an online user-created repository that contains
30K tropes associated with 1.9M examples of their occurrences across film,
television, and literature. We automatically score the "genderedness" of each
trope in our TVTROPES dataset, which enables an analysis of (1) highly-gendered
topics within tropes, (2) the relationship between gender bias and popular
reception, and (3) how the gender of a work's creator correlates with the types
of tropes that they use.
| 2,020 |
Computation and Language
|
Joint Masked CPC and CTC Training for ASR
|
Self-supervised learning (SSL) has shown promise in learning representations
of audio that are useful for automatic speech recognition (ASR). But, training
SSL models like wav2vec~2.0 requires a two-stage pipeline. In this paper we
demonstrate a single-stage training of ASR models that can utilize both
unlabeled and labeled data. During training, we alternately minimize two
losses: an unsupervised masked Contrastive Predictive Coding (CPC) loss and the
supervised audio-to-text alignment loss Connectionist Temporal Classification
(CTC). We show that this joint training method directly optimizes performance
for the downstream ASR task using unsupervised data while achieving similar
word error rates to wav2vec~2.0 on the Librispeech 100-hour dataset. Finally,
we postulate that solving the contrastive task is a regularization for the
supervised CTC loss.
| 2,021 |
Computation and Language
|
Learning Structured Representations of Entity Names using Active
Learning and Weak Supervision
|
Structured representations of entity names are useful for many entity-related
tasks such as entity normalization and variant generation. Learning the
implicit structured representations of entity names without context and
external knowledge is particularly challenging. In this paper, we present a
novel learning framework that combines active learning and weak supervision to
solve this problem. Our experimental evaluation show that this framework
enables the learning of high-quality models from merely a dozen or so labeled
examples.
| 2,020 |
Computation and Language
|
Improving Dialogue Breakdown Detection with Semi-Supervised Learning
|
Building user trust in dialogue agents requires smooth and consistent
dialogue exchanges. However, agents can easily lose conversational context and
generate irrelevant utterances. These situations are called dialogue breakdown,
where agent utterances prevent users from continuing the conversation. Building
systems to detect dialogue breakdown allows agents to recover appropriately or
avoid breakdown entirely. In this paper we investigate the use of
semi-supervised learning methods to improve dialogue breakdown detection,
including continued pre-training on the Reddit dataset and a manifold-based
data augmentation method. We demonstrate the effectiveness of these methods on
the Dialogue Breakdown Detection Challenge (DBDC) English shared task. Our
submissions to the 2020 DBDC5 shared task place first, beating baselines and
other submissions by over 12\% accuracy. In ablations on DBDC4 data from 2019,
our semi-supervised learning methods improve the performance of a baseline BERT
model by 2\% accuracy. These methods are applicable generally to any dialogue
task and provide a simple way to improve model performance.
| 2,023 |
Computation and Language
|
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis
|
This paper analyzes the pre-trained hidden representations learned from
reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work
is motivated by the recent progress in BERT-based language models for ABSA.
However, it is not clear how the general proxy task of (masked) language model
trained on unlabeled corpus without annotations of aspects or opinions can
provide important features for downstream tasks in ABSA. By leveraging the
annotated datasets in ABSA, we investigate both the attentions and the learned
representations of BERT pre-trained on reviews. We found that BERT uses very
few self-attention heads to encode context words (such as prepositions or
pronouns that indicating an aspect) and opinion words for an aspect. Most
features in the representation of an aspect are dedicated to the fine-grained
semantics of the domain (or product category) and the aspect itself, instead of
carrying summarized opinions from its context. We hope this investigation can
help future research in improving self-supervised learning, unsupervised
learning and fine-tuning for ABSA. The pre-trained model and code can be found
at https://github.com/howardhsu/BERT-for-RRC-ABSA.
| 2,020 |
Computation and Language
|
Personalized Multimodal Feedback Generation in Education
|
The automatic evaluation for school assignments is an important application
of AI in the education field. In this work, we focus on the task of
personalized multimodal feedback generation, which aims to generate
personalized feedback for various teachers to evaluate students' assignments
involving multimodal inputs such as images, audios, and texts. This task
involves the representation and fusion of multimodal information and natural
language generation, which presents the challenges from three aspects: 1) how
to encode and integrate multimodal inputs; 2) how to generate feedback specific
to each modality; and 3) how to realize personalized feedback generation. In
this paper, we propose a novel Personalized Multimodal Feedback Generation
Network (PMFGN) armed with a modality gate mechanism and a personalized bias
mechanism to address these challenges. The extensive experiments on real-world
K-12 education data show that our model significantly outperforms several
baselines by generating more accurate and diverse feedback. In addition,
detailed ablation experiments are conducted to deepen our understanding of the
proposed framework.
| 2,020 |
Computation and Language
|
Evaluating Bias In Dutch Word Embeddings
|
Recent research in Natural Language Processing has revealed that word
embeddings can encode social biases present in the training data which can
affect minorities in real world applications. This paper explores the gender
bias implicit in Dutch embeddings while investigating whether English language
based approaches can also be used in Dutch. We implement the Word Embeddings
Association Test (WEAT), Clustering and Sentence Embeddings Association Test
(SEAT) methods to quantify the gender bias in Dutch word embeddings, then we
proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods
and finally we evaluate the performance of the debiased embeddings in
downstream tasks. The results suggest that, among others, gender bias is
present in traditional and contextualized Dutch word embeddings. We highlight
how techniques used to measure and reduce bias created for English can be used
in Dutch embeddings by adequately translating the data and taking into account
the unique characteristics of the language. Furthermore, we analyze the effect
of the debiasing techniques on downstream tasks which show a negligible impact
on traditional embeddings and a 2% decrease in performance in contextualized
embeddings. Finally, we release the translated Dutch datasets to the public
along with the traditional embeddings with mitigated bias.
| 2,020 |
Computation and Language
|
Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution
|
Now that the performance of coreference resolvers on the simpler forms of
anaphoric reference has greatly improved, more attention is devoted to more
complex aspects of anaphora. One limitation of virtually all coreference
resolution models is the focus on single-antecedent anaphors. Plural anaphors
with multiple antecedents-so-called split-antecedent anaphors (as in John met
Mary. They went to the movies) have not been widely studied, because they are
not annotated in ONTONOTES and are relatively infrequent in other corpora. In
this paper, we introduce the first model for unrestricted resolution of
split-antecedent anaphors. We start with a strong baseline enhanced by BERT
embeddings, and show that we can substantially improve its performance by
addressing the sparsity issue. To do this, we experiment with auxiliary corpora
where split-antecedent anaphors were annotated by the crowd, and with transfer
learning models using element-of bridging references and single-antecedent
coreference as auxiliary tasks. Evaluation on the gold annotated ARRAU corpus
shows that the out best model uses a combination of three auxiliary corpora
achieved F1 scores of 70% and 43.6% when evaluated in a lenient and strict
setting, respectively, i.e., 11 and 21 percentage points gain when compared
with our baseline.
| 2,020 |
Computation and Language
|
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