Titles
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Do Natural Language Explanations Represent Valid Logical Arguments?
Verifying Entailment in Explainable NLI Gold Standards
|
An emerging line of research in Explainable NLP is the creation of datasets
enriched with human-annotated explanations and rationales, used to build and
evaluate models with step-wise inference and explanation generation
capabilities. While human-annotated explanations are used as ground-truth for
the inference, there is a lack of systematic assessment of their consistency
and rigour. In an attempt to provide a critical quality assessment of
Explanation Gold Standards (XGSs) for NLI, we propose a systematic annotation
methodology, named Explanation Entailment Verification (EEV), to quantify the
logical validity of human-annotated explanations. The application of EEV on
three mainstream datasets reveals the surprising conclusion that a majority of
the explanations, while appearing coherent on the surface, represent logically
invalid arguments, ranging from being incomplete to containing clearly
identifiable logical errors. This conclusion confirms that the inferential
properties of explanations are still poorly formalised and understood, and that
additional work on this line of research is necessary to improve the way
Explanation Gold Standards are constructed.
| 2,021 |
Computation and Language
|
Evaluation Of Word Embeddings From Large-Scale French Web Content
|
Distributed word representations are popularly used in many tasks in natural
language processing. Adding that pretrained word vectors on huge text corpus
achieved high performance in many different NLP tasks. This paper introduces
multiple high-quality word vectors for the French language where two of them
are trained on massive crawled French data during this study and the others are
trained on an already existing French corpus. We also evaluate the quality of
our proposed word vectors and the existing French word vectors on the French
word analogy task. In addition, we do the evaluation on multiple real NLP tasks
that shows the important performance enhancement of the pre-trained word
vectors compared to the existing and random ones. Finally, we created a demo
web application to test and visualize the obtained word embeddings. The
produced French word embeddings are available to the public, along with the
finetuning code on the NLU tasks and the demo code.
| 2,022 |
Computation and Language
|
Rare Disease Identification from Clinical Notes with Ontologies and Weak
Supervision
|
The identification of rare diseases from clinical notes with Natural Language
Processing (NLP) is challenging due to the few cases available for machine
learning and the need of data annotation from clinical experts. We propose a
method using ontologies and weak supervision. The approach includes two steps:
(i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language
System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak
supervision based on customised rules and Bidirectional Encoder Representations
from Transformers (BERT) based contextual representations, and (ii)
UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease
Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a
case study, we show that the Text-to-UMLS process can be greatly improved with
weak supervision, without any annotated data from domain experts. Our analysis
shows that the overall pipeline processing discharge summaries can surface rare
disease cases, which are mostly uncaptured in manual ICD codes of the hospital
admissions.
| 2,021 |
Computation and Language
|
ADAM: A Sandbox for Implementing Language Learning
|
We present ADAM, a software system for designing and running child language
learning experiments in Python. The system uses a virtual world to simulate a
grounded language acquisition process in which the language learner utilizes
cognitively plausible learning algorithms to form perceptual and linguistic
representations of the observed world. The modular nature of ADAM makes it easy
to design and test different language learning curricula as well as learning
algorithms. In this report, we describe the architecture of the ADAM system in
detail, and illustrate its components with examples. We provide our code.
| 2,021 |
Computation and Language
|
Genetic Algorithms For Extractive Summarization
|
Most current work in NLP utilizes deep learning, which requires a lot of
training data and computational power. This paper investigates the strengths of
Genetic Algorithms (GAs) for extractive summarization, as we hypothesized that
GAs could construct more efficient solutions for the summarization task due to
their relative customizability relative to deep learning models. This is done
by building a vocabulary set, the words of which are represented as an array of
weights, and optimizing those set of weights with the GA. These weights can be
used to build an overall weighting of a sentence, which can then be passed to
some threshold for extraction. Our results showed that the GA was able to learn
a weight representation that could filter out excessive vocabulary and thus
dictate sentence importance based on common English words.
| 2,022 |
Computation and Language
|
XeroAlign: Zero-Shot Cross-lingual Transformer Alignment
|
The introduction of pretrained cross-lingual language models brought decisive
improvements to multilingual NLP tasks. However, the lack of labelled task data
necessitates a variety of methods aiming to close the gap to high-resource
languages. Zero-shot methods in particular, often use translated task data as a
training signal to bridge the performance gap between the source and target
language(s). We introduce XeroAlign, a simple method for task-specific
alignment of cross-lingual pretrained transformers such as XLM-R. XeroAlign
uses translated task data to encourage the model to generate similar sentence
embeddings for different languages. The XeroAligned XLM-R, called XLM-RA, shows
strong improvements over the baseline models to achieve state-of-the-art
zero-shot results on three multilingual natural language understanding tasks.
XLM-RA's text classification accuracy exceeds that of XLM-R trained with
labelled data and performs on par with state-of-the-art models on a
cross-lingual adversarial paraphrasing task.
| 2,021 |
Computation and Language
|
Quantitative Evaluation of Alternative Translations in a Corpus of
Highly Dissimilar Finnish Paraphrases
|
In this paper, we present a quantitative evaluation of differences between
alternative translations in a large recently released Finnish paraphrase corpus
focusing in particular on non-trivial variation in translation. We combine a
series of automatic steps detecting systematic variation with manual analysis
to reveal regularities and identify categories of translation differences. We
find the paraphrase corpus to contain highly non-trivial translation variants
difficult to recognize through automatic approaches.
| 2,021 |
Computation and Language
|
A Unified Pre-training Framework for Conversational AI
|
In this work, we explore the application of PLATO-2 on various dialogue
systems, including open-domain conversation, knowledge grounded dialogue, and
task-oriented conversation. PLATO-2 is initially designed as an open-domain
chatbot, trained via two-stage curriculum learning. In the first stage, a
coarse-grained response generation model is learned to fit the simplified
one-to-one mapping relationship. This model is applied to the task-oriented
conversation, given that the semantic mappings tend to be deterministic in task
completion. In the second stage, another fine-grained generation model and an
evaluation model are further learned for diverse response generation and
coherence estimation, respectively. With superior capability on capturing
one-to-many mapping, such models are suitable for the open-domain conversation
and knowledge grounded dialogue. For the comprehensive evaluation of PLATO-2,
we have participated in multiple tasks of DSTC9, including interactive
evaluation of open-domain conversation (Track3-task2), static evaluation of
knowledge grounded dialogue (Track3-task1), and end-to-end task-oriented
conversation (Track2-task1). PLATO-2 has obtained the 1st place in all three
tasks, verifying its effectiveness as a unified framework for various dialogue
systems.
| 2,021 |
Computation and Language
|
Towards General Natural Language Understanding with Probabilistic
Worldbuilding
|
We introduce the Probabilistic Worldbuilding Model (PWM), a new
fully-symbolic Bayesian model of semantic parsing and reasoning, as a first
step in a research program toward more domain- and task-general NLU and AI.
Humans create internal mental models of their observations which greatly aid in
their ability to understand and reason about a large variety of problems. In
PWM, the meanings of sentences, acquired facts about the world, and
intermediate steps in reasoning are all expressed in a human-readable formal
language, with the design goal of interpretability. PWM is Bayesian, designed
specifically to be able to generalize to new domains and new tasks. We derive
and implement an inference algorithm that reads sentences by parsing and
abducing updates to its latent world model that capture the semantics of those
sentences, and evaluate it on two out-of-domain question-answering datasets:
(1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be
more representative of real language but still simple enough to focus on
evaluating reasoning ability, while being robust against heuristics. Our method
outperforms baselines on both, thereby demonstrating its value as a
proof-of-concept.
| 2,021 |
Computation and Language
|
Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms
Extractionwith Rich Syntactic Knowledge
|
In this paper, we propose to enhance the pair-wise aspect and opinion terms
extraction (PAOTE) task by incorporating rich syntactic knowledge. We first
build a syntax fusion encoder for encoding syntactic features, including a
label-aware graph convolutional network (LAGCN) for modeling the dependency
edges and labels, as well as the POS tags unifiedly, and a local-attention
module encoding POS tags for better term boundary detection. During pairing, we
then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term
pairing, in the meantime re-harnessing the syntax-enriched representations in
LAGCN for syntactic-aware scoring. Experimental results on four benchmark
datasets demonstrate that our model outperforms current state-of-the-art
baselines, meanwhile yielding explainable predictions with syntactic knowledge.
| 2,021 |
Computation and Language
|
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation
|
Keyphrases, that concisely summarize the high-level topics discussed in a
document, can be categorized into present keyphrase which explicitly appears in
the source text, and absent keyphrase which does not match any contiguous
subsequence but is highly semantically related to the source. Most existing
keyphrase generation approaches synchronously generate present and absent
keyphrases without explicitly distinguishing these two categories. In this
paper, a Select-Guide-Generate (SGG) approach is proposed to deal with present
and absent keyphrase generation separately with different mechanisms.
Specifically, SGG is a hierarchical neural network which consists of a
pointing-based selector at low layer concentrated on present keyphrase
generation, a selection-guided generator at high layer dedicated to absent
keyphrase generation, and a guider in the middle to transfer information from
selector to generator. Experimental results on four keyphrase generation
benchmarks demonstrate the effectiveness of our model, which significantly
outperforms the strong baselines for both present and absent keyphrases
generation. Furthermore, we extend SGG to a title generation task which
indicates its extensibility in natural language generation tasks.
| 2,021 |
Computation and Language
|
Assessing Dialogue Systems with Distribution Distances
|
An important aspect of developing dialogue systems is how to evaluate and
compare the performance of different systems. Existing automatic evaluation
metrics are based on turn-level quality evaluation and use average scores for
system-level comparison. In this paper, we propose to measure the performance
of a dialogue system by computing the distribution-wise distance between its
generated conversations and real-world conversations. Specifically, two
distribution-wise metrics, FBD and PRD, are developed and evaluated.
Experiments on several dialogue corpora show that our proposed metrics
correlate better with human judgments than existing metrics.
| 2,021 |
Computation and Language
|
TABBIE: Pretrained Representations of Tabular Data
|
Existing work on tabular representation learning jointly models tables and
associated text using self-supervised objective functions derived from
pretrained language models such as BERT. While this joint pretraining improves
tasks involving paired tables and text (e.g., answering questions about
tables), we show that it underperforms on tasks that operate over tables
without any associated text (e.g., populating missing cells). We devise a
simple pretraining objective (corrupt cell detection) that learns exclusively
from tabular data and reaches the state-of-the-art on a suite of table based
prediction tasks. Unlike competing approaches, our model (TABBIE) provides
embeddings of all table substructures (cells, rows, and columns), and it also
requires far less compute to train. A qualitative analysis of our model's
learned cell, column, and row representations shows that it understands complex
table semantics and numerical trends.
| 2,021 |
Computation and Language
|
GraphFormers: GNN-nested Transformers for Representation Learning on
Textual Graph
|
The representation learning on textual graph is to generate low-dimensional
embeddings for the nodes based on the individual textual features and the
neighbourhood information. Recent breakthroughs on pretrained language models
and graph neural networks push forward the development of corresponding
techniques. The existing works mainly rely on the cascaded model architecture:
the textual features of nodes are independently encoded by language models at
first; the textual embeddings are aggregated by graph neural networks
afterwards. However, the above architecture is limited due to the independent
modeling of textual features. In this work, we propose GraphFormers, where
layerwise GNN components are nested alongside the transformer blocks of
language models. With the proposed architecture, the text encoding and the
graph aggregation are fused into an iterative workflow, {making} each node's
semantic accurately comprehended from the global perspective. In addition, a
{progressive} learning strategy is introduced, where the model is successively
trained on manipulated data and original data to reinforce its capability of
integrating information on graph. Extensive evaluations are conducted on three
large-scale benchmark datasets, where GraphFormers outperform the SOTA
baselines with comparable running efficiency.
| 2,023 |
Computation and Language
|
Bird's Eye: Probing for Linguistic Graph Structures with a Simple
Information-Theoretic Approach
|
NLP has a rich history of representing our prior understanding of language in
the form of graphs. Recent work on analyzing contextualized text
representations has focused on hand-designed probe models to understand how and
to what extent do these representations encode a particular linguistic
phenomenon. However, due to the inter-dependence of various phenomena and
randomness of training probe models, detecting how these representations encode
the rich information in these linguistic graphs remains a challenging problem.
In this paper, we propose a new information-theoretic probe, Bird's Eye, which
is a fairly simple probe method for detecting if and how these representations
encode the information in these linguistic graphs. Instead of using classifier
performance, our probe takes an information-theoretic view of probing and
estimates the mutual information between the linguistic graph embedded in a
continuous space and the contextualized word representations. Furthermore, we
also propose an approach to use our probe to investigate localized linguistic
information in the linguistic graphs using perturbation analysis. We call this
probing setup Worm's Eye. Using these probes, we analyze BERT models on their
ability to encode a syntactic and a semantic graph structure, and find that
these models encode to some degree both syntactic as well as semantic
information; albeit syntactic information to a greater extent.
| 2,021 |
Computation and Language
|
Improving the Faithfulness of Attention-based Explanations with
Task-specific Information for Text Classification
|
Neural network architectures in natural language processing often use
attention mechanisms to produce probability distributions over input token
representations. Attention has empirically been demonstrated to improve
performance in various tasks, while its weights have been extensively used as
explanations for model predictions. Recent studies (Jain and Wallace, 2019;
Serrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot
generally be considered as a faithful explanation (Jacovi and Goldberg, 2020)
across encoders and tasks. In this paper, we seek to improve the faithfulness
of attention-based explanations for text classification. We achieve this by
proposing a new family of Task-Scaling (TaSc) mechanisms that learn
task-specific non-contextualised information to scale the original attention
weights. Evaluation tests for explanation faithfulness, show that the three
proposed variants of TaSc improve attention-based explanations across two
attention mechanisms, five encoders and five text classification datasets
without sacrificing predictive performance. Finally, we demonstrate that TaSc
consistently provides more faithful attention-based explanations compared to
three widely-used interpretability techniques.
| 2,021 |
Computation and Language
|
Learning to Perturb Word Embeddings for Out-of-distribution QA
|
QA models based on pretrained language mod-els have achieved remarkable
performance on various benchmark datasets.However, QA models do not generalize
well to unseen data that falls outside the training distribution, due to
distributional shifts.Data augmentation (DA) techniques which drop/replace
words have shown to be effective in regularizing the model from overfitting to
the training data.Yet, they may adversely affect the QA tasks since they incur
semantic changes that may lead to wrong answers for the QA task. To tackle this
problem, we propose a simple yet effective DA method based on a stochastic
noise generator, which learns to perturb the word embedding of the input
questions and context without changing their semantics. We validate the
performance of the QA models trained with our word embedding perturbation on a
single source dataset, on five different target domains.The results show that
our method significantly outperforms the baselineDA methods. Notably, the model
trained with ours outperforms the model trained with more than 240K
artificially generated QA pairs.
| 2,021 |
Computation and Language
|
What's in the Box? A Preliminary Analysis of Undesirable Content in the
Common Crawl Corpus
|
Whereas much of the success of the current generation of neural language
models has been driven by increasingly large training corpora, relatively
little research has been dedicated to analyzing these massive sources of
textual data. In this exploratory analysis, we delve deeper into the Common
Crawl, a colossal web corpus that is extensively used for training language
models. We find that it contains a significant amount of undesirable content,
including hate speech and sexually explicit content, even after filtering
procedures. We discuss the potential impacts of this content on language models
and conclude with future research directions and a more mindful approach to
corpus collection and analysis.
| 2,021 |
Computation and Language
|
Introducing Information Retrieval for Biomedical Informatics Students
|
Introducing biomedical informatics (BMI) students to natural language
processing (NLP) requires balancing technical depth with practical know-how to
address application-focused needs. We developed a set of three activities
introducing introductory BMI students to information retrieval with NLP,
covering document representation strategies and language models from TF-IDF to
BERT. These activities provide students with hands-on experience targeted
towards common use cases, and introduce fundamental components of NLP workflows
for a wide variety of applications.
| 2,021 |
Computation and Language
|
On the Ethical Limits of Natural Language Processing on Legal Text
|
Natural language processing (NLP) methods for analyzing legal text offer
legal scholars and practitioners a range of tools allowing to empirically
analyze law on a large scale. However, researchers seem to struggle when it
comes to identifying ethical limits to using NLP systems for acquiring genuine
insights both about the law and the systems' predictive capacity. In this paper
we set out a number of ways in which to think systematically about such issues.
We place emphasis on three crucial normative parameters which have, to the best
of our knowledge, been underestimated by current debates: (a) the importance of
academic freedom, (b) the existence of a wide diversity of legal and ethical
norms domestically but even more so internationally and (c) the threat of
moralism in research related to computational law. For each of these three
parameters we provide specific recommendations for the legal NLP community. Our
discussion is structured around the study of a real-life scenario that has
prompted recent debate in the legal NLP research community.
| 2,021 |
Computation and Language
|
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep
Text Classification
|
It is evident that deep text classification models trained on human data
could be biased. In particular, they produce biased outcomes for texts that
explicitly include identity terms of certain demographic groups. We refer to
this type of bias as explicit bias, which has been extensively studied.
However, deep text classification models can also produce biased outcomes for
texts written by authors of certain demographic groups. We refer to such bias
as implicit bias of which we still have a rather limited understanding. In this
paper, we first demonstrate that implicit bias exists in different text
classification tasks for different demographic groups. Then, we build a
learning-based interpretation method to deepen our knowledge of implicit bias.
Specifically, we verify that classifiers learn to make predictions based on
language features that are related to the demographic attributes of the
authors. Next, we propose a framework Debiased-TC to train deep text
classifiers to make predictions on the right features and consequently mitigate
implicit bias. We conduct extensive experiments on three real-world datasets.
The results show that the text classification models trained under our proposed
framework outperform traditional models significantly in terms of fairness, and
also slightly in terms of classification performance.
| 2,021 |
Computation and Language
|
Stylistic Analysis of the French Presidential Speeches: Is Macron really
different?
|
Presidential speeches indicate the government's intentions and justifications
supported by a dedicated style and rhetoric oscillating between explanation and
controversy. Over a period of sixty years, can we observe stylistic variations
by the different French presidents of the Fifth Republic (1958-2018)? Based on
official transcripts of all their allocution, this paper illustrates the
stylistic evolution and presents the underlying main trends. This study shows
that de Gaulle's rhetoric is not mainly dedicated to his own person, or that
the two terms of J. Chirac are not fully similar. According to several overall
stylistic indicators, Macron's style does not appear as complex compared to his
predecessors (F. Hollande or N. Sarkozy) but a more careful analysis clearly
demonstrates his noticeable new style. Compared to the recent US presidents,
the French ones present some similarities (e.g., similar mean sentence length)
and dissimilarities (more I-words, less we-words). In this comparative
analysis, Macron's style is also clearly distinctive from both the US and
former French presidents. Opting for a more abstract discourse, less anchored
in space, using less numbers, E. Macron tends to use long sentences. These
various stylistic and rhetorical features could explain his being misunderstood
by the French people and his recurrent low approval ratings.
| 2,021 |
Computation and Language
|
Adapting Monolingual Models: Data can be Scarce when Language Similarity
is High
|
For many (minority) languages, the resources needed to train large models are
not available. We investigate the performance of zero-shot transfer learning
with as little data as possible, and the influence of language similarity in
this process. We retrain the lexical layers of four BERT-based models using
data from two low-resource target language varieties, while the Transformer
layers are independently fine-tuned on a POS-tagging task in the model's source
language. By combining the new lexical layers and fine-tuned Transformer
layers, we achieve high task performance for both target languages. With high
language similarity, 10MB of data appears sufficient to achieve substantial
monolingual transfer performance. Monolingual BERT-based models generally
achieve higher downstream task performance after retraining the lexical layer
than multilingual BERT, even when the target language is included in the
multilingual model.
| 2,021 |
Computation and Language
|
Hone as You Read: A Practical Type of Interactive Summarization
|
We present HARE, a new task where reader feedback is used to optimize
document summaries for personal interest during the normal flow of reading.
This task is related to interactive summarization, where personalized summaries
are produced following a long feedback stage where users may read the same
sentences many times. However, this process severely interrupts the flow of
reading, making it impractical for leisurely reading. We propose to gather
minimally-invasive feedback during the reading process to adapt to user
interests and augment the document in real-time. Building off of recent
advances in unsupervised summarization evaluation, we propose a suitable metric
for this task and use it to evaluate a variety of approaches. Our approaches
range from simple heuristics to preference-learning and their analysis provides
insight into this important task. Human evaluation additionally supports the
practicality of HARE. The code to reproduce this work is available at
https://github.com/tannerbohn/HoneAsYouRead.
| 2,021 |
Computation and Language
|
On the logistical difficulties and findings of Jopara Sentiment Analysis
|
This paper addresses the problem of sentiment analysis for Jopara, a
code-switching language between Guarani and Spanish. We first collect a corpus
of Guarani-dominant tweets and discuss on the difficulties of finding quality
data for even relatively easy-to-annotate tasks, such as sentiment analysis.
Then, we train a set of neural models, including pre-trained language models,
and explore whether they perform better than traditional machine learning ones
in this low-resource setup. Transformer architectures obtain the best results,
despite not considering Guarani during pre-training, but traditional machine
learning models perform close due to the low-resource nature of the problem.
| 2,021 |
Computation and Language
|
Graph-based Multilingual Product Retrieval in E-commerce Search
|
Nowadays, with many e-commerce platforms conducting global business,
e-commerce search systems are required to handle product retrieval under
multilingual scenarios. Moreover, comparing with maintaining per-country
specific e-commerce search systems, having a universal system across countries
can further reduce the operational and computational costs, and facilitate
business expansion to new countries. In this paper, we introduce a universal
end-to-end multilingual retrieval system, and discuss our learnings and
technical details when training and deploying the system to serve billion-scale
product retrieval for e-commerce search. In particular, we propose a
multilingual graph attention based retrieval network by leveraging recent
advances in transformer-based multilingual language models and graph neural
network architectures to capture the interactions between search queries and
items in e-commerce search. Offline experiments on five countries data show
that our algorithm outperforms the state-of-the-art baselines by 35% recall and
25% mAP on average. Moreover, the proposed model shows significant increase of
conversion/revenue in online A/B experiments and has been deployed in
production for multiple countries.
| 2,021 |
Computation and Language
|
Do language models learn typicality judgments from text?
|
Building on research arguing for the possibility of conceptual and
categorical knowledge acquisition through statistics contained in language, we
evaluate predictive language models (LMs) -- informed solely by textual input
-- on a prevalent phenomenon in cognitive science: typicality. Inspired by
experiments that involve language processing and show robust typicality effects
in humans, we propose two tests for LMs. Our first test targets whether
typicality modulates LM probabilities in assigning taxonomic category
memberships to items. The second test investigates sensitivities to typicality
in LMs' probabilities when extending new information about items to their
categories. Both tests show modest -- but not completely absent --
correspondence between LMs and humans, suggesting that text-based exposure
alone is insufficient to acquire typicality knowledge.
| 2,021 |
Computation and Language
|
Efficient Weight factorization for Multilingual Speech Recognition
|
End-to-end multilingual speech recognition involves using a single model
training on a compositional speech corpus including many languages, resulting
in a single neural network to handle transcribing different languages. Due to
the fact that each language in the training data has different characteristics,
the shared network may struggle to optimize for all various languages
simultaneously. In this paper we propose a novel multilingual architecture that
targets the core operation in neural networks: linear transformation functions.
The key idea of the method is to assign fast weight matrices for each language
by decomposing each weight matrix into a shared component and a language
dependent component. The latter is then factorized into vectors using rank-1
assumptions to reduce the number of parameters per language. This efficient
factorization scheme is proved to be effective in two multilingual settings
with $7$ and $27$ languages, reducing the word error rates by $26\%$ and $27\%$
rel. for two popular architectures LSTM and Transformer, respectively.
| 2,021 |
Computation and Language
|
A Dataset of Information-Seeking Questions and Answers Anchored in
Research Papers
|
Readers of academic research papers often read with the goal of answering
specific questions. Question Answering systems that can answer those questions
can make consumption of the content much more efficient. However, building such
tools requires data that reflect the difficulty of the task arising from
complex reasoning about claims made in multiple parts of a paper. In contrast,
existing information-seeking question answering datasets usually contain
questions about generic factoid-type information. We therefore present QASPER,
a dataset of 5,049 questions over 1,585 Natural Language Processing papers.
Each question is written by an NLP practitioner who read only the title and
abstract of the corresponding paper, and the question seeks information present
in the full text. The questions are then answered by a separate set of NLP
practitioners who also provide supporting evidence to answers. We find that
existing models that do well on other QA tasks do not perform well on answering
these questions, underperforming humans by at least 27 F1 points when answering
them from entire papers, motivating further research in document-grounded,
information-seeking QA, which our dataset is designed to facilitate.
| 2,021 |
Computation and Language
|
DExperts: Decoding-Time Controlled Text Generation with Experts and
Anti-Experts
|
Despite recent advances in natural language generation, it remains
challenging to control attributes of generated text. We propose DExperts:
Decoding-time Experts, a decoding-time method for controlled text generation
that combines a pretrained language model with "expert" LMs and/or
"anti-expert" LMs in a product of experts. Intuitively, under the ensemble,
tokens only get high probability if they are considered likely by the experts,
and unlikely by the anti-experts. We apply DExperts to language detoxification
and sentiment-controlled generation, where we outperform existing controllable
generation methods on both automatic and human evaluations. Moreover, because
DExperts operates only on the output of the pretrained LM, it is effective with
(anti-)experts of smaller size, including when operating on GPT-3. Our work
highlights the promise of tuning small LMs on text with (un)desirable
attributes for efficient decoding-time steering.
| 2,021 |
Computation and Language
|
Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing
Regressions In NLP Model Updates
|
Behavior of deep neural networks can be inconsistent between different
versions. Regressions during model update are a common cause of concern that
often over-weigh the benefits in accuracy or efficiency gain. This work focuses
on quantifying, reducing and analyzing regression errors in the NLP model
updates. Using negative flip rate as regression measure, we show that
regression has a prevalent presence across tasks in the GLUE benchmark. We
formulate the regression-free model updates into a constrained optimization
problem, and further reduce it into a relaxed form which can be approximately
optimized through knowledge distillation training method. We empirically
analyze how model ensemble reduces regression. Finally, we conduct CheckList
behavioral testing to understand the distribution of regressions across
linguistic phenomena, and the efficacy of ensemble and distillation methods.
| 2,021 |
Computation and Language
|
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
|
There is a wide variety of speech processing tasks ranging from extracting
content information from speech signals to generating speech signals. For
different tasks, model networks are usually designed and tuned separately. If a
universal model can perform multiple speech processing tasks, some tasks might
be improved with the related abilities learned from other tasks. The multi-task
learning of a wide variety of speech processing tasks with a universal model
has not been studied. This paper proposes a universal modularized model,
SpeechNet, which treats all speech processing tasks into a speech/text input
and speech/text output format. We select five essential speech processing tasks
for multi-task learning experiments with SpeechNet. We show that SpeechNet
learns all of the above tasks, and we further analyze which tasks can be
improved by other tasks. SpeechNet is modularized and flexible for
incorporating more modules, tasks, or training approaches in the future. We
release the code and experimental settings to facilitate the research of
modularized universal models and multi-task learning of speech processing
tasks.
| 2,021 |
Computation and Language
|
A Survey of Data Augmentation Approaches for NLP
|
Data augmentation has recently seen increased interest in NLP due to more
work in low-resource domains, new tasks, and the popularity of large-scale
neural networks that require large amounts of training data. Despite this
recent upsurge, this area is still relatively underexplored, perhaps due to the
challenges posed by the discrete nature of language data. In this paper, we
present a comprehensive and unifying survey of data augmentation for NLP by
summarizing the literature in a structured manner. We first introduce and
motivate data augmentation for NLP, and then discuss major methodologically
representative approaches. Next, we highlight techniques that are used for
popular NLP applications and tasks. We conclude by outlining current challenges
and directions for future research. Overall, our paper aims to clarify the
landscape of existing literature in data augmentation for NLP and motivate
additional work in this area. We also present a GitHub repository with a paper
list that will be continuously updated at
https://github.com/styfeng/DataAug4NLP
| 2,021 |
Computation and Language
|
Learning Shared Semantic Space for Speech-to-Text Translation
|
Having numerous potential applications and great impact, end-to-end speech
translation (ST) has long been treated as an independent task, failing to fully
draw strength from the rapid advances of its sibling - text machine translation
(MT). With text and audio inputs represented differently, the modality gap has
rendered MT data and its end-to-end models incompatible with their ST
counterparts. In observation of this obstacle, we propose to bridge this
representation gap with Chimera. By projecting audio and text features to a
common semantic representation, Chimera unifies MT and ST tasks and boosts the
performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new
state-of-the-art. Specifically, Chimera obtains 27.1 BLEU on MuST-C EN-DE,
improving the SOTA by a +1.9 BLEU margin. Further experimental analyses
demonstrate that the shared semantic space indeed conveys common knowledge
between these two tasks and thus paves a new way for augmenting training
resources across modalities. Code, data, and resources are available at
https://github.com/Glaciohound/Chimera-ST.
| 2,021 |
Computation and Language
|
AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech
Detection Dataset
|
Along with the COVID-19 pandemic, an "infodemic" of false and misleading
information has emerged and has complicated the COVID-19 response efforts.
Social networking sites such as Facebook and Twitter have contributed largely
to the spread of rumors, conspiracy theories, hate, xenophobia, racism, and
prejudice. To combat the spread of fake news, researchers around the world have
and are still making considerable efforts to build and share COVID-19 related
research articles, models, and datasets. This paper releases "AraCOVID19-MFH" a
manually annotated multi-label Arabic COVID-19 fake news and hate speech
detection dataset. Our dataset contains 10,828 Arabic tweets annotated with 10
different labels. The labels have been designed to consider some aspects
relevant to the fact-checking task, such as the tweet's check worthiness,
positivity/negativity, and factuality. To confirm our annotated dataset's
practical utility, we used it to train and evaluate several classification
models and reported the obtained results. Though the dataset is mainly designed
for fake news detection, it can also be used for hate speech detection,
opinion/news classification, dialect identification, and many other tasks.
| 2,021 |
Computation and Language
|
CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as
Explicitations of Implicit Knowledge in Texts
|
In this work we leverage commonsense knowledge in form of knowledge paths to
establish connections between sentences, as a form of explicitation of implicit
knowledge. Such connections can be direct (singlehop paths) or require
intermediate concepts (multihop paths). To construct such paths we combine two
model types in a joint framework we call Co-nnect: a relation classifier that
predicts direct connections between concepts; and a target prediction model
that generates target or intermediate concepts given a source concept and a
relation, which we use to construct multihop paths. Unlike prior work that
relies exclusively on static knowledge sources, we leverage language models
finetuned on knowledge stored in ConceptNet, to dynamically generate knowledge
paths, as explanations of implicit knowledge that connects sentences in texts.
As a central contribution we design manual and automatic evaluation settings
for assessing the quality of the generated paths. We conduct evaluations on two
argumentative datasets and show that a combination of the two model types
generates meaningful, high-quality knowledge paths between sentences that
reveal implicit knowledge conveyed in text.
| 2,021 |
Computation and Language
|
Identity Signals in Emoji Do not Influence Perception of Factual Truth
on Twitter
|
Prior work has shown that Twitter users use skin-toned emoji as an act of
self-representation to express their racial/ethnic identity. We test whether
this signal of identity can influence readers' perceptions about the content of
a post containing that signal. In a large scale (n=944) pre-registered
controlled experiment, we manipulate the presence of skin-toned emoji and
profile photos in a task where readers rate obscure trivia facts (presented as
tweets) as true or false. Using a Bayesian statistical analysis, we find that
neither emoji nor profile photo has an effect on how readers rate these facts.
This result will be of some comfort to anyone concerned about the manipulation
of online users through the crafting of fake profiles.
| 2,021 |
Computation and Language
|
The Shadowy Lives of Emojis: An Analysis of a Hacktivist Collective's
Use of Emojis on Twitter
|
Emojis have established themselves as a popular means of communication in
online messaging. Despite the apparent ubiquity in these image-based tokens,
however, interpretation and ambiguity may allow for unique uses of emojis to
appear. In this paper, we present the first examination of emoji usage by
hacktivist groups via a study of the Anonymous collective on Twitter. This
research aims to identify whether Anonymous affiliates have evolved their own
approach to using emojis. To do this, we compare a large dataset of Anonymous
tweets to a baseline tweet dataset from randomly sampled Twitter users using
computational and qualitative analysis to compare their emoji usage. We utilise
Word2Vec language models to examine the semantic relationships between emojis,
identifying clear distinctions in the emoji-emoji relationships of Anonymous
users. We then explore how emojis are used as a means of conveying emotions,
finding that despite little commonality in emoji-emoji semantic ties, Anonymous
emoji usage displays similar patterns of emotional purpose to the emojis of
baseline Twitter users. Finally, we explore the textual context in which these
emojis occur, finding that although similarities exist between the emoji usage
of our Anonymous and baseline Twitter datasets, Anonymous users appear to have
adopted more specific interpretations of certain emojis. This includes the use
of emojis as a means of expressing adoration and infatuation towards notable
Anonymous affiliates. These findings indicate that emojis appear to retain a
considerable degree of similarity within Anonymous accounts as compared to more
typical Twitter users. However, their are signs that emoji usage in Anonymous
accounts has evolved somewhat, gaining additional group-specific associations
that reveal new insights into the behaviours of this unusual collective.
| 2,021 |
Computation and Language
|
A Grounded Approach to Modeling Generic Knowledge Acquisition
|
We introduce and implement a cognitively plausible model for learning from
generic language, statements that express generalizations about members of a
category and are an important aspect of concept development in language
acquisition (Carlson & Pelletier, 1995; Gelman, 2009). We extend a
computational framework designed to model grounded language acquisition by
introducing the concept network. This new layer of abstraction enables the
system to encode knowledge learned from generic statements and represent the
associations between concepts learned by the system. Through three tasks that
utilize the concept network, we demonstrate that our extensions to ADAM can
acquire generic information and provide an example of how ADAM can be used to
model language acquisition.
| 2,021 |
Computation and Language
|
VAULT: VAriable Unified Long Text Representation for Machine Reading
Comprehension
|
Existing models on Machine Reading Comprehension (MRC) require complex model
architecture for effectively modeling long texts with paragraph representation
and classification, thereby making inference computationally inefficient for
production use. In this work, we propose VAULT: a light-weight and
parallel-efficient paragraph representation for MRC based on contextualized
representation from long document input, trained using a new Gaussian
distribution-based objective that pays close attention to the partially correct
instances that are close to the ground-truth. We validate our VAULT
architecture showing experimental results on two benchmark MRC datasets that
require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and
the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ
with a state-of-the-art (SOTA) complex document modeling approach while being
16 times faster, demonstrating the efficiency of our proposed model. We also
demonstrate that our model can also be effectively adapted to a completely
different domain -- TechQA -- with large improvement over a model fine-tuned on
a previously published large PLM.
| 2,021 |
Computation and Language
|
A Multi-Size Neural Network with Attention Mechanism for Answer
Selection
|
Semantic matching is of central significance to the answer selection task
which aims to select correct answers for a given question from a candidate
answer pool. A useful method is to employ neural networks with attention to
generate sentences representations in a way that information from pair
sentences can mutually influence the computation of representations. In this
work, an effective architecture,multi-size neural network with attention
mechanism (AM-MSNN),is introduced into the answer selection task. This
architecture captures more levels of language granularities in parallel,
because of the various sizes of filters comparing with single-layer CNN and
multi-layer CNNs. Meanwhile it extends the sentence representations by
attention mechanism, thus containing more information for different types of
questions. The empirical study on three various benchmark tasks of answer
selection demonstrates the efficacy of the proposed model in all the benchmarks
and its superiority over competitors. The experimental results show that (1)
multi-size neural network (MSNN) is a more useful method to capture abstract
features on different levels of granularities than single/multi-layer CNNs; (2)
the attention mechanism (AM) is a better strategy to derive more informative
representations; (3) AM-MSNN is a better architecture for the answer selection
task for the moment.
| 2,021 |
Computation and Language
|
Generating abstractive summaries of Lithuanian news articles using a
transformer model
|
In this work, we train the first monolingual Lithuanian transformer model on
a relatively large corpus of Lithuanian news articles and compare various
output decoding algorithms for abstractive news summarization. We achieve an
average ROUGE-2 score 0.163, generated summaries are coherent and look
impressive at first glance. However, some of them contain misleading
information that is not so easy to spot. We describe all the technical details
and share our trained model and accompanying code in an online open-source
repository, as well as some characteristic samples of the generated summaries.
| 2,021 |
Computation and Language
|
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of
Idioms
|
We present a fairly large, Potential Idiomatic Expression (PIE) dataset for
Natural Language Processing (NLP) in English. The challenges with NLP systems
with regards to tasks such as Machine Translation (MT), word sense
disambiguation (WSD) and information retrieval make it imperative to have a
labelled idioms dataset with classes such as it is in this work. To the best of
the authors' knowledge, this is the first idioms corpus with classes of idioms
beyond the literal and the general idioms classification. In particular, the
following classes are labelled in the dataset: metaphor, simile, euphemism,
parallelism, personification, oxymoron, paradox, hyperbole, irony and literal.
We obtain an overall inter-annotator agreement (IAA) score, between two
independent annotators, of 88.89%. Many past efforts have been limited in the
corpus size and classes of samples but this dataset contains over 20,100
samples with almost 1,200 cases of idioms (with their meanings) from 10 classes
(or senses). The corpus may also be extended by researchers to meet specific
needs. The corpus has part of speech (PoS) tagging from the NLTK library.
Classification experiments performed on the corpus to obtain a baseline and
comparison among three common models, including the BERT model, give good
results. We also make publicly available the corpus and the relevant codes for
working with it for NLP tasks.
| 2,022 |
Computation and Language
|
Translation Quality Assessment: A Brief Survey on Manual and Automatic
Methods
|
To facilitate effective translation modeling and translation studies, one of
the crucial questions to address is how to assess translation quality. From the
perspectives of accuracy, reliability, repeatability and cost, translation
quality assessment (TQA) itself is a rich and challenging task. In this work,
we present a high-level and concise survey of TQA methods, including both
manual judgement criteria and automated evaluation metrics, which we classify
into further detailed sub-categories. We hope that this work will be an asset
for both translation model researchers and quality assessment researchers. In
addition, we hope that it will enable practitioners to quickly develop a better
understanding of the conventional TQA field, and to find corresponding closely
relevant evaluation solutions for their own needs. This work may also serve
inspire further development of quality assessment and evaluation methodologies
for other natural language processing (NLP) tasks in addition to machine
translation (MT), such as automatic text summarization (ATS), natural language
understanding (NLU) and natural language generation (NLG).
| 2,021 |
Computation and Language
|
Looking for COVID-19 misinformation in multilingual social media texts
|
This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for
detecting and observing the spread of misinformation about this disease within
texts. CMTA proposes a data science (DS) pipeline that applies machine learning
models for processing, classifying (Dense-CNN) and analyzing (MBERT)
multilingual (micro)-texts. DS pipeline data preparation tasks extract features
from multilingual textual data and categorize it into specific information
classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has
been experimented with multilingual micro-texts (tweets), showing
misinformation spread across different languages. To assess the performance of
CMTA and put it in perspective, we performed a comparative analysis of CMTA
with eight monolingual models used for detecting misinformation. The comparison
shows that CMTA has surpassed various monolingual models and suggests that it
can be used as a general method for detecting misinformation in multilingual
micro-texts. CMTA experimental results show misinformation trends about
COVID-19 in different languages during the first pandemic months.
| 2,021 |
Computation and Language
|
Recognition and Processing of NATOM
|
In this paper we show how to process the NOTAM (Notice to Airmen) data of the
field in civil aviation. The main research contents are as follows: 1.Data
preprocessing: For the original data of the NOTAM, there is a mixture of
Chinese and English, and the structure is poor. The original data is cleaned,
the Chinese data and the English data are processed separately, word
segmentation is completed, and stopping-words are removed. Using Glove word
vector methods to represent the data for using a custom mapping vocabulary.
2.Decoupling features and classifiers: In order to improve the ability of the
text classification model to recognize minority samples, the overall model
training process is decoupled from the perspective of the algorithm as a whole,
divided into two stages of feature learning and classifier learning. The
weights of the feature learning stage and the classifier learning stage adopt
different strategies to overcome the influence of the head data and tail data
of the imbalanced data set on the classification model. Experiments have proved
that the use of decoupling features and classifier methods based on the neural
network classification model can complete text multi-classification tasks in
the field of civil aviation, and at the same time can improve the recognition
accuracy of the minority samples in the data set.
| 2,021 |
Computation and Language
|
Learning Models for Suicide Prediction from Social Media Posts
|
We propose a deep learning architecture and test three other machine learning
models to automatically detect individuals that will attempt suicide within (1)
30 days and (2) six months, using their social media post data provided in the
CLPsych 2021 shared task. Additionally, we create and extract three sets of
handcrafted features for suicide risk detection based on the three-stage theory
of suicide and prior work on emotions and the use of pronouns among persons
exhibiting suicidal ideations. Extensive experimentations show that some of the
traditional machine learning methods outperform the baseline with an F1 score
of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30
days prior). However, the proposed deep learning method outperforms the
baseline with F1 score of 0.737 and F2 score of 0.843 on subtask 2 (prediction
of suicide 6 months prior).
| 2,021 |
Computation and Language
|
Multi-Task Learning of Query Intent and Named Entities using Transfer
Learning
|
Named entity recognition (NER) has been studied extensively and the earlier
algorithms were based on sequence labeling like Hidden Markov Models (HMM) and
conditional random fields (CRF). These were followed by neural network based
deep learning models. Recently, BERT has shown new state of the art accuracy in
sequence labeling tasks like NER. In this short article, we study various
approaches to task specific NER. Task specific NER has two components -
identifying the intent of a piece of text (like search queries), and then
labeling the query with task specific named entities. For example, we consider
the task of labeling Target store locations in a search query (which could be
entered in a search box or spoken in a device like Alexa or Google Home). Store
locations are highly ambiguous and sometimes it is difficult to differentiate
between say a location and a non-location. For example, "pickup my order at
orange store" has "orange" as the store location, while "buy orange at target"
has "orange" as a fruit. We explore this difficulty by doing multi-task
learning which we call global to local transfer of information. We jointly
learn the query intent (i.e. store lookup) and the named entities by using
multiple loss functions in our BERT based model and find interesting results.
| 2,021 |
Computation and Language
|
Are Pre-trained Convolutions Better than Pre-trained Transformers?
|
In the era of pre-trained language models, Transformers are the de facto
choice of model architectures. While recent research has shown promise in
entirely convolutional, or CNN, architectures, they have not been explored
using the pre-train-fine-tune paradigm. In the context of language models, are
convolutional models competitive to Transformers when pre-trained? This paper
investigates this research question and presents several interesting findings.
Across an extensive set of experiments on 8 datasets/tasks, we find that
CNN-based pre-trained models are competitive and outperform their Transformer
counterpart in certain scenarios, albeit with caveats. Overall, the findings
outlined in this paper suggest that conflating pre-training and architectural
advances is misguided and that both advances should be considered
independently. We believe our research paves the way for a healthy amount of
optimism in alternative architectures.
| 2,022 |
Computation and Language
|
A Benchmarking on Cloud based Speech-To-Text Services for French Speech
and Background Noise Effect
|
This study presents a large scale benchmarking on cloud based Speech-To-Text
systems: {Google Cloud Speech-To-Text}, {Microsoft Azure Cognitive Services},
{Amazon Transcribe}, {IBM Watson Speech to Text}. For each systems, 40158 clean
and noisy speech files about 101 hours are tested. Effect of background noise
on STT quality is also evaluated with 5 different Signal-to-noise ratios from
40dB to 0dB. Results showed that {Microsoft Azure} provided lowest
transcription error rate $9.09\%$ on clean speech, with high robustness to
noisy environment. {Google Cloud} and {Amazon Transcribe} gave similar
performance, but the latter is very limited for time-constraint usage. Though
{IBM Watson} could work correctly in quiet conditions, it is highly sensible to
noisy speech which could strongly limit its application in real life
situations.
| 2,021 |
Computation and Language
|
Diff-Explainer: Differentiable Convex Optimization for Explainable
Multi-hop Inference
|
This paper presents Diff-Explainer, the first hybrid framework for
explainable multi-hop inference that integrates explicit constraints with
neural architectures through differentiable convex optimization. Specifically,
Diff-Explainer allows for the fine-tuning of neural representations within a
constrained optimization framework to answer and explain multi-hop questions in
natural language. To demonstrate the efficacy of the hybrid framework, we
combine existing ILP-based solvers for multi-hop Question Answering (QA) with
Transformer-based representations. An extensive empirical evaluation on
scientific and commonsense QA tasks demonstrates that the integration of
explicit constraints in an end-to-end differentiable framework can
significantly improve the performance of non-differentiable ILP solvers (8.91%
- 13.3%). Moreover, additional analysis reveals that Diff-Explainer is able to
achieve strong performance when compared to standalone Transformers and
previous multi-hop approaches while still providing structured explanations in
support of its predictions.
| 2,022 |
Computation and Language
|
Generalising Multilingual Concept-to-Text NLG with Language Agnostic
Delexicalisation
|
Concept-to-text Natural Language Generation is the task of expressing an
input meaning representation in natural language. Previous approaches in this
task have been able to generalise to rare or unseen instances by relying on a
delexicalisation of the input. However, this often requires that the input
appears verbatim in the output text. This poses challenges in multilingual
settings, where the task expands to generate the output text in multiple
languages given the same input. In this paper, we explore the application of
multilingual models in concept-to-text and propose Language Agnostic
Delexicalisation, a novel delexicalisation method that uses multilingual
pretrained embeddings, and employs a character-level post-editing model to
inflect words in their correct form during relexicalisation. Our experiments
across five datasets and five languages show that multilingual models
outperform monolingual models in concept-to-text and that our framework
outperforms previous approaches, especially for low resource languages.
| 2,021 |
Computation and Language
|
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
|
Sequence-to-sequence learning naturally has two directions. How to
effectively utilize supervision signals from both directions? Existing
approaches either require two separate models, or a multitask-learned model but
with inferior performance. In this paper, we propose REDER (Reversible Duplex
Transformer), a parameter-efficient model and apply it to machine translation.
Either end of REDER can simultaneously input and output a distinct language.
Thus REDER enables reversible machine translation by simply flipping the input
and output ends. Experiments verify that REDER achieves the first success of
reversible machine translation, which helps outperform its multitask-trained
baselines by up to 1.3 BLEU.
| 2,022 |
Computation and Language
|
Measuring and Increasing Context Usage in Context-Aware Machine
Translation
|
Recent work in neural machine translation has demonstrated both the necessity
and feasibility of using inter-sentential context -- context from sentences
other than those currently being translated. However, while many current
methods present model architectures that theoretically can use this extra
context, it is often not clear how much they do actually utilize it at
translation time. In this paper, we introduce a new metric, conditional
cross-mutual information, to quantify the usage of context by these models.
Using this metric, we measure how much document-level machine translation
systems use particular varieties of context. We find that target context is
referenced more than source context, and that conditioning on a longer context
has a diminishing effect on results. We then introduce a new, simple training
method, context-aware word dropout, to increase the usage of context by
context-aware models. Experiments show that our method increases context usage
and that this reflects on the translation quality according to metrics such as
BLEU and COMET, as well as performance on anaphoric pronoun resolution and
lexical cohesion contrastive datasets.
| 2,021 |
Computation and Language
|
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP:
The Role of Sample Size and Dimensionality
|
In human-level NLP tasks, such as predicting mental health, personality, or
demographics, the number of observations is often smaller than the standard
768+ hidden state sizes of each layer within modern transformer-based language
models, limiting the ability to effectively leverage transformers. Here, we
provide a systematic study on the role of dimension reduction methods
(principal components analysis, factorization techniques, or multi-layer
auto-encoders) as well as the dimensionality of embedding vectors and sample
sizes as a function of predictive performance. We first find that fine-tuning
large models with a limited amount of data pose a significant difficulty which
can be overcome with a pre-trained dimension reduction regime. RoBERTa
consistently achieves top performance in human-level tasks, with PCA giving
benefit over other reduction methods in better handling users that write longer
texts. Finally, we observe that a majority of the tasks achieve results
comparable to the best performance with just $\frac{1}{12}$ of the embedding
dimensions.
| 2,023 |
Computation and Language
|
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction
from Language Models
|
Coherent discourse is distinguished from a mere collection of utterances by
the satisfaction of a diverse set of constraints, for example choice of
expression, logical relation between denoted events, and implicit compatibility
with world-knowledge. Do neural language models encode such constraints? We
design an extendable set of test suites addressing different aspects of
discourse and dialogue coherence. Unlike most previous coherence evaluation
studies, we address specific linguistic devices beyond sentence order
perturbations, allowing for a more fine-grained analysis of what constitutes
coherence and what neural models trained on a language modelling objective do
encode. Extending the targeted evaluation paradigm for neural language models
(Marvin and Linzen, 2018) to phenomena beyond syntax, we show that this
paradigm is equally suited to evaluate linguistic qualities that contribute to
the notion of coherence.
| 2,021 |
Computation and Language
|
Unsupervised Cross-Domain Prerequisite Chain Learning using Variational
Graph Autoencoders
|
Learning prerequisite chains is an essential task for efficiently acquiring
knowledge in both known and unknown domains. For example, one may be an expert
in the natural language processing (NLP) domain but want to determine the best
order to learn new concepts in an unfamiliar Computer Vision domain (CV). Both
domains share some common concepts, such as machine learning basics and deep
learning models. In this paper, we propose unsupervised cross-domain concept
prerequisite chain learning using an optimized variational graph autoencoder.
Our model learns to transfer concept prerequisite relations from an
information-rich domain (source domain) to an information-poor domain (target
domain), substantially surpassing other baseline models. Also, we expand an
existing dataset by introducing two new domains: CV and Bioinformatics (BIO).
The annotated data and resources, as well as the code, will be made publicly
available.
| 2,021 |
Computation and Language
|
Understanding by Understanding Not: Modeling Negation in Language Models
|
Negation is a core construction in natural language. Despite being very
successful on many tasks, state-of-the-art pre-trained language models often
handle negation incorrectly. To improve language models in this regard, we
propose to augment the language modeling objective with an unlikelihood
objective that is based on negated generic sentences from a raw text corpus. By
training BERT with the resulting combined objective we reduce the mean top~1
error rate to 4% on the negated LAMA dataset. We also see some improvements on
the negated NLI benchmarks.
| 2,021 |
Computation and Language
|
Comprehensive Study: How the Context Information of Different
Granularity Affects Dialogue State Tracking?
|
Dialogue state tracking (DST) plays a key role in task-oriented dialogue
systems to monitor the user's goal. In general, there are two strategies to
track a dialogue state: predicting it from scratch and updating it from
previous state. The scratch-based strategy obtains each slot value by inquiring
all the dialogue history, and the previous-based strategy relies on the current
turn dialogue to update the previous dialogue state. However, it is hard for
the scratch-based strategy to correctly track short-dependency dialogue state
because of noise; meanwhile, the previous-based strategy is not very useful for
long-dependency dialogue state tracking. Obviously, it plays different roles
for the context information of different granularity to track different kinds
of dialogue states. Thus, in this paper, we will study and discuss how the
context information of different granularity affects dialogue state tracking.
First, we explore how greatly different granularities affect dialogue state
tracking. Then, we further discuss how to combine multiple granularities for
dialogue state tracking. Finally, we apply the findings about context
granularity to few-shot learning scenario. Besides, we have publicly released
all codes.
| 2,021 |
Computation and Language
|
Improving Cross-Lingual Reading Comprehension with Self-Training
|
Substantial improvements have been made in machine reading comprehension,
where the machine answers questions based on a given context. Current
state-of-the-art models even surpass human performance on several benchmarks.
However, their abilities in the cross-lingual scenario are still to be
explored. Previous works have revealed the abilities of pre-trained
multilingual models for zero-shot cross-lingual reading comprehension. In this
paper, we further utilized unlabeled data to improve the performance. The model
is first supervised-trained on source language corpus, and then self-trained
with unlabeled target language data. The experiment results showed improvements
for all languages, and we also analyzed how self-training benefits
cross-lingual reading comprehension in qualitative aspects.
| 2,021 |
Computation and Language
|
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax
and Sampling
|
Neural text generation models are likely to suffer from the low-diversity
problem. Various decoding strategies and training-based methods have been
proposed to promote diversity only by exploiting contextual features, but
rarely do they consider incorporating syntactic structure clues. In this work,
we propose using linguistic annotation, i.e., part-of-speech (POS), to guide
the text generation. In detail, we introduce POS Guided Softmax to explicitly
model two posterior probabilities: (i) next-POS, and (ii) next-token from the
vocabulary of the target POS. A POS Guided Sampling strategy is further
proposed to address the low-diversity problem by enriching the diversity of
POS. Extensive experiments and human evaluations show that, compared with
existing state-of-the-art methods, our POS Guided Softmax and Sampling (POSG)
can generate more diverse text while maintaining comparable quality.
| 2,022 |
Computation and Language
|
Improving Named Entity Recognition by External Context Retrieving and
Cooperative Learning
|
Recent advances in Named Entity Recognition (NER) show that document-level
contexts can significantly improve model performance. In many application
scenarios, however, such contexts are not available. In this paper, we propose
to find external contexts of a sentence by retrieving and selecting a set of
semantically relevant texts through a search engine, with the original sentence
as the query. We find empirically that the contextual representations computed
on the retrieval-based input view, constructed through the concatenation of a
sentence and its external contexts, can achieve significantly improved
performance compared to the original input view based only on the sentence.
Furthermore, we can improve the model performance of both input views by
Cooperative Learning, a training method that encourages the two input views to
produce similar contextual representations or output label distributions.
Experiments show that our approach can achieve new state-of-the-art performance
on 8 NER data sets across 5 domains.
| 2,022 |
Computation and Language
|
Logic-Driven Context Extension and Data Augmentation for Logical
Reasoning of Text
|
Logical reasoning of text requires understanding critical logical information
in the text and performing inference over them. Large-scale pre-trained models
for logical reasoning mainly focus on word-level semantics of text while
struggling to capture symbolic logic. In this paper, we propose to understand
logical symbols and expressions in the text to arrive at the answer. Based on
such logical information, we not only put forward a context extension framework
but also propose a data augmentation algorithm. The former extends the context
to cover implicit logical expressions following logical equivalence laws. The
latter augments literally similar but logically different instances to better
capture logical information, especially logical negative and conditional
relationships. We conduct experiments on ReClor dataset. The results show that
our method achieves the state-of-the-art performance, and both logic-driven
context extension framework and data augmentation algorithm can help improve
the accuracy. And our multi-model ensemble system is the first to surpass human
performance on both EASY set and HARD set of ReClor.
| 2,021 |
Computation and Language
|
D2S: Document-to-Slide Generation Via Query-Based Text Summarization
|
Presentations are critical for communication in all areas of our lives, yet
the creation of slide decks is often tedious and time-consuming. There has been
limited research aiming to automate the document-to-slides generation process
and all face a critical challenge: no publicly available dataset for training
and benchmarking. In this work, we first contribute a new dataset, SciDuet,
consisting of pairs of papers and their corresponding slides decks from recent
years' NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel
system that tackles the document-to-slides task with a two-step approach: 1)
Use slide titles to retrieve relevant and engaging text, figures, and tables;
2) Summarize the retrieved context into bullet points with long-form question
answering. Our evaluation suggests that long-form QA outperforms
state-of-the-art summarization baselines on both automated ROUGE metrics and
qualitative human evaluation.
| 2,021 |
Computation and Language
|
Falling Through the Gaps: Neural Architectures as Models of
Morphological Rule Learning
|
Recent advances in neural architectures have revived the problem of
morphological rule learning. We evaluate the Transformer as a model of
morphological rule learning and compare it with Recurrent Neural Networks (RNN)
on English, German, and Russian. We bring to the fore a hitherto overlooked
problem, the morphological gaps, where the expected inflection of a word is
missing. For example, 63 Russian verbs lack a first-person-singular present
form such that one cannot comfortably say "*o\v{s}\v{c}u\v{s}\v{c}u" ("I
feel"). Even English has gaps, such as the past participle of "stride": the
function of morphological inflection can be partial. Both neural architectures
produce inflections that ought to be missing. Analyses reveal that Transformers
recapitulate the statistical distribution of inflections in the training data,
similar to RNNs. Models' success on English and German is driven by the fact
that rules in these languages can be identified with the majority forms, which
is not universal.
| 2,021 |
Computation and Language
|
Continuous representations of intents for dialogue systems
|
Intent modelling has become an important part of modern dialogue systems.
With the rapid expansion of practical dialogue systems and virtual assistants,
such as Amazon Alexa, Apple Siri, and Google Assistant, the interest has only
increased. However, up until recently the focus has been on detecting a fixed,
discrete, number of seen intents. Recent years have seen some work done on
unseen intent detection in the context of zero-shot learning. This paper
continues the prior work by proposing a novel model where intents are
continuous points placed in a specialist Intent Space that yields several
advantages. First, the continuous representation enables to investigate
relationships between the seen intents. Second, it allows any unseen intent to
be reliably represented given limited quantities of data. Finally, this paper
will show how the proposed model can be augmented with unseen intents without
retraining any of the seen ones. Experiments show that the model can reliably
add unseen intents with a high accuracy while retaining a high performance on
the seen intents.
| 2,021 |
Computation and Language
|
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
|
Recently, few certified defense methods have been developed to provably
guarantee the robustness of a text classifier to adversarial synonym
substitutions. However, all existing certified defense methods assume that the
defenders are informed of how the adversaries generate synonyms, which is not a
realistic scenario. In this paper, we propose a certifiably robust defense
method by randomly masking a certain proportion of the words in an input text,
in which the above unrealistic assumption is no longer necessary. The proposed
method can defend against not only word substitution-based attacks, but also
character-level perturbations. We can certify the classifications of over 50%
texts to be robust to any perturbation of 5 words on AGNEWS, and 2 words on
SST2 dataset. The experimental results show that our randomized smoothing
method significantly outperforms recently proposed defense methods across
multiple datasets.
| 2,021 |
Computation and Language
|
NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using
the Long Document Transformer
|
This paper presents a technical report of our submission to the 4th task of
SemEval-2021, titled: Reading Comprehension of Abstract Meaning. In this task,
we want to predict the correct answer based on a question given a context.
Usually, contexts are very lengthy and require a large receptive field from the
model. Thus, common contextualized language models like BERT miss fine
representation and performance due to the limited capacity of the input tokens.
To tackle this problem, we used the Longformer model to better process the
sequences. Furthermore, we utilized the method proposed in the Longformer
benchmark on Wikihop dataset which improved the accuracy on our task data from
23.01% and 22.95% achieved by the baselines for subtask 1 and 2, respectively,
to 70.30% and 64.38%.
| 2,021 |
Computation and Language
|
Enhancing Transformers with Gradient Boosted Decision Trees for NLI
Fine-Tuning
|
Transfer learning has become the dominant paradigm for many natural language
processing tasks. In addition to models being pretrained on large datasets,
they can be further trained on intermediate (supervised) tasks that are similar
to the target task. For small Natural Language Inference (NLI) datasets,
language modelling is typically followed by pretraining on a large (labelled)
NLI dataset before fine-tuning with each NLI subtask. In this work, we explore
Gradient Boosted Decision Trees (GBDTs) as an alternative to the commonly used
Multi-Layer Perceptron (MLP) classification head. GBDTs have desirable
properties such as good performance on dense, numerical features and are
effective where the ratio of the number of samples w.r.t the number of features
is low. We then introduce FreeGBDT, a method of fitting a GBDT head on the
features computed during fine-tuning to increase performance without additional
computation by the neural network. We demonstrate the effectiveness of our
method on several NLI datasets using a strong baseline model (RoBERTa-large
with MNLI pretraining). The FreeGBDT shows a consistent improvement over the
MLP classification head.
| 2,022 |
Computation and Language
|
Long-Span Summarization via Local Attention and Content Selection
|
Transformer-based models have achieved state-of-the-art results in a wide
range of natural language processing (NLP) tasks including document
summarization. Typically these systems are trained by fine-tuning a large
pre-trained model to the target task. One issue with these transformer-based
models is that they do not scale well in terms of memory and compute
requirements as the input length grows. Thus, for long document summarization,
it can be challenging to train or fine-tune these models. In this work, we
exploit large pre-trained transformer-based models and address long-span
dependencies in abstractive summarization using two methods: local
self-attention; and explicit content selection. These approaches are compared
on a range of network configurations. Experiments are carried out on standard
long-span summarization tasks, including Spotify Podcast, arXiv, and PubMed
datasets. We demonstrate that by combining these methods, we can achieve
state-of-the-art results on all three tasks in the ROUGE scores. Moreover,
without a large-scale GPU card, our approach can achieve comparable or better
results than existing approaches.
| 2,021 |
Computation and Language
|
Knowledge-based Review Generation by Coherence Enhanced Text Planning
|
As a natural language generation task, it is challenging to generate
informative and coherent review text. In order to enhance the informativeness
of the generated text, existing solutions typically learn to copy entities or
triples from knowledge graphs (KGs). However, they lack overall consideration
to select and arrange the incorporated knowledge, which tends to cause text
incoherence.
To address the above issue, we focus on improving entity-centric coherence of
the generated reviews by leveraging the semantic structure of KGs. In this
paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based
on knowledge graphs (KGs) to improve both global and local coherence for review
generation. The proposed model learns a two-level text plan for generating a
document: (1) the document plan is modeled as a sequence of sentence plans in
order, and (2) the sentence plan is modeled as an entity-based subgraph from
KG. Local coherence can be naturally enforced by KG subgraphs through
intra-sentence correlations between entities. For global coherence, we design a
hierarchical self-attentive architecture with both subgraph- and node-level
attention to enhance the correlations between subgraphs. To our knowledge, we
are the first to utilize a KG-based text planning model to enhance text
coherence for review generation. Extensive experiments on three datasets
confirm the effectiveness of our model on improving the content coherence of
generated texts.
| 2,021 |
Computation and Language
|
FNet: Mixing Tokens with Fourier Transforms
|
We show that Transformer encoder architectures can be sped up, with limited
accuracy costs, by replacing the self-attention sublayers with simple linear
transformations that "mix" input tokens. These linear mixers, along with
standard nonlinearities in feed-forward layers, prove competent at modeling
semantic relationships in several text classification tasks. Most surprisingly,
we find that replacing the self-attention sublayer in a Transformer encoder
with a standard, unparameterized Fourier Transform achieves 92-97% of the
accuracy of BERT counterparts on the GLUE benchmark, but trains 80% faster on
GPUs and 70% faster on TPUs at standard 512 input lengths. At longer input
lengths, our FNet model is significantly faster: when compared to the
"efficient" Transformers on the Long Range Arena benchmark, FNet matches the
accuracy of the most accurate models, while outpacing the fastest models across
all sequence lengths on GPUs (and across relatively shorter lengths on TPUs).
Finally, FNet has a light memory footprint and is particularly efficient at
smaller model sizes; for a fixed speed and accuracy budget, small FNet models
outperform Transformer counterparts.
| 2,022 |
Computation and Language
|
FastCorrect: Fast Error Correction with Edit Alignment for Automatic
Speech Recognition
|
Error correction techniques have been used to refine the output sentences
from automatic speech recognition (ASR) models and achieve a lower word error
rate (WER) than original ASR outputs. Previous works usually use a
sequence-to-sequence model to correct an ASR output sentence autoregressively,
which causes large latency and cannot be deployed in online ASR services. A
straightforward solution to reduce latency, inspired by non-autoregressive
(NAR) neural machine translation, is to use an NAR sequence generation model
for ASR error correction, which, however, comes at the cost of significantly
increased ASR error rate. In this paper, observing distinctive error patterns
and correction operations (i.e., insertion, deletion, and substitution) in ASR,
we propose FastCorrect, a novel NAR error correction model based on edit
alignment. In training, FastCorrect aligns each source token from an ASR output
sentence to the target tokens from the corresponding ground-truth sentence
based on the edit distance between the source and target sentences, and
extracts the number of target tokens corresponding to each source token during
edition/correction, which is then used to train a length predictor and to
adjust the source tokens to match the length of the target sentence for
parallel generation. In inference, the token number predicted by the length
predictor is used to adjust the source tokens for target sequence generation.
Experiments on the public AISHELL-1 dataset and an internal industrial-scale
ASR dataset show the effectiveness of FastCorrect for ASR error correction: 1)
it speeds up the inference by 6-9 times and maintains the accuracy (8-14% WER
reduction) compared with the autoregressive correction model; and 2) it
outperforms the popular NAR models adopted in neural machine translation and
text edition by a large margin.
| 2,022 |
Computation and Language
|
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents
|
Legal artificial intelligence (LegalAI) aims to benefit legal systems with
the technology of artificial intelligence, especially natural language
processing (NLP). Recently, inspired by the success of pre-trained language
models (PLMs) in the generic domain, many LegalAI researchers devote their
effort to apply PLMs to legal tasks. However, utilizing PLMs to address legal
tasks is still challenging, as the legal documents usually consist of thousands
of tokens, which is far longer than the length that mainstream PLMs can
process. In this paper, we release the Longformer-based pre-trained language
model, named as Lawformer, for Chinese legal long documents understanding. We
evaluate Lawformer on a variety of LegalAI tasks, including judgment
prediction, similar case retrieval, legal reading comprehension, and legal
question answering. The experimental results demonstrate that our model can
achieve promising improvement on tasks with long documents as inputs.
| 2,021 |
Computation and Language
|
gComm: An environment for investigating generalization in Grounded
Language Acquisition
|
gComm is a step towards developing a robust platform to foster research in
grounded language acquisition in a more challenging and realistic setting. It
comprises a 2-d grid environment with a set of agents (a stationary speaker and
a mobile listener connected via a communication channel) exposed to a
continuous array of tasks in a partially observable setting. The key to solving
these tasks lies in agents developing linguistic abilities and utilizing them
for efficiently exploring the environment. The speaker and listener have access
to information provided in different modalities, i.e. the speaker's input is a
natural language instruction that contains the target and task specifications
and the listener's input is its grid-view. Each must rely on the other to
complete the assigned task, however, the only way they can achieve the same, is
to develop and use some form of communication. gComm provides several tools for
studying different forms of communication and assessing their generalization.
| 2,021 |
Computation and Language
|
High-performance symbolic-numerics via multiple dispatch
|
As mathematical computing becomes more democratized in high-level languages,
high-performance symbolic-numeric systems are necessary for domain scientists
and engineers to get the best performance out of their machine without deep
knowledge of code optimization. Naturally, users need different term types
either to have different algebraic properties for them, or to use efficient
data structures. To this end, we developed Symbolics.jl, an extendable symbolic
system which uses dynamic multiple dispatch to change behavior depending on the
domain needs. In this work we detail an underlying abstract term interface
which allows for speed without sacrificing generality. We show that by
formalizing a generic API on actions independent of implementation, we can
retroactively add optimized data structures to our system without changing the
pre-existing term rewriters. We showcase how this can be used to optimize term
construction and give a 113x acceleration on general symbolic transformations.
Further, we show that such a generic API allows for complementary
term-rewriting implementations. We demonstrate the ability to swap between
classical term-rewriting simplifiers and e-graph-based term-rewriting
simplifiers. We showcase an e-graph ruleset which minimizes the number of CPU
cycles during expression evaluation, and demonstrate how it simplifies a
real-world reaction-network simulation to halve the runtime. Additionally, we
show a reaction-diffusion partial differential equation solver which is able to
be automatically converted into symbolic expressions via multiple dispatch
tracing, which is subsequently accelerated and parallelized to give a 157x
simulation speedup. Together, this presents Symbolics.jl as a next-generation
symbolic-numeric computing environment geared towards modeling and simulation.
| 2,022 |
Computation and Language
|
Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural
Machine Translation
|
The data scarcity in low-resource languages has become a bottleneck to
building robust neural machine translation systems. Fine-tuning a multilingual
pre-trained model (e.g., mBART (Liu et al., 2020)) on the translation task is a
good approach for low-resource languages; however, its performance will be
greatly limited when there are unseen languages in the translation pairs. In
this paper, we present a continual pre-training (CPT) framework on mBART to
effectively adapt it to unseen languages. We first construct noisy
mixed-language text from the monolingual corpus of the target language in the
translation pair to cover both the source and target languages, and then, we
continue pre-training mBART to reconstruct the original monolingual text.
Results show that our method can consistently improve the fine-tuning
performance upon the mBART baseline, as well as other strong baselines, across
all tested low-resource translation pairs containing unseen languages.
Furthermore, our approach also boosts the performance on translation pairs
where both languages are seen in the original mBART's pre-training. The code is
available at https://github.com/zliucr/cpt-nmt.
| 2,021 |
Computation and Language
|
Improving Patent Mining and Relevance Classification using Transformers
|
Patent analysis and mining are time-consuming and costly processes for
companies, but nevertheless essential if they are willing to remain
competitive. To face the overload induced by numerous patents, the idea is to
automatically filter them, bringing only few to read to experts. This paper
reports a successful application of fine-tuning and retraining on pre-trained
deep Natural Language Processing models on patent classification. The solution
that we propose combines several state-of-the-art treatments to achieve our
goal - decrease the workload while preserving recall and precision metrics.
| 2,021 |
Computation and Language
|
Advising Agent for Service-Providing Live-Chat Operators
|
Call centers, in which human operators attend clients using textual chat, are
very common in modern e-commerce. Training enough skilled operators who are
able to provide good service is a challenge. We suggest an algorithm and a
method to train and implement an assisting agent that provides on-line advice
to operators while they attend clients. The agent is domain-independent and can
be introduced to new domains without major efforts in design, training and
organizing structured knowledge of the professional discipline. We demonstrate
the applicability of the system in an experiment that realizes its full
life-cycle on a specific domain and analyze its capabilities.
| 2,021 |
Computation and Language
|
Dispatcher: A Message-Passing Approach To Language Modelling
|
This paper proposes a message-passing mechanism to address language
modelling. A new layer type is introduced that aims to substitute
self-attention for unidirectional sequence generation tasks. The system is
shown to be competitive with existing methods: Given N tokens, the
computational complexity is O(N logN) and the memory complexity is O(N) under
reasonable assumptions. In the end, the Dispatcher layer is seen to achieve
comparable perplexity to prior results while being more efficient.
| 2,021 |
Computation and Language
|
DocSCAN: Unsupervised Text Classification via Learning from Neighbors
|
We introduce DocSCAN, a completely unsupervised text classification approach
using Semantic Clustering by Adopting Nearest-Neighbors (SCAN). For each
document, we obtain semantically informative vectors from a large pre-trained
language model. Similar documents have proximate vectors, so neighbors in the
representation space tend to share topic labels. Our learnable clustering
approach uses pairs of neighboring datapoints as a weak learning signal. The
proposed approach learns to assign classes to the whole dataset without
provided ground-truth labels. On five topic classification benchmarks, we
improve on various unsupervised baselines by a large margin. In datasets with
relatively few and balanced outcome classes, DocSCAN approaches the performance
of supervised classification. The method fails for other types of
classification, such as sentiment analysis, pointing to important conceptual
and practical differences between classifying images and texts.
| 2,022 |
Computation and Language
|
Analyzing Online Political Advertisements
|
Online political advertising is a central aspect of modern election
campaigning for influencing public opinion. Computational analysis of political
ads is of utmost importance in political science to understand the
characteristics of digital campaigning. It is also important in computational
linguistics to study features of political discourse and communication on a
large scale. In this work, we present the first computational study on online
political ads with the aim to (1) infer the political ideology of an ad
sponsor; and (2) identify whether the sponsor is an official political party or
a third-party organization. We develop two new large datasets for the two tasks
consisting of ads from the U.S.. Evaluation results show that our approach that
combines textual and visual information from pre-trained neural models
outperforms a state-of-the-art method for generic commercial ad classification.
Finally, we provide an in-depth analysis of the limitations of our
best-performing models and linguistic analysis to study the characteristics of
political ads discourse.
| 2,021 |
Computation and Language
|
Societal Biases in Language Generation: Progress and Challenges
|
Technology for language generation has advanced rapidly, spurred by
advancements in pre-training large models on massive amounts of data and the
need for intelligent agents to communicate in a natural manner. While
techniques can effectively generate fluent text, they can also produce
undesirable societal biases that can have a disproportionately negative impact
on marginalized populations. Language generation presents unique challenges for
biases in terms of direct user interaction and the structure of decoding
techniques. To better understand these challenges, we present a survey on
societal biases in language generation, focusing on how data and techniques
contribute to biases and progress towards reducing biases. Motivated by a lack
of studies on biases from decoding techniques, we also conduct experiments to
quantify the effects of these techniques. By further discussing general trends
and open challenges, we call to attention promising directions for research and
the importance of fairness and inclusivity considerations for language
generation applications.
| 2,021 |
Computation and Language
|
SRLF: A Stance-aware Reinforcement Learning Framework for Content-based
Rumor Detection on Social Media
|
The rapid development of social media changes the lifestyle of people and
simultaneously provides an ideal place for publishing and disseminating rumors,
which severely exacerbates social panic and triggers a crisis of social trust.
Early content-based methods focused on finding clues from the text and user
profiles for rumor detection. Recent studies combine the stances of users'
comments with news content to capture the difference between true and false
rumors. Although the user's stance is effective for rumor detection, the manual
labeling process is time-consuming and labor-intensive, which limits the
application of utilizing it to facilitate rumor detection.
In this paper, we first finetune a pre-trained BERT model on a small labeled
dataset and leverage this model to annotate weak stance labels for users'
comment data to overcome the problem mentioned above. Then, we propose a novel
Stance-aware Reinforcement Learning Framework (SRLF) to select high-quality
labeled stance data for model training and rumor detection. Both the stance
selection and rumor detection tasks are optimized simultaneously to promote
both tasks mutually. We conduct experiments on two commonly used real-world
datasets. The experimental results demonstrate that our framework outperforms
the state-of-the-art models significantly, which confirms the effectiveness of
the proposed framework.
| 2,021 |
Computation and Language
|
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
|
Achieving human-level performance on some of Machine Reading Comprehension
(MRC) datasets is no longer challenging with the help of powerful Pre-trained
Language Models (PLMs). However, it is necessary to provide both answer
prediction and its explanation to further improve the MRC system's reliability,
especially for real-life applications. In this paper, we propose a new
benchmark called ExpMRC for evaluating the explainability of the MRC systems.
ExpMRC contains four subsets, including SQuAD, CMRC 2018, RACE$^+$, and C$^3$
with additional annotations of the answer's evidence. The MRC systems are
required to give not only the correct answer but also its explanation. We use
state-of-the-art pre-trained language models to build baseline systems and
adopt various unsupervised approaches to extract evidence without a
human-annotated training set. The experimental results show that these models
are still far from human performance, suggesting that the ExpMRC is
challenging. Resources will be available through
https://github.com/ymcui/expmrc
| 2,022 |
Computation and Language
|
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language
and Symbolic Reasoning
|
Geometry problem solving has attracted much attention in the NLP community
recently. The task is challenging as it requires abstract problem understanding
and symbolic reasoning with axiomatic knowledge. However, current datasets are
either small in scale or not publicly available. Thus, we construct a new
large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with
dense annotation in formal language. We further propose a novel geometry
solving approach with formal language and symbolic reasoning, called
Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the
problem text and diagram into formal language automatically via rule-based text
parsing and neural object detecting, respectively. Unlike implicit learning in
existing methods, Inter-GPS incorporates theorem knowledge as conditional rules
and performs symbolic reasoning step by step. Also, a theorem predictor is
designed to infer the theorem application sequence fed to the symbolic solver
for the more efficient and reasonable searching path. Extensive experiments on
the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves
significant improvements over existing methods. The project with code and data
is available at https://lupantech.github.io/inter-gps.
| 2,021 |
Computation and Language
|
REPT: Bridging Language Models and Machine Reading Comprehension via
Retrieval-Based Pre-training
|
Pre-trained Language Models (PLMs) have achieved great success on Machine
Reading Comprehension (MRC) over the past few years. Although the general
language representation learned from large-scale corpora does benefit MRC, the
poor support in evidence extraction which requires reasoning across multiple
sentences hinders PLMs from further advancing MRC. To bridge the gap between
general PLMs and MRC, we present REPT, a REtrieval-based Pre-Training approach.
In particular, we introduce two self-supervised tasks to strengthen evidence
extraction during pre-training, which is further inherited by downstream MRC
tasks through the consistent retrieval operation and model architecture. To
evaluate our proposed method, we conduct extensive experiments on five MRC
datasets that require collecting evidence from and reasoning across multiple
sentences. Experimental results demonstrate the effectiveness of our
pre-training approach. Moreover, further analysis shows that our approach is
able to enhance the capacity of evidence extraction without explicit
supervision.
| 2,021 |
Computation and Language
|
Similarities between Arabic Dialects: Investigating Geographical
Proximity
|
The automatic classification of Arabic dialects is an ongoing research
challenge, which has been explored in recent work that defines dialects based
on increasingly limited geographic areas like cities and provinces. This paper
focuses on a related yet relatively unexplored topic: the effects of the
geographical proximity of cities located in Arab countries on their dialectical
similarity. Our work is twofold, reliant on: 1) comparing the textual
similarities between dialects using cosine similarity and 2) measuring the
geographical distance between locations. We study MADAR and NADI, two
established datasets with Arabic dialects from many cities and provinces. Our
results indicate that cities located in different countries may in fact have
more dialectical similarity than cities within the same country, depending on
their geographical proximity. The correlation between dialectical similarity
and city proximity suggests that cities that are closer together are more
likely to share dialectical attributes, regardless of country borders. This
nuance provides the potential for important advancements in Arabic dialect
research because it indicates that a more granular approach to dialect
classification is essential to understanding how to frame the problem of Arabic
dialects identification.
| 2,022 |
Computation and Language
|
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State
Tracking
|
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle
task-oriented dialogue in unseen domains without the expense of collecting
in-domain data. In this paper, we propose a slot description enhanced
generative approach for zero-shot cross-domain DST. Specifically, our model
first encodes dialogue context and slots with a pre-trained self-attentive
encoder, and generates slot values in an auto-regressive manner. In addition,
we incorporate Slot Type Informed Descriptions that capture the shared
information across slots to facilitate cross-domain knowledge transfer.
Experimental results on the MultiWOZ dataset show that our proposed method
significantly improves existing state-of-the-art results in the zero-shot
cross-domain setting.
| 2,021 |
Computation and Language
|
ReadTwice: Reading Very Large Documents with Memories
|
Knowledge-intensive tasks such as question answering often require
assimilating information from different sections of large inputs such as books
or article collections. We propose ReadTwice, a simple and effective technique
that combines several strengths of prior approaches to model long-range
dependencies with Transformers. The main idea is to read text in small
segments, in parallel, summarizing each segment into a memory table to be used
in a second read of the text. We show that the method outperforms models of
comparable size on several question answering (QA) datasets and sets a new
state of the art on the challenging NarrativeQA task, with questions about
entire books. Source code and pre-trained checkpoints for ReadTwice can be
found at https://goo.gle/research-readtwice.
| 2,021 |
Computation and Language
|
DocOIE: A Document-level Context-Aware Dataset for OpenIE
|
Open Information Extraction (OpenIE) aims to extract structured relational
tuples (subject, relation, object) from sentences and plays critical roles for
many downstream NLP applications. Existing solutions perform extraction at
sentence level, without referring to any additional contextual information. In
reality, however, a sentence typically exists as part of a document rather than
standalone; we often need to access relevant contextual information around the
sentence before we can accurately interpret it. As there is no document-level
context-aware OpenIE dataset available, we manually annotate 800 sentences from
80 documents in two domains (Healthcare and Transportation) to form a DocOIE
dataset for evaluation. In addition, we propose DocIE, a novel document-level
context-aware OpenIE model. Our experimental results based on DocIE demonstrate
that incorporating document-level context is helpful in improving OpenIE
performance. Both DocOIE dataset and DocIE model are released for public.
| 2,021 |
Computation and Language
|
DefSent: Sentence Embeddings using Definition Sentences
|
Sentence embedding methods using natural language inference (NLI) datasets
have been successfully applied to various tasks. However, these methods are
only available for limited languages due to relying heavily on the large NLI
datasets. In this paper, we propose DefSent, a sentence embedding method that
uses definition sentences from a word dictionary, which performs comparably on
unsupervised semantics textual similarity (STS) tasks and slightly better on
SentEval tasks than conventional methods. Since dictionaries are available for
many languages, DefSent is more broadly applicable than methods using NLI
datasets without constructing additional datasets. We demonstrate that DefSent
performs comparably on unsupervised semantics textual similarity (STS) tasks
and slightly better on SentEval tasks to the methods using large NLI datasets.
Our code is publicly available at https://github.com/hpprc/defsent .
| 2,021 |
Computation and Language
|
Poolingformer: Long Document Modeling with Pooling Attention
|
In this paper, we introduce a two-level attention schema, Poolingformer, for
long document modeling. Its first level uses a smaller sliding window pattern
to aggregate information from neighbors. Its second level employs a larger
window to increase receptive fields with pooling attention to reduce both
computational cost and memory consumption. We first evaluate Poolingformer on
two long sequence QA tasks: the monolingual NQ and the multilingual TyDi QA.
Experimental results show that Poolingformer sits atop three official
leaderboards measured by F1, outperforming previous state-of-the-art models by
1.9 points (79.8 vs. 77.9) on NQ long answer, 1.9 points (79.5 vs. 77.6) on
TyDi QA passage answer, and 1.6 points (67.6 vs. 66.0) on TyDi QA minimal
answer. We further evaluate Poolingformer on a long sequence summarization
task. Experimental results on the arXiv benchmark continue to demonstrate its
superior performance.
| 2,022 |
Computation and Language
|
Recent Advances in Deep Learning Based Dialogue Systems: A Systematic
Survey
|
Dialogue systems are a popular natural language processing (NLP) task as it
is promising in real-life applications. It is also a complicated task since
many NLP tasks deserving study are involved. As a result, a multitude of novel
works on this task are carried out, and most of them are deep learning based
due to the outstanding performance. In this survey, we mainly focus on the deep
learning based dialogue systems. We comprehensively review state-of-the-art
research outcomes in dialogue systems and analyze them from two angles: model
type and system type. Specifically, from the angle of model type, we discuss
the principles, characteristics, and applications of different models that are
widely used in dialogue systems. This will help researchers acquaint these
models and see how they are applied in state-of-the-art frameworks, which is
rather helpful when designing a new dialogue system. From the angle of system
type, we discuss task-oriented and open-domain dialogue systems as two streams
of research, providing insight into the hot topics related. Furthermore, we
comprehensively review the evaluation methods and datasets for dialogue systems
to pave the way for future research. Finally, some possible research trends are
identified based on the recent research outcomes. To the best of our knowledge,
this survey is the most comprehensive and up-to-date one at present for deep
learning based dialogue systems, extensively covering the popular techniques.
We speculate that this work is a good starting point for academics who are new
to the dialogue systems or those who want to quickly grasp up-to-date
techniques in this area.
| 2,022 |
Computation and Language
|
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error
Correction
|
Grammatical Error Correction (GEC) aims to correct writing errors and help
language learners improve their writing skills. However, existing GEC models
tend to produce spurious corrections or fail to detect lots of errors. The
quality estimation model is necessary to ensure learners get accurate GEC
results and avoid misleading from poorly corrected sentences. Well-trained GEC
models can generate several high-quality hypotheses through decoding, such as
beam search, which provide valuable GEC evidence and can be used to evaluate
GEC quality. However, existing models neglect the possible GEC evidence from
different hypotheses. This paper presents the Neural Verification Network
(VERNet) for GEC quality estimation with multiple hypotheses. VERNet
establishes interactions among hypotheses with a reasoning graph and conducts
two kinds of attention mechanisms to propagate GEC evidence to verify the
quality of generated hypotheses. Our experiments on four GEC datasets show that
VERNet achieves state-of-the-art grammatical error detection performance,
achieves the best quality estimation results, and significantly improves GEC
performance by reranking hypotheses. All data and source codes are available at
https://github.com/thunlp/VERNet.
| 2,021 |
Computation and Language
|
Self-Guided Curriculum Learning for Neural Machine Translation
|
In the field of machine learning, the well-trained model is assumed to be
able to recover the training labels, i.e. the synthetic labels predicted by the
model should be as close to the ground-truth labels as possible. Inspired by
this, we propose a self-guided curriculum strategy to encourage the learning of
neural machine translation (NMT) models to follow the above recovery criterion,
where we cast the recovery degree of each training example as its learning
difficulty. Specifically, we adopt the sentence level BLEU score as the proxy
of recovery degree. Different from existing curricula relying on linguistic
prior knowledge or third-party language models, our chosen learning difficulty
is more suitable to measure the degree of knowledge mastery of the NMT models.
Experiments on translation benchmarks, including WMT14
English$\Rightarrow$German and WMT17 Chinese$\Rightarrow$English, demonstrate
that our approach can consistently improve translation performance against
strong baseline Transformer.
| 2,021 |
Computation and Language
|
End-to-End Speech Translation with Pre-trained Models and Adapters: UPC
at IWSLT 2021
|
This paper describes the submission to the IWSLT 2021 offline speech
translation task by the UPC Machine Translation group. The task consists of
building a system capable of translating English audio recordings extracted
from TED talks into German text. Submitted systems can be either cascade or
end-to-end and use a custom or given segmentation. Our submission is an
end-to-end speech translation system, which combines pre-trained models
(Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder,
and uses an efficient fine-tuning technique, which trains only 20% of its total
parameters. We show that adding an Adapter to the system and pre-training it,
can increase the convergence speed and the final result, with which we achieve
a BLEU score of 27.3 on the MuST-C test set. Our final model is an ensemble
that obtains 28.22 BLEU score on the same set. Our submission also uses a
custom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for
identifying periods of untranscribable text and can bring improvements of 2.5
to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the
given segmentation.
| 2,021 |
Computation and Language
|
Automatic Classification of Human Translation and Machine Translation: A
Study from the Perspective of Lexical Diversity
|
By using a trigram model and fine-tuning a pretrained BERT model for sequence
classification, we show that machine translation and human translation can be
classified with an accuracy above chance level, which suggests that machine
translation and human translation are different in a systematic way. The
classification accuracy of machine translation is much higher than of human
translation. We show that this may be explained by the difference in lexical
diversity between machine translation and human translation. If machine
translation has independent patterns from human translation, automatic metrics
which measure the deviation of machine translation from human translation may
conflate difference with quality. Our experiment with two different types of
automatic metrics shows correlation with the result of the classification task.
Therefore, we suggest the difference in lexical diversity between machine
translation and human translation be given more attention in machine
translation evaluation.
| 2,021 |
Computation and Language
|
Improving Factual Consistency of Abstractive Summarization via Question
Answering
|
A commonly observed problem with the state-of-the art abstractive
summarization models is that the generated summaries can be factually
inconsistent with the input documents. The fact that automatic summarization
may produce plausible-sounding yet inaccurate summaries is a major concern that
limits its wide application. In this paper we present an approach to address
factual consistency in summarization. We first propose an efficient automatic
evaluation metric to measure factual consistency; next, we propose a novel
learning algorithm that maximizes the proposed metric during model training.
Through extensive experiments, we confirm that our method is effective in
improving factual consistency and even overall quality of the summaries, as
judged by both automatic metrics and human evaluation.
| 2,021 |
Computation and Language
|
Measuring Economic Policy Uncertainty Using an Unsupervised Word
Embedding-based Method
|
Economic Policy Uncertainty (EPU) is a critical indicator in economic
studies, while it can be used to forecast a recession. Under higher levels of
uncertainty, firms' owners cut their investment, which leads to a longer
post-recession recovery. EPU index is computed by counting news articles
containing pre-defined keywords related to policy-making and economy and convey
uncertainty. Unfortunately, this method is sensitive to the original keyword
set, its richness, and the news coverage. Thus, reproducing its results for
different countries is challenging. In this paper, we propose an unsupervised
text mining method that uses word-embedding representation space to select
relevant keywords. This method is not strictly sensitive to the semantic
similarity threshold applied to the word embedding vectors and does not require
a pre-defined dictionary. Our experiments using a massive repository of Persian
news show that the EPU series computed by the proposed method precisely follows
major events affecting Iran's economy and is compatible with the World
Uncertainty Index (WUI) of Iran.
| 2,021 |
Computation and Language
|
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