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Hidden Biases in Unreliable News Detection Datasets
|
Automatic unreliable news detection is a research problem with great
potential impact. Recently, several papers have shown promising results on
large-scale news datasets with models that only use the article itself without
resorting to any fact-checking mechanism or retrieving any supporting evidence.
In this work, we take a closer look at these datasets. While they all provide
valuable resources for future research, we observe a number of problems that
may lead to results that do not generalize in more realistic settings.
Specifically, we show that selection bias during data collection leads to
undesired artifacts in the datasets. In addition, while most systems train and
predict at the level of individual articles, overlapping article sources in the
training and evaluation data can provide a strong confounding factor that
models can exploit. In the presence of this confounding factor, the models can
achieve good performance by directly memorizing the site-label mapping instead
of modeling the real task of unreliable news detection. We observed a
significant drop (>10%) in accuracy for all models tested in a clean split with
no train/test source overlap. Using the observations and experimental results,
we provide practical suggestions on how to create more reliable datasets for
the unreliable news detection task. We suggest future dataset creation include
a simple model as a difficulty/bias probe and future model development use a
clean non-overlapping site and date split.
| 2,021 |
Computation and Language
|
Towards Solving Multimodal Comprehension
|
This paper targets the problem of procedural multimodal machine comprehension
(M3C). This task requires an AI to comprehend given steps of multimodal
instructions and then answer questions. Compared to vanilla machine
comprehension tasks where an AI is required only to understand a textual input,
procedural M3C is more challenging as the AI needs to comprehend both the
temporal and causal factors along with multimodal inputs. Recently Yagcioglu et
al. [35] introduced RecipeQA dataset to evaluate M3C. Our first contribution is
the introduction of two new M3C datasets- WoodworkQA and DecorationQA with 16K
and 10K instructional procedures, respectively. We then evaluate M3C using a
textual cloze style question-answering task and highlight an inherent bias in
the question answer generation method from [35] that enables a naive baseline
to cheat by learning from only answer choices. This naive baseline performs
similar to a popular method used in question answering- Impatient Reader [6]
that uses attention over both the context and the query. We hypothesized that
this naturally occurring bias present in the dataset affects even the best
performing model. We verify our proposed hypothesis and propose an algorithm
capable of modifying the given dataset to remove the bias elements. Finally, we
report our performance on the debiased dataset with several strong baselines.
We observe that the performance of all methods falls by a margin of 8% - 16%
after correcting for the bias. We hope these datasets and the analysis will
provide valuable benchmarks and encourage further research in this area.
| 2,021 |
Computation and Language
|
Evaluating the Immediate Applicability of Pose Estimation for Sign
Language Recognition
|
Signed languages are visual languages produced by the movement of the hands,
face, and body. In this paper, we evaluate representations based on skeleton
poses, as these are explainable, person-independent, privacy-preserving,
low-dimensional representations. Basically, skeletal representations generalize
over an individual's appearance and background, allowing us to focus on the
recognition of motion. But how much information is lost by the skeletal
representation? We perform two independent studies using two state-of-the-art
pose estimation systems. We analyze the applicability of the pose estimation
systems to sign language recognition by evaluating the failure cases of the
recognition models. Importantly, this allows us to characterize the current
limitations of skeletal pose estimation approaches in sign language
recognition.
| 2,021 |
Computation and Language
|
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense
Reasoning Tasks
|
Integrating external knowledge into commonsense reasoning tasks has shown
progress in resolving some, but not all, knowledge gaps in these tasks. For
knowledge integration to yield peak performance, it is critical to select a
knowledge graph (KG) that is well-aligned with the given task's objective. We
present an approach to assess how well a candidate KG can correctly identify
and accurately fill in gaps of reasoning for a task, which we call KG-to-task
match. We show this KG-to-task match in 3 phases: knowledge-task
identification, knowledge-task alignment, and knowledge-task integration. We
also analyze our transformer-based KG-to-task models via commonsense probes to
measure how much knowledge is captured in these models before and after KG
integration. Empirically, we investigate KG matches for the SocialIQA (SIQA)
(Sap et al., 2019b), Physical IQA (PIQA) (Bisk et al., 2020), and MCScript2.0
(Ostermann et al., 2019) datasets with 3 diverse KGs: ATOMIC (Sap et al.,
2019a), ConceptNet (Speer et al., 2017), and an automatically constructed
instructional KG based on WikiHow (Koupaee and Wang, 2018). With our methods we
are able to demonstrate that ATOMIC, an event-inference focused KG, is the best
match for SIQA and MCScript2.0, and that the taxonomic ConceptNet and
WikiHow-based KGs are the best matches for PIQA across all 3 analysis phases.
We verify our methods and findings with human evaluation.
| 2,021 |
Computation and Language
|
How individuals change language
|
Languages emerge and change over time at the population level though
interactions between individual speakers. It is, however, hard to directly
observe how a single speaker's linguistic innovation precipitates a
population-wide change in the language, and many theoretical proposals exist.
We introduce a very general mathematical model that encompasses a wide variety
of individual-level linguistic behaviours and provides statistical predictions
for the population-level changes that result from them. This model allows us to
compare the likelihood of empirically-attested changes in definite and
indefinite articles in multiple languages under different assumptions on the
way in which individuals learn and use language. We find that accounts of
language change that appeal primarily to errors in childhood language
acquisition are very weakly supported by the historical data, whereas those
that allow speakers to change incrementally across the lifespan are more
plausible, particularly when combined with social network effects.
| 2,021 |
Computation and Language
|
Machine Learning Meets Natural Language Processing -- The story so far
|
Natural Language Processing (NLP) has evolved significantly over the last
decade. This paper highlights the most important milestones of this period
while trying to pinpoint the contribution of each individual model and
algorithm to the overall progress. Furthermore, it focuses on issues still
remaining to be solved, emphasizing the groundbreaking proposals of
Transformers, BERT, and all the similar attention-based models.
| 2,021 |
Computation and Language
|
Evaluating the Impact of a Hierarchical Discourse Representation on
Entity Coreference Resolution Performance
|
Recent work on entity coreference resolution (CR) follows current trends in
Deep Learning applied to embeddings and relatively simple task-related
features. SOTA models do not make use of hierarchical representations of
discourse structure. In this work, we leverage automatically constructed
discourse parse trees within a neural approach and demonstrate a significant
improvement on two benchmark entity coreference-resolution datasets. We explore
how the impact varies depending upon the type of mention.
| 2,021 |
Computation and Language
|
Modeling Event Plausibility with Consistent Conceptual Abstraction
|
Understanding natural language requires common sense, one aspect of which is
the ability to discern the plausibility of events. While distributional models
-- most recently pre-trained, Transformer language models -- have demonstrated
improvements in modeling event plausibility, their performance still falls
short of humans'. In this work, we show that Transformer-based plausibility
models are markedly inconsistent across the conceptual classes of a lexical
hierarchy, inferring that "a person breathing" is plausible while "a dentist
breathing" is not, for example. We find this inconsistency persists even when
models are softly injected with lexical knowledge, and we present a simple
post-hoc method of forcing model consistency that improves correlation with
human plausibility judgements.
| 2,021 |
Computation and Language
|
Analyzing COVID-19 Tweets with Transformer-based Language Models
|
This paper describes a method for using Transformer-based Language Models
(TLMs) to understand public opinion from social media posts. In this approach,
we train a set of GPT models on several COVID-19 tweet corpora that reflect
populations of users with distinctive views. We then use prompt-based queries
to probe these models to reveal insights into the biases and opinions of the
users. We demonstrate how this approach can be used to produce results which
resemble polling the public on diverse social, political and public health
issues. The results on the COVID-19 tweet data show that transformer language
models are promising tools that can help us understand public opinions on
social media at scale.
| 2,021 |
Computation and Language
|
StateCensusLaws.org: A Web Application for Consuming and Annotating
Legal Discourse Learning
|
In this work, we create a web application to highlight the output of NLP
models trained to parse and label discourse segments in law text. Our system is
built primarily with journalists and legal interpreters in mind, and we focus
on state-level law that uses U.S. Census population numbers to allocate
resources and organize government.
Our system exposes a corpus we collect of 6,000 state-level laws that pertain
to the U.S. census, using 25 scrapers we built to crawl state law websites,
which we release. We also build a novel, flexible annotation framework that can
handle span-tagging and relation tagging on an arbitrary input text document
and be embedded simply into any webpage. This framework allows journalists and
researchers to add to our annotation database by correcting and tagging new
data.
| 2,022 |
Computation and Language
|
Novel Aficionados and Doppelg\"angers: a referential task for semantic
representations of individual entities
|
In human semantic cognition, proper names (names which refer to individual
entities) are harder to learn and retrieve than common nouns. This seems to be
the case for machine learning algorithms too, but the linguistic and
distributional reasons for this behaviour have not been investigated in depth
so far. To tackle this issue, we show that the semantic distinction between
proper names and common nouns is reflected in their linguistic distributions by
employing an original task for distributional semantics, the Doppelg\"anger
test, an extensive set of models, and a new dataset, the Novel Aficionados
dataset. The results indicate that the distributional representations of
different individual entities are less clearly distinguishable from each other
than those of common nouns, an outcome which intriguingly mirrors human
cognition.
| 2,021 |
Computation and Language
|
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual
Question Answering
|
Images are more than a collection of objects or attributes -- they represent
a web of relationships among interconnected objects. Scene Graph has emerged as
a new modality for a structured graphical representation of images. Scene Graph
encodes objects as nodes connected via pairwise relations as edges. To support
question answering on scene graphs, we propose GraphVQA, a language-guided
graph neural network framework that translates and executes a natural language
question as multiple iterations of message passing among graph nodes. We
explore the design space of GraphVQA framework, and discuss the trade-off of
different design choices. Our experiments on GQA dataset show that GraphVQA
outperforms the state-of-the-art model by a large margin (88.43% vs. 94.78%).
| 2,021 |
Computation and Language
|
Diverse and Specific Clarification Question Generation with Keywords
|
Product descriptions on e-commerce websites often suffer from missing
important aspects. Clarification question generation (CQGen) can be a promising
approach to help alleviate the problem. Unlike traditional QGen assuming the
existence of answers in the context and generating questions accordingly, CQGen
mimics user behaviors of asking for unstated information. The generated CQs can
serve as a sanity check or proofreading to help e-commerce merchant to identify
potential missing information before advertising their product, and improve
consumer experience consequently. Due to the variety of possible user
backgrounds and use cases, the information need can be quite diverse but also
specific to a detailed topic, while previous works assume generating one CQ per
context and the results tend to be generic. We thus propose the task of Diverse
CQGen and also tackle the challenge of specificity. We propose a new model
named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to
deal with the tasks. Automatic and human evaluation on 2 datasets (Home &
Kitchen, Office) showed that KPCNet can generate more specific questions and
promote better group-level diversity than several competing baselines.
| 2,021 |
Computation and Language
|
Discriminative Self-training for Punctuation Prediction
|
Punctuation prediction for automatic speech recognition (ASR) output
transcripts plays a crucial role for improving the readability of the ASR
transcripts and for improving the performance of downstream natural language
processing applications. However, achieving good performance on punctuation
prediction often requires large amounts of labeled speech transcripts, which is
expensive and laborious. In this paper, we propose a Discriminative
Self-Training approach with weighted loss and discriminative label smoothing to
exploit unlabeled speech transcripts. Experimental results on the English
IWSLT2011 benchmark test set and an internal Chinese spoken language dataset
demonstrate that the proposed approach achieves significant improvement on
punctuation prediction accuracy over strong baselines including BERT, RoBERTa,
and ELECTRA models. The proposed Discriminative Self-Training approach
outperforms the vanilla self-training approach. We establish a new
state-of-the-art (SOTA) on the IWSLT2011 test set, outperforming the current
SOTA model by 1.3% absolute gain on F$_1$.
| 2,021 |
Computation and Language
|
Sensitivity as a Complexity Measure for Sequence Classification Tasks
|
We introduce a theoretical framework for understanding and predicting the
complexity of sequence classification tasks, using a novel extension of the
theory of Boolean function sensitivity. The sensitivity of a function, given a
distribution over input sequences, quantifies the number of disjoint subsets of
the input sequence that can each be individually changed to change the output.
We argue that standard sequence classification methods are biased towards
learning low-sensitivity functions, so that tasks requiring high sensitivity
are more difficult. To that end, we show analytically that simple lexical
classifiers can only express functions of bounded sensitivity, and we show
empirically that low-sensitivity functions are easier to learn for LSTMs. We
then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher
on challenging tasks collected in GLUE than on simple text classification
tasks, and that sensitivity predicts the performance both of simple lexical
classifiers and of vanilla BiLSTMs without pretrained contextualized
embeddings. Within a task, sensitivity predicts which inputs are hard for such
simple models. Our results suggest that the success of massively pretrained
contextual representations stems in part because they provide representations
from which information can be extracted by low-sensitivity decoders.
| 2,021 |
Computation and Language
|
Improving Biomedical Pretrained Language Models with Knowledge
|
Pretrained language models have shown success in many natural language
processing tasks. Many works explore incorporating knowledge into language
models. In the biomedical domain, experts have taken decades of effort on
building large-scale knowledge bases. For example, the Unified Medical Language
System (UMLS) contains millions of entities with their synonyms and defines
hundreds of relations among entities. Leveraging this knowledge can benefit a
variety of downstream tasks such as named entity recognition and relation
extraction. To this end, we propose KeBioLM, a biomedical pretrained language
model that explicitly leverages knowledge from the UMLS knowledge bases.
Specifically, we extract entities from PubMed abstracts and link them to UMLS.
We then train a knowledge-aware language model that firstly applies a text-only
encoding layer to learn entity representation and applies a text-entity fusion
encoding to aggregate entity representation. Besides, we add two training
objectives as entity detection and entity linking. Experiments on the named
entity recognition and relation extraction from the BLURB benchmark demonstrate
the effectiveness of our approach. Further analysis on a collected probing
dataset shows that our model has better ability to model medical knowledge.
| 2,021 |
Computation and Language
|
Pre-training for Spoken Language Understanding with Joint Textual and
Phonetic Representation Learning
|
In the traditional cascading architecture for spoken language understanding
(SLU), it has been observed that automatic speech recognition errors could be
detrimental to the performance of natural language understanding. End-to-end
(E2E) SLU models have been proposed to directly map speech input to desired
semantic frame with a single model, hence mitigating ASR error propagation.
Recently, pre-training technologies have been explored for these E2E models. In
this paper, we propose a novel joint textual-phonetic pre-training approach for
learning spoken language representations, aiming at exploring the full
potentials of phonetic information to improve SLU robustness to ASR errors. We
explore phoneme labels as high-level speech features, and design and compare
pre-training tasks based on conditional masked language model objectives and
inter-sentence relation objectives. We also investigate the efficacy of
combining textual and phonetic information during fine-tuning. Experimental
results on spoken language understanding benchmarks, Fluent Speech Commands and
SNIPS, show that the proposed approach significantly outperforms strong
baseline models and improves robustness of spoken language understanding to ASR
errors.
| 2,021 |
Computation and Language
|
End-to-end Speech Translation via Cross-modal Progressive Training
|
End-to-end speech translation models have become a new trend in research due
to their potential of reducing error propagation. However, these models still
suffer from the challenge of data scarcity. How to effectively use unlabeled or
other parallel corpora from machine translation is promising but still an open
problem. In this paper, we propose Cross Speech-Text Network (XSTNet), an
end-to-end model for speech-to-text translation. XSTNet takes both speech and
text as input and outputs both transcription and translation text. The model
benefits from its three key design aspects: a self-supervised pre-trained
sub-network as the audio encoder, a multi-task training objective to exploit
additional parallel bilingual text, and a progressive training procedure. We
evaluate the performance of XSTNet and baselines on the MuST-C En-X and
LibriSpeech En-Fr datasets. In particular, XSTNet achieves state-of-the-art
results on all language directions with an average BLEU of 28.8, outperforming
the previous best method by 3.2 BLEU. Code, models, cases, and more detailed
analysis are available at https://github.com/ReneeYe/XSTNet.
| 2,021 |
Computation and Language
|
On User Interfaces for Large-Scale Document-Level Human Evaluation of
Machine Translation Outputs
|
Recent studies emphasize the need of document context in human evaluation of
machine translations, but little research has been done on the impact of user
interfaces on annotator productivity and the reliability of assessments. In
this work, we compare human assessment data from the last two WMT evaluation
campaigns collected via two different methods for document-level evaluation.
Our analysis shows that a document-centric approach to evaluation where the
annotator is presented with the entire document context on a screen leads to
higher quality segment and document level assessments. It improves the
correlation between segment and document scores and increases inter-annotator
agreement for document scores but is considerably more time consuming for
annotators.
| 2,021 |
Computation and Language
|
Should we Stop Training More Monolingual Models, and Simply Use Machine
Translation Instead?
|
Most work in NLP makes the assumption that it is desirable to develop
solutions in the native language in question. There is consequently a strong
trend towards building native language models even for low-resource languages.
This paper questions this development, and explores the idea of simply
translating the data into English, thereby enabling the use of pretrained, and
large-scale, English language models. We demonstrate empirically that a large
English language model coupled with modern machine translation outperforms
native language models in most Scandinavian languages. The exception to this is
Finnish, which we assume is due to inferior translation quality. Our results
suggest that machine translation is a mature technology, which raises a serious
counter-argument for training native language models for low-resource
languages. This paper therefore strives to make a provocative but important
point. As English language models are improving at an unprecedented pace, which
in turn improves machine translation, it is from an empirical and environmental
stand-point more effective to translate data from low-resource languages into
English, than to build language models for such languages.
| 2,021 |
Computation and Language
|
Text Summarization of Czech News Articles Using Named Entities
|
The foundation for the research of summarization in the Czech language was
laid by the work of Straka et al. (2018). They published the SumeCzech, a large
Czech news-based summarization dataset, and proposed several baseline
approaches. However, it is clear from the achieved results that there is a
large space for improvement. In our work, we focus on the impact of named
entities on the summarization of Czech news articles. First, we annotate
SumeCzech with named entities. We propose a new metric ROUGE_NE that measures
the overlap of named entities between the true and generated summaries, and we
show that it is still challenging for summarization systems to reach a high
score in it. We propose an extractive summarization approach Named Entity
Density that selects a sentence with the highest ratio between a number of
entities and the length of the sentence as the summary of the article. The
experiments show that the proposed approach reached results close to the solid
baseline in the domain of news articles selecting the first sentence. Moreover,
we demonstrate that the selected sentence reflects the style of reports
concisely identifying to whom, when, where, and what happened. We propose that
such a summary is beneficial in combination with the first sentence of an
article in voice applications presenting news articles. We propose two
abstractive summarization approaches based on Seq2Seq architecture. The first
approach uses the tokens of the article. The second approach has access to the
named entity annotations. The experiments show that both approaches exceed
state-of-the-art results previously reported by Straka et al. (2018), with the
latter achieving slightly better results on SumeCzech's out-of-domain testing
set.
| 2,021 |
Computation and Language
|
End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
|
Disease name recognition and normalization, which is generally called
biomedical entity linking, is a fundamental process in biomedical text mining.
Recently, neural joint learning of both tasks has been proposed to utilize the
mutual benefits. While this approach achieves high performance, disease
concepts that do not appear in the training dataset cannot be accurately
predicted. This study introduces a novel end-to-end approach that combines span
representations with dictionary-matching features to address this problem. Our
model handles unseen concepts by referring to a dictionary while maintaining
the performance of neural network-based models, in an end-to-end fashion.
Experiments using two major datasets demonstrate that our model achieved
competitive results with strong baselines, especially for unseen concepts
during training.
| 2,021 |
Computation and Language
|
On Sampling-Based Training Criteria for Neural Language Modeling
|
As the vocabulary size of modern word-based language models becomes ever
larger, many sampling-based training criteria are proposed and investigated.
The essence of these sampling methods is that the softmax-related traversal
over the entire vocabulary can be simplified, giving speedups compared to the
baseline. A problem we notice about the current landscape of such sampling
methods is the lack of a systematic comparison and some myths about preferring
one over another. In this work, we consider Monte Carlo sampling, importance
sampling, a novel method we call compensated partial summation, and noise
contrastive estimation. Linking back to the three traditional criteria, namely
mean squared error, binary cross-entropy, and cross-entropy, we derive the
theoretical solutions to the training problems. Contrary to some common belief,
we show that all these sampling methods can perform equally well, as long as we
correct for the intended class posterior probabilities. Experimental results in
language modeling and automatic speech recognition on Switchboard and
LibriSpeech support our claim, with all sampling-based methods showing similar
perplexities and word error rates while giving the expected speedups.
| 2,021 |
Computation and Language
|
How Will Your Tweet Be Received? Predicting the Sentiment Polarity of
Tweet Replies
|
Twitter sentiment analysis, which often focuses on predicting the polarity of
tweets, has attracted increasing attention over the last years, in particular
with the rise of deep learning (DL). In this paper, we propose a new task:
predicting the predominant sentiment among (first-order) replies to a given
tweet. Therefore, we created RETWEET, a large dataset of tweets and replies
manually annotated with sentiment labels. As a strong baseline, we propose a
two-stage DL-based method: first, we create automatically labeled training data
by applying a standard sentiment classifier to tweet replies and aggregating
its predictions for each original tweet; our rationale is that individual
errors made by the classifier are likely to cancel out in the aggregation step.
Second, we use the automatically labeled data for supervised training of a
neural network to predict reply sentiment from the original tweets. The
resulting classifier is evaluated on the new RETWEET dataset, showing promising
results, especially considering that it has been trained without any manually
labeled data. Both the dataset and the baseline implementation are publicly
available.
| 2,021 |
Computation and Language
|
Improving BERT Pretraining with Syntactic Supervision
|
Bidirectional masked Transformers have become the core theme in the current
NLP landscape. Despite their impressive benchmarks, a recurring theme in recent
research has been to question such models' capacity for syntactic
generalization. In this work, we seek to address this question by adding a
supervised, token-level supertagging objective to standard unsupervised
pretraining, enabling the explicit incorporation of syntactic biases into the
network's training dynamics. Our approach is straightforward to implement,
induces a marginal computational overhead and is general enough to adapt to a
variety of settings. We apply our methodology on Lassy Large, an automatically
annotated corpus of written Dutch. Our experiments suggest that our
syntax-aware model performs on par with established baselines, despite Lassy
Large being one order of magnitude smaller than commonly used corpora.
| 2,021 |
Computation and Language
|
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning
Architectures
|
In recent years, Natural Language Processing (NLP) models have achieved
phenomenal success in linguistic and semantic tasks like text classification,
machine translation, cognitive dialogue systems, information retrieval via
Natural Language Understanding (NLU), and Natural Language Generation (NLG).
This feat is primarily attributed due to the seminal Transformer architecture,
leading to designs such as BERT, GPT (I, II, III), etc. Although these
large-size models have achieved unprecedented performances, they come at high
computational costs. Consequently, some of the recent NLP architectures have
utilized concepts of transfer learning, pruning, quantization, and knowledge
distillation to achieve moderate model sizes while keeping nearly similar
performances as achieved by their predecessors. Additionally, to mitigate the
data size challenge raised by language models from a knowledge extraction
perspective, Knowledge Retrievers have been built to extricate explicit data
documents from a large corpus of databases with greater efficiency and
accuracy. Recent research has also focused on superior inference by providing
efficient attention to longer input sequences. In this paper, we summarize and
examine the current state-of-the-art (SOTA) NLP models that have been employed
for numerous NLP tasks for optimal performance and efficiency. We provide a
detailed understanding and functioning of the different architectures, a
taxonomy of NLP designs, comparative evaluations, and future directions in NLP.
| 2,021 |
Computation and Language
|
K-XLNet: A General Method for Combining Explicit Knowledge with Language
Model Pretraining
|
Though pre-trained language models such as Bert and XLNet, have rapidly
advanced the state-of-the-art on many NLP tasks, they implicit semantics only
relying on surface information between words in corpus. Intuitively, background
knowledge influences the efficacy of understanding. Inspired by this common
sense, we focus on improving model pretraining by leveraging explicit
knowledge. Different from recent research that optimize pretraining model by
knowledge masking strategies, we propose a simple but general method to combine
explicit knowledge with pretraining. To be specific, we first match knowledge
facts from knowledge graph (KG) and then add a knowledge injunction layer to
transformer directly without changing its architecture. The present study seeks
to find the direct impact of explicit knowledge on transformer per-training. We
conduct experiments on various datasets for different downstream tasks. The
experimental results show that solely by adding external knowledge to
transformer can improve the learning performance on many NLP tasks.
| 2,021 |
Computation and Language
|
TransICD: Transformer Based Code-wise Attention Model for Explainable
ICD Coding
|
International Classification of Disease (ICD) coding procedure which refers
to tagging medical notes with diagnosis codes has been shown to be effective
and crucial to the billing system in medical sector. Currently, ICD codes are
assigned to a clinical note manually which is likely to cause many errors.
Moreover, training skilled coders also requires time and human resources.
Therefore, automating the ICD code determination process is an important task.
With the advancement of artificial intelligence theory and computational
hardware, machine learning approach has emerged as a suitable solution to
automate this process. In this project, we apply a transformer-based
architecture to capture the interdependence among the tokens of a document and
then use a code-wise attention mechanism to learn code-specific representations
of the entire document. Finally, they are fed to separate dense layers for
corresponding code prediction. Furthermore, to handle the imbalance in the code
frequency of clinical datasets, we employ a label distribution aware margin
(LDAM) loss function. The experimental results on the MIMIC-III dataset show
that our proposed model outperforms other baselines by a significant margin. In
particular, our best setting achieves a micro-AUC score of 0.923 compared to
0.868 of bidirectional recurrent neural networks. We also show that by using
the code-wise attention mechanism, the model can provide more insights about
its prediction, and thus it can support clinicians to make reliable decisions.
Our code is available online (https://github.com/biplob1ly/TransICD)
| 2,021 |
Computation and Language
|
Using GPT-2 to Create Synthetic Data to Improve the Prediction
Performance of NLP Machine Learning Classification Models
|
Classification Models use input data to predict the likelihood that the
subsequent input data will fall into predetermined categories. To perform
effective classifications, these models require large datasets for training. It
is becoming common practice to utilize synthetic data to boost the performance
of Machine Learning Models. It is reported that Shell is using synthetic data
to build models to detect problems that rarely occur; for example Shell created
synthetic data to help models to identify deteriorating oil lines. It is common
practice for Machine Learning Practitioners to generate synthetic data by
rotating, flipping, and cropping images to increase the volume of image data to
train Convolutional Neural Networks. The purpose of this paper is to explore
creating and utilizing synthetic NLP data to improve the performance of Natural
Language Processing Machine Learning Classification Models. In this paper I
used a Yelp pizza restaurant reviews dataset and transfer learning to fine-tune
a pre-trained GPT-2 Transformer Model to generate synthetic pizza reviews data.
I then combined this synthetic data with the original genuine data to create a
new joint dataset. The new combined model significantly outperformed the
original model in accuracy and precision.
| 2,021 |
Computation and Language
|
Interval Probabilistic Fuzzy WordNet
|
WordNet lexical-database groups English words into sets of synonyms called
"synsets." Synsets are utilized for several applications in the field of
text-mining. However, they were also open to criticism because although, in
reality, not all the members of a synset represent the meaning of that synset
with the same degree, in practice, they are considered as members of the
synset, identically. Thus, the fuzzy version of synsets, called fuzzy-synsets
(or fuzzy word-sense classes) were proposed and studied. In this study, we
discuss why (type-1) fuzzy synsets (T1 F-synsets) do not properly model the
membership uncertainty, and propose an upgraded version of fuzzy synsets in
which membership degrees of word-senses are represented by intervals, similar
to what in Interval Type 2 Fuzzy Sets (IT2 FS) and discuss that IT2 FS
theoretical framework is insufficient for analysis and design of such synsets,
and propose a new concept, called Interval Probabilistic Fuzzy (IPF) sets. Then
we present an algorithm for constructing the IPF synsets in any language, given
a corpus and a word-sense-disambiguation system. Utilizing our algorithm and
the open-American-online-corpus (OANC) and UKB word-sense-disambiguation, we
constructed and published the IPF synsets of WordNet for English language.
| 2,021 |
Computation and Language
|
Towards Automated Psychotherapy via Language Modeling
|
In this experiment, a model was devised, trained, and evaluated to automate
psychotherapist/client text conversations through the use of state-of-the-art,
Seq2Seq Transformer-based Natural Language Generation (NLG) systems. Through
training the model upon a mix of the Cornell Movie Dialogue Corpus for language
understanding and an open-source, anonymized, and public licensed
psychotherapeutic dataset, the model achieved statistically significant
performance in published, standardized qualitative benchmarks against
human-written validation data - meeting or exceeding human-written responses'
performance in 59.7% and 67.1% of the test set for two independent test methods
respectively. Although the model cannot replace the work of psychotherapists
entirely, its ability to synthesize human-appearing utterances for the majority
of the test set serves as a promising step towards communizing and easing
stigma at the psychotherapeutic point-of-care.
| 2,021 |
Computation and Language
|
COVID-19 sentiment analysis via deep learning during the rise of novel
cases
|
Social scientists and psychologists take interest in understanding how people
express emotions and sentiments when dealing with catastrophic events such as
natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a
catastrophic event that has raised a number of psychological issues such as
depression given abrupt social changes and lack of employment. Advancements of
deep learning-based language models have been promising for sentiment analysis
with data from social networks such as Twitter. Given the situation with
COVID-19 pandemic, different countries had different peaks where the rise and
fall of new cases affected lock-downs which directly affected the economy and
employment. During the rise of COVID-19 cases with stricter lock-downs, people
have been expressing their sentiments in social media. This can provide a deep
understanding of human psychology during catastrophic events. In this paper, we
present a framework that employs deep learning-based language models via long
short-term memory (LSTM) recurrent neural networks for sentiment analysis
during the rise of novel COVID-19 cases in India. The framework features LSTM
language model with a global vector embedding and state-of-art BERT language
model. We review the sentiments expressed for selective months in 2020 which
covers the first major peak of novel cases in India. Our framework utilises
multi-label sentiment classification where more than one sentiment can be
expressed at once. Our results indicate that the majority of the tweets have
been positive with high levels of optimism during the rise of the novel
COVID-19 cases and the number of tweets significantly lowered towards the peak.
The predictions generally indicate that although the majority have been
optimistic, a significant group of population has been annoyed towards the way
the pandemic was handled by the authorities.
| 2,021 |
Computation and Language
|
Learning Fine-grained Fact-Article Correspondence in Legal Cases
|
Automatically recommending relevant law articles to a given legal case has
attracted much attention as it can greatly release human labor from searching
over the large database of laws. However, current researches only support
coarse-grained recommendation where all relevant articles are predicted as a
whole without explaining which specific fact each article is relevant with.
Since one case can be formed of many supporting facts, traversing over them to
verify the correctness of recommendation results can be time-consuming. We
believe that learning fine-grained correspondence between each single fact and
law articles is crucial for an accurate and trustworthy AI system. With this
motivation, we perform a pioneering study and create a corpus with manually
annotated fact-article correspondences. We treat the learning as a text
matching task and propose a multi-level matching network to address it. To help
the model better digest the content of law articles, we parse articles in form
of premise-conclusion pairs with random forest. Experiments show that the
parsed form yielded better performance and the resulting model surpassed other
popular text matching baselines. Furthermore, we compare with previous
researches and find that establishing the fine-grained fact-article
correspondences can improve the recommendation accuracy by a large margin. Our
best system reaches an F1 score of 96.3%, making it of great potential for
practical use. It can also significantly boost the downstream task of legal
decision prediction, increasing the F1 score by up to 12.7%.
| 2,021 |
Computation and Language
|
Accented Speech Recognition: A Survey
|
Automatic Speech Recognition (ASR) systems generalize poorly on accented
speech. The phonetic and linguistic variability of accents present hard
challenges for ASR systems today in both data collection and modeling
strategies. The resulting bias in ASR performance across accents comes at a
cost to both users and providers of ASR.
We present a survey of current promising approaches to accented speech
recognition and highlight the key challenges in the space. Approaches mostly
focus on single model generalization and accent feature engineering. Among the
challenges, lack of a standard benchmark makes research and comparison
especially difficult.
| 2,021 |
Computation and Language
|
Disfluency Detection with Unlabeled Data and Small BERT Models
|
Disfluency detection models now approach high accuracy on English text.
However, little exploration has been done in improving the size and inference
time of the model. At the same time, automatic speech recognition (ASR) models
are moving from server-side inference to local, on-device inference. Supporting
models in the transcription pipeline (like disfluency detection) must follow
suit. In this work we concentrate on the disfluency detection task, focusing on
small, fast, on-device models based on the BERT architecture. We demonstrate it
is possible to train disfluency detection models as small as 1.3 MiB, while
retaining high performance. We build on previous work that showed the benefit
of data augmentation approaches such as self-training. Then, we evaluate the
effect of domain mismatch between conversational and written text on model
performance. We find that domain adaptation and data augmentation strategies
have a more pronounced effect on these smaller models, as compared to
conventional BERT models.
| 2,021 |
Computation and Language
|
Extracting Adverse Drug Events from Clinical Notes
|
Adverse drug events (ADEs) are unexpected incidents caused by the
administration of a drug or medication. To identify and extract these events,
we require information about not just the drug itself but attributes describing
the drug (e.g., strength, dosage), the reason why the drug was initially
prescribed, and any adverse reaction to the drug. This paper explores the
relationship between a drug and its associated attributes using relation
extraction techniques. We explore three approaches: a rule-based approach, a
deep learning-based approach, and a contextualized language model-based
approach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our
experimental results demonstrate that the contextualized language model-based
approach outperformed other models overall and obtain the state-of-the-art
performance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an
$F_1$ score of 0.94; however, for certain relation types, the rule-based
approach obtained a higher Precision and Recall than either learning approach.
| 2,021 |
Computation and Language
|
Provable Limitations of Acquiring Meaning from Ungrounded Form: What
Will Future Language Models Understand?
|
Language models trained on billions of tokens have recently led to
unprecedented results on many NLP tasks. This success raises the question of
whether, in principle, a system can ever ``understand'' raw text without access
to some form of grounding. We formally investigate the abilities of ungrounded
systems to acquire meaning. Our analysis focuses on the role of ``assertions'':
textual contexts that provide indirect clues about the underlying semantics. We
study whether assertions enable a system to emulate representations preserving
semantic relations like equivalence. We find that assertions enable semantic
emulation of languages that satisfy a strong notion of semantic transparency.
However, for classes of languages where the same expression can take different
values in different contexts, we show that emulation can become uncomputable.
Finally, we discuss differences between our formal model and natural language,
exploring how our results generalize to a modal setting and other semantic
relations. Together, our results suggest that assertions in code or language do
not provide sufficient signal to fully emulate semantic representations. We
formalize ways in which ungrounded language models appear to be fundamentally
limited in their ability to ``understand''.
| 2,021 |
Computation and Language
|
A Short Survey of Pre-trained Language Models for Conversational AI-A
NewAge in NLP
|
Building a dialogue system that can communicate naturally with humans is a
challenging yet interesting problem of agent-based computing. The rapid growth
in this area is usually hindered by the long-standing problem of data scarcity
as these systems are expected to learn syntax, grammar, decision making, and
reasoning from insufficient amounts of task-specific dataset. The recently
introduced pre-trained language models have the potential to address the issue
of data scarcity and bring considerable advantages by generating contextualized
word embeddings. These models are considered counterpart of ImageNet in NLP and
have demonstrated to capture different facets of language such as hierarchical
relations, long-term dependency, and sentiment. In this short survey paper, we
discuss the recent progress made in the field of pre-trained language models.
We also deliberate that how the strengths of these language models can be
leveraged in designing more engaging and more eloquent conversational agents.
This paper, therefore, intends to establish whether these pre-trained models
can overcome the challenges pertinent to dialogue systems, and how their
architecture could be exploited in order to overcome these challenges. Open
challenges in the field of dialogue systems have also been deliberated.
| 2,021 |
Computation and Language
|
Finding Fuzziness in Neural Network Models of Language Processing
|
Humans often communicate by using imprecise language, suggesting that fuzzy
concepts with unclear boundaries are prevalent in language use. In this paper,
we test the extent to which models trained to capture the distributional
statistics of language show correspondence to fuzzy-membership patterns. Using
the task of natural language inference, we test a recent state of the art model
on the classical case of temperature, by examining its mapping of temperature
data to fuzzy-perceptions such as "cool", "hot", etc. We find the model to show
patterns that are similar to classical fuzzy-set theoretic formulations of
linguistic hedges, albeit with a substantial amount of noise, suggesting that
models trained solely on language show promise in encoding fuzziness.
| 2,021 |
Computation and Language
|
Fuzzy Classification of Multi-intent Utterances
|
Current intent classification approaches assign binary intent class
memberships to natural language utterances while disregarding the inherent
vagueness in language and the corresponding vagueness in intent class
boundaries. In this work, we propose a scheme to address the ambiguity in
single-intent as well as multi-intent natural language utterances by creating
degree memberships over fuzzified intent classes. To our knowledge, this is the
first work to address and quantify the impact of the fuzzy nature of natural
language utterances over intent category memberships. Additionally, our
approach overcomes the sparsity of multi-intent utterance data to train
classification models by using a small database of single intent utterances to
generate class memberships over multi-intent utterances. We evaluate our
approach over two task-oriented dialog datasets, across different fuzzy
membership generation techniques and approximate string similarity measures.
Our results reveal the impact of lexical overlap between utterances of
different intents, and the underlying data distributions, on the fuzzification
of intent memberships. Moreover, we evaluate the accuracy of our approach by
comparing the defuzzified memberships to their binary counterparts, across
different combinations of membership functions and string similarity measures.
| 2,021 |
Computation and Language
|
Low Anisotropy Sense Retrofitting (LASeR) : Towards Isotropic and Sense
Enriched Representations
|
Contextual word representation models have shown massive improvements on a
multitude of NLP tasks, yet their word sense disambiguation capabilities remain
poorly explained. To address this gap, we assess whether contextual word
representations extracted from deep pretrained language models create
distinguishable representations for different senses of a given word. We
analyze the representation geometry and find that most layers of deep
pretrained language models create highly anisotropic representations, pointing
towards the existence of representation degeneration problem in contextual word
representations. After accounting for anisotropy, our study further reveals
that there is variability in sense learning capabilities across different
language models. Finally, we propose LASeR, a 'Low Anisotropy Sense
Retrofitting' approach that renders off-the-shelf representations isotropic and
semantically more meaningful, resolving the representation degeneration problem
as a post-processing step, and conducting sense-enrichment of contextualized
representations extracted from deep neural language models.
| 2,021 |
Computation and Language
|
Enriched Attention for Robust Relation Extraction
|
The performance of relation extraction models has increased considerably with
the rise of neural networks. However, a key issue of neural relation extraction
is robustness: the models do not scale well to long sentences with multiple
entities and relations. In this work, we address this problem with an enriched
attention mechanism. Attention allows the model to focus on parts of the input
sentence that are relevant to relation extraction. We propose to enrich the
attention function with features modeling knowledge about the relation
arguments and the shortest dependency path between them. Thus, for different
relation arguments, the model can pay attention to different parts of the
sentence. Our model outperforms prior work using comparable setups on two
popular benchmarks, and our analysis confirms that it indeed scales to long
sentences with many entities.
| 2,021 |
Computation and Language
|
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of
Media Frames
|
Understanding how news media frame political issues is important due to its
impact on public attitudes, yet hard to automate. Computational approaches have
largely focused on classifying the frame of a full news article while framing
signals are often subtle and local. Furthermore, automatic news analysis is a
sensitive domain, and existing classifiers lack transparency in their
predictions. This paper addresses both issues with a novel semi-supervised
model, which jointly learns to embed local information about the events and
related actors in a news article through an auto-encoding framework, and to
leverage this signal for document-level frame classification. Our experiments
show that: our model outperforms previous models of frame prediction; we can
further improve performance with unlabeled training data leveraging the
semi-supervised nature of our model; and the learnt event and actor embeddings
intuitively corroborate the document-level predictions, providing a nuanced and
interpretable article frame representation.
| 2,021 |
Computation and Language
|
Adapting Long Context NLM for ASR Rescoring in Conversational Agents
|
Neural Language Models (NLM), when trained and evaluated with context
spanning multiple utterances, have been shown to consistently outperform both
conventional n-gram language models and NLMs that use limited context. In this
paper, we investigate various techniques to incorporate turn based context
history into both recurrent (LSTM) and Transformer-XL based NLMs. For recurrent
based NLMs, we explore context carry over mechanism and feature based
augmentation, where we incorporate other forms of contextual information such
as bot response and system dialogue acts as classified by a Natural Language
Understanding (NLU) model. To mitigate the sharp nearby, fuzzy far away problem
with contextual NLM, we propose the use of attention layer over lexical
metadata to improve feature based augmentation. Additionally, we adapt our
contextual NLM towards user provided on-the-fly speech patterns by leveraging
encodings from a large pre-trained masked language model and performing fusion
with a Transformer-XL based NLM. We test our proposed models using N-best
rescoring of ASR hypotheses of task-oriented dialogues and also evaluate on
downstream NLU tasks such as intent classification and slot labeling. The best
performing model shows a relative WER between 1.6% and 9.1% and a slot labeling
F1 score improvement of 4% over non-contextual baselines.
| 2,021 |
Computation and Language
|
Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network
|
Adaption of end-to-end speech recognition systems to new tasks is known to be
challenging. A number of solutions have been proposed which apply external
language models with various fusion methods, possibly with a combination of
two-pass decoding. Also TTS systems have been used to generate adaptation data
for the end-to-end models. In this paper we show that RNN-transducer models can
be effectively adapted to new domains using only small amounts of textual data.
By taking advantage of model's inherent structure, where the prediction network
is interpreted as a language model, we can apply fast adaptation to the model.
Adapting the model avoids the need for complicated decoding time fusions and
external language models. Using appropriate regularization, the prediction
network can be adapted to new domains while still retaining good generalization
capabilities. We show with multiple ASR evaluation tasks how this method can
provide relative gains of 10-45% in target task WER. We also share insights how
RNN-transducer prediction network performs as a language model.
| 2,021 |
Computation and Language
|
Earnings-21: A Practical Benchmark for ASR in the Wild
|
Commonly used speech corpora inadequately challenge academic and commercial
ASR systems. In particular, speech corpora lack metadata needed for detailed
analysis and WER measurement. In response, we present Earnings-21, a 39-hour
corpus of earnings calls containing entity-dense speech from nine different
financial sectors. This corpus is intended to benchmark ASR systems in the wild
with special attention towards named entity recognition. We benchmark four
commercial ASR models, two internal models built with open-source tools, and an
open-source LibriSpeech model and discuss their differences in performance on
Earnings-21. Using our recently released fstalign tool, we provide a candid
analysis of each model's recognition capabilities under different partitions.
Our analysis finds that ASR accuracy for certain NER categories is poor,
presenting a significant impediment to transcript comprehension and usage.
Earnings-21 bridges academic and commercial ASR system evaluation and enables
further research on entity modeling and WER on real world audio.
| 2,022 |
Computation and Language
|
Transfer training from smaller language model
|
Large language models have led to state-of-the-art accuracies across a range
of tasks. However,training large language model needs massive computing
resource, as more and more open source pre-training models are available, it is
worthy to study how to take full advantage of available model. We find a method
to save training time and resource cost by changing the small well-trained
model to large model. We initialize a larger target model from a smaller source
model by copy weight values from source model and padding with zeros or small
initialization values on it to make the source and target model have
approximate outputs, which is valid due to block matrix multiplication and
residual connection in transformer structure. We test the target model on
several data sets and find it is still comparable with the source model. When
we continue training the target model, the training loss can start from a
smaller value.
| 2,021 |
Computation and Language
|
BERT-CoQAC: BERT-based Conversational Question Answering in Context
|
As one promising way to inquire about any particular information through a
dialog with the bot, question answering dialog systems have gained increasing
research interests recently. Designing interactive QA systems has always been a
challenging task in natural language processing and used as a benchmark to
evaluate a machine's ability of natural language understanding. However, such
systems often struggle when the question answering is carried out in multiple
turns by the users to seek more information based on what they have already
learned, thus, giving rise to another complicated form called Conversational
Question Answering (CQA). CQA systems are often criticized for not
understanding or utilizing the previous context of the conversation when
answering the questions. To address the research gap, in this paper, we explore
how to integrate conversational history into the neural machine comprehension
system. On one hand, we introduce a framework based on a publically available
pre-trained language model called BERT for incorporating history turns into the
system. On the other hand, we propose a history selection mechanism that
selects the turns that are relevant and contributes the most to answer the
current question. Experimentation results revealed that our framework is
comparable in performance with the state-of-the-art models on the QuAC leader
board. We also conduct a number of experiments to show the side effects of
using entire context information which brings unnecessary information and noise
signals resulting in a decline in the model's performance.
| 2,021 |
Computation and Language
|
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised
Representation Learning from Speech
|
Self-Supervised Learning (SSL) using huge unlabeled data has been
successfully explored for image and natural language processing. Recent works
also investigated SSL from speech. They were notably successful to improve
performance on downstream tasks such as automatic speech recognition (ASR).
While these works suggest it is possible to reduce dependence on labeled data
for building efficient speech systems, their evaluation was mostly made on ASR
and using multiple and heterogeneous experimental settings (most of them for
English). This questions the objective comparison of SSL approaches and the
evaluation of their impact on building speech systems. In this paper, we
propose LeBenchmark: a reproducible framework for assessing SSL from speech. It
not only includes ASR (high and low resource) tasks but also spoken language
understanding, speech translation and emotion recognition. We also focus on
speech technologies in a language different than English: French. SSL models of
different sizes are trained from carefully sourced and documented datasets.
Experiments show that SSL is beneficial for most but not all tasks which
confirms the need for exhaustive and reliable benchmarks to evaluate its real
impact. LeBenchmark is shared with the scientific community for reproducible
research in SSL from speech.
| 2,021 |
Computation and Language
|
Multimodal Fusion with BERT and Attention Mechanism for Fake News
Detection
|
Fake news detection is an important task for increasing the credibility of
information on the media since fake news is constantly spreading on social
media every day and it is a very serious concern in our society. Fake news is
usually created by manipulating images, texts, and videos. In this paper, we
present a novel method for detecting fake news by fusing multimodal features
derived from textual and visual data. Specifically, we used a pre-trained BERT
model to learn text features and a VGG-19 model pre-trained on the ImageNet
dataset to extract image features. We proposed a scale-dot product attention
mechanism to capture the relationship between text features and visual
features. Experimental results showed that our approach performs better than
the current state-of-the-art method on a public Twitter dataset by 3.1%
accuracy.
| 2,021 |
Computation and Language
|
Learning to Learn to be Right for the Right Reasons
|
Improving model generalization on held-out data is one of the core objectives
in commonsense reasoning. Recent work has shown that models trained on the
dataset with superficial cues tend to perform well on the easy test set with
superficial cues but perform poorly on the hard test set without superficial
cues. Previous approaches have resorted to manual methods of encouraging models
not to overfit to superficial cues. While some of the methods have improved
performance on hard instances, they also lead to degraded performance on easy
instances. Here, we propose to explicitly learn a model that does well on both
the easy test set with superficial cues and hard test set without superficial
cues. Using a meta-learning objective, we learn such a model that improves
performance on both the easy test set and the hard test set. By evaluating our
models on Choice of Plausible Alternatives (COPA) and Commonsense Explanation,
we show that our proposed method leads to improved performance on both the easy
test set and the hard test set upon which we observe up to 16.5 percentage
points improvement over the baseline.
| 2,021 |
Computation and Language
|
Deep learning for sentence clustering in essay grading support
|
Essays as a form of assessment test student knowledge on a deeper level than
short answer and multiple-choice questions. However, the manual evaluation of
essays is time- and labor-consuming. Automatic clustering of essays, or their
fragments, prior to manual evaluation presents a possible solution to reducing
the effort required in the evaluation process. Such clustering presents
numerous challenges due to the variability and ambiguity of natural language.
In this paper, we introduce two datasets of undergraduate student essays in
Finnish, manually annotated for salient arguments on the sentence level. Using
these datasets, we evaluate several deep-learning embedding methods for their
suitability to sentence clustering in support of essay grading. We find that
the choice of the most suitable method depends on the nature of the exam
question and the answers, with deep-learning methods being capable of, but not
guaranteeing better performance over simpler methods based on lexical overlap.
| 2,021 |
Computation and Language
|
Optimizing small BERTs trained for German NER
|
Currently, the most widespread neural network architecture for training
language models is the so called BERT which led to improvements in various
Natural Language Processing (NLP) tasks. In general, the larger the number of
parameters in a BERT model, the better the results obtained in these NLP tasks.
Unfortunately, the memory consumption and the training duration drastically
increases with the size of these models. In this article, we investigate
various training techniques of smaller BERT models: We combine different
methods from other BERT variants like ALBERT, RoBERTa, and relative positional
encoding. In addition, we propose two new fine-tuning modifications leading to
better performance: Class-Start-End tagging and a modified form of Linear Chain
Conditional Random Fields. Furthermore, we introduce Whole-Word Attention which
reduces BERTs memory usage and leads to a small increase in performance
compared to classical Multi-Head-Attention. We evaluate these techniques on
five public German Named Entity Recognition (NER) tasks of which two are
introduced by this article.
| 2,021 |
Computation and Language
|
Weakly-supervised Multi-task Learning for Multimodal Affect Recognition
|
Multimodal affect recognition constitutes an important aspect for enhancing
interpersonal relationships in human-computer interaction. However, relevant
data is hard to come by and notably costly to annotate, which poses a
challenging barrier to build robust multimodal affect recognition systems.
Models trained on these relatively small datasets tend to overfit and the
improvement gained by using complex state-of-the-art models is marginal
compared to simple baselines. Meanwhile, there are many different multimodal
affect recognition datasets, though each may be small. In this paper, we
propose to leverage these datasets using weakly-supervised multi-task learning
to improve the generalization performance on each of them. Specifically, we
explore three multimodal affect recognition tasks: 1) emotion recognition; 2)
sentiment analysis; and 3) sarcasm recognition. Our experimental results show
that multi-tasking can benefit all these tasks, achieving an improvement up to
2.9% accuracy and 3.3% F1-score. Furthermore, our method also helps to improve
the stability of model performance. In addition, our analysis suggests that
weak supervision can provide a comparable contribution to strong supervision if
the tasks are highly correlated.
| 2,021 |
Computation and Language
|
QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific
Claim Verification
|
Scientific claim verification is a unique challenge that is attracting
increasing interest. The SCIVER shared task offers a benchmark scenario to test
and compare claim verification approaches by participating teams and consists
in three steps: relevant abstract selection, rationale selection and label
prediction. In this paper, we present team QMUL-SDS's participation in the
shared task. We propose an approach that performs scientific claim verification
by doing binary classifications step-by-step. We trained a BioBERT-large
classifier to select abstracts based on pairwise relevance assessments for each
<claim, title of the abstract> and continued to train it to select rationales
out of each retrieved abstract based on <claim, sentence>. We then propose a
two-step setting for label prediction, i.e. first predicting "NOT_ENOUGH_INFO"
or "ENOUGH_INFO", then label those marked as "ENOUGH_INFO" as either "SUPPORT"
or "CONTRADICT". Compared to the baseline system, we achieve substantial
improvements on the dev set. As a result, our team is the No. 4 team on the
leaderboard.
| 2,021 |
Computation and Language
|
Understanding who uses Reddit: Profiling individuals with a
self-reported bipolar disorder diagnosis
|
Recently, research on mental health conditions using public online data,
including Reddit, has surged in NLP and health research but has not reported
user characteristics, which are important to judge generalisability of
findings. This paper shows how existing NLP methods can yield information on
clinical, demographic, and identity characteristics of almost 20K Reddit users
who self-report a bipolar disorder diagnosis. This population consists of
slightly more feminine- than masculine-gendered mainly young or middle-aged
US-based adults who often report additional mental health diagnoses, which is
compared with general Reddit statistics and epidemiological studies.
Additionally, this paper carefully evaluates all methods and discusses ethical
issues.
| 2,021 |
Computation and Language
|
Claim Detection in Biomedical Twitter Posts
|
Social media contains unfiltered and unique information, which is potentially
of great value, but, in the case of misinformation, can also do great harm.
With regards to biomedical topics, false information can be particularly
dangerous. Methods of automatic fact-checking and fake news detection address
this problem, but have not been applied to the biomedical domain in social
media yet. We aim to fill this research gap and annotate a corpus of 1200
tweets for implicit and explicit biomedical claims (the latter also with span
annotations for the claim phrase). With this corpus, which we sample to be
related to COVID-19, measles, cystic fibrosis, and depression, we develop
baseline models which detect tweets that contain a claim automatically. Our
analyses reveal that biomedical tweets are densely populated with claims (45 %
in a corpus sampled to contain 1200 tweets focused on the domains mentioned
above). Baseline classification experiments with embedding-based classifiers
and BERT-based transfer learning demonstrate that the detection is challenging,
however, shows acceptable performance for the identification of explicit
expressions of claims. Implicit claim tweets are more challenging to detect.
| 2,021 |
Computation and Language
|
Turkish Text Classification: From Lexicon Analysis to Bidirectional
Transformer
|
Text classification has seen an increased use in both academic and industry
settings. Though rule based methods have been fairly successful, supervised
machine learning has been shown to be most successful for most languages, where
most research was done on English. In this article, the success of lexicon
analysis, support vector machines, and extreme gradient boosting for the task
of text classification and sentiment analysis are evaluated in Turkish and a
pretrained transformer based classifier is proposed, outperforming previous
methods for Turkish text classification. In the context of text classification,
all machine learning models proposed in the article are domain-independent and
do not require any task-specific modifications.
| 2,021 |
Computation and Language
|
Interventional Aspect-Based Sentiment Analysis
|
Recent neural-based aspect-based sentiment analysis approaches, though
achieving promising improvement on benchmark datasets, have reported suffering
from poor robustness when encountering confounder such as non-target aspects.
In this paper, we take a causal view to addressing this issue. We propose a
simple yet effective method, namely, Sentiment Adjustment (SENTA), by applying
a backdoor adjustment to disentangle those confounding factors. Experimental
results on the Aspect Robustness Test Set (ARTS) dataset demonstrate that our
approach improves the performance while maintaining accuracy in the original
test set.
| 2,021 |
Computation and Language
|
Evaluating Deception Detection Model Robustness To Linguistic Variation
|
With the increasing use of machine-learning driven algorithmic judgements, it
is critical to develop models that are robust to evolving or manipulated
inputs. We propose an extensive analysis of model robustness against linguistic
variation in the setting of deceptive news detection, an important task in the
context of misinformation spread online. We consider two prediction tasks and
compare three state-of-the-art embeddings to highlight consistent trends in
model performance, high confidence misclassifications, and high impact
failures. By measuring the effectiveness of adversarial defense strategies and
evaluating model susceptibility to adversarial attacks using character- and
word-perturbed text, we find that character or mixed ensemble models are the
most effective defenses and that character perturbation-based attack tactics
are more successful.
| 2,021 |
Computation and Language
|
Towards Trustworthy Deception Detection: Benchmarking Model Robustness
across Domains, Modalities, and Languages
|
Evaluating model robustness is critical when developing trustworthy models
not only to gain deeper understanding of model behavior, strengths, and
weaknesses, but also to develop future models that are generalizable and robust
across expected environments a model may encounter in deployment. In this paper
we present a framework for measuring model robustness for an important but
difficult text classification task - deceptive news detection. We evaluate
model robustness to out-of-domain data, modality-specific features, and
languages other than English.
Our investigation focuses on three type of models: LSTM models trained on
multiple datasets(Cross-Domain), several fusion LSTM models trained with images
and text and evaluated with three state-of-the-art embeddings, BERT ELMo, and
GloVe (Cross-Modality), and character-level CNN models trained on multiple
languages (Cross-Language). Our analyses reveal a significant drop in
performance when testing neural models on out-of-domain data and non-English
languages that may be mitigated using diverse training data. We find that with
additional image content as input, ELMo embeddings yield significantly fewer
errors compared to BERT orGLoVe. Most importantly, this work not only carefully
analyzes deception model robustness but also provides a framework of these
analyses that can be applied to new models or extended datasets in the future.
| 2,020 |
Computation and Language
|
On a Utilitarian Approach to Privacy Preserving Text Generation
|
Differentially-private mechanisms for text generation typically add carefully
calibrated noise to input words and use the nearest neighbor to the noised
input as the output word. When the noise is small in magnitude, these
mechanisms are susceptible to reconstruction of the original sensitive text.
This is because the nearest neighbor to the noised input is likely to be the
original input. To mitigate this empirical privacy risk, we propose a novel
class of differentially private mechanisms that parameterizes the nearest
neighbor selection criterion in traditional mechanisms. Motivated by Vickrey
auction, where only the second highest price is revealed and the highest price
is kept private, we balance the choice between the first and the second nearest
neighbors in the proposed class of mechanisms using a tuning parameter. This
parameter is selected by empirically solving a constrained optimization problem
for maximizing utility, while maintaining the desired privacy guarantees. We
argue that this empirical measurement framework can be used to align different
mechanisms along a common benchmark for their privacy-utility tradeoff,
particularly when different distance metrics are used to calibrate the amount
of noise added. Our experiments on real text classification datasets show up to
50% improvement in utility compared to the existing state-of-the-art with the
same empirical privacy guarantee.
| 2,021 |
Computation and Language
|
Incremental Few-shot Text Classification with Multi-round New Classes:
Formulation, Dataset and System
|
Text classification is usually studied by labeling natural language texts
with relevant categories from a predefined set. In the real world, new classes
might keep challenging the existing system with limited labeled data. The
system should be intelligent enough to recognize upcoming new classes with a
few examples. In this work, we define a new task in the NLP domain, incremental
few-shot text classification, where the system incrementally handles multiple
rounds of new classes. For each round, there is a batch of new classes with a
few labeled examples per class. Two major challenges exist in this new task:
(i) For the learning process, the system should incrementally learn new classes
round by round without re-training on the examples of preceding classes; (ii)
For the performance, the system should perform well on new classes without much
loss on preceding classes. In addition to formulating the new task, we also
release two benchmark datasets in the incremental few-shot setting: intent
classification and relation classification. Moreover, we propose two entailment
approaches, ENTAILMENT and HYBRID, which show promise for solving this novel
problem.
| 2,021 |
Computation and Language
|
Modeling Coverage for Non-Autoregressive Neural Machine Translation
|
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant
inference speedup by generating all tokens simultaneously. Despite its high
efficiency, NAT usually suffers from two kinds of translation errors:
over-translation (e.g. repeated tokens) and under-translation (e.g. missing
translations), which eventually limits the translation quality. In this paper,
we argue that these issues of NAT can be addressed through coverage modeling,
which has been proved to be useful in autoregressive decoding. We propose a
novel Coverage-NAT to model the coverage information directly by a token-level
coverage iterative refinement mechanism and a sentence-level coverage
agreement, which can remind the model if a source token has been translated or
not and improve the semantics consistency between the translation and the
source, respectively. Experimental results on WMT14 En-De and WMT16 En-Ro
translation tasks show that our method can alleviate those errors and achieve
strong improvements over the baseline system.
| 2,021 |
Computation and Language
|
Extract then Distill: Efficient and Effective Task-Agnostic BERT
Distillation
|
Task-agnostic knowledge distillation, a teacher-student framework, has been
proved effective for BERT compression. Although achieving promising results on
NLP tasks, it requires enormous computational resources. In this paper, we
propose Extract Then Distill (ETD), a generic and flexible strategy to reuse
the teacher's parameters for efficient and effective task-agnostic
distillation, which can be applied to students of any size. Specifically, we
introduce two variants of ETD, ETD-Rand and ETD-Impt, which extract the
teacher's parameters in a random manner and by following an importance metric
respectively. In this way, the student has already acquired some knowledge at
the beginning of the distillation process, which makes the distillation process
converge faster. We demonstrate the effectiveness of ETD on the GLUE benchmark
and SQuAD. The experimental results show that: (1) compared with the baseline
without an ETD strategy, ETD can save 70\% of computation cost. Moreover, it
achieves better results than the baseline when using the same computing
resource. (2) ETD is generic and has been proven effective for different
distillation methods (e.g., TinyBERT and MiniLM) and students of different
sizes. The source code will be publicly available upon publication.
| 2,021 |
Computation and Language
|
Vietnamese Complaint Detection on E-Commerce Websites
|
Customer product reviews play a role in improving the quality of products and
services for business organizations or their brands. Complaining is an attitude
that expresses dissatisfaction with an event or a product not meeting customer
expectations. In this paper, we build a Open-domain Complaint Detection dataset
(UIT-ViOCD), including 5,485 human-annotated reviews on four categories about
product reviews on e-commerce sites. After the data collection phase, we
proceed to the annotation task and achieve the inter-annotator agreement Am of
87%. Then, we present an extensive methodology for the research purposes and
achieve 92.16% by F1-score for identifying complaints. With the results, in the
future, we aim to build a system for open-domain complaint detection in
E-commerce websites.
| 2,021 |
Computation and Language
|
Open Intent Discovery through Unsupervised Semantic Clustering and
Dependency Parsing
|
Intent understanding plays an important role in dialog systems, and is
typically formulated as a supervised learning problem. However, it is
challenging and time-consuming to design the intents for a new domain from
scratch, which usually requires a lot of manual effort of domain experts. This
paper presents an unsupervised two-stage approach to discover intents and
generate meaningful intent labels automatically from a collection of unlabeled
utterances in a domain. In the first stage, we aim to generate a set of
semantically coherent clusters where the utterances within each cluster convey
the same intent. We obtain the utterance representation from various
pre-trained sentence embeddings and present a metric of balanced score to
determine the optimal number of clusters in K-means clustering for balanced
datasets. In the second stage, the objective is to generate an intent label
automatically for each cluster. We extract the ACTION-OBJECT pair from each
utterance using a dependency parser and take the most frequent pair within each
cluster, e.g., book-restaurant, as the generated intent label. We empirically
show that the proposed unsupervised approach can generate meaningful intent
labels automatically and achieve high precision and recall in utterance
clustering and intent discovery.
| 2,021 |
Computation and Language
|
Automatic Post-Editing for Vietnamese
|
Automatic post-editing (APE) is an important remedy for reducing errors of
raw translated texts that are produced by machine translation (MT) systems or
software-aided translation. In this paper, we present a systematic approach to
tackle the APE task for Vietnamese. Specifically, we construct the first
large-scale dataset of 5M Vietnamese translated and corrected sentence pairs.
We then apply strong neural MT models to handle the APE task, using our
constructed dataset. Experimental results from both automatic and human
evaluations show the effectiveness of the neural MT models in handling the
Vietnamese APE task.
| 2,021 |
Computation and Language
|
Transformers to Fight the COVID-19 Infodemic
|
The massive spread of false information on social media has become a global
risk especially in a global pandemic situation like COVID-19. False information
detection has thus become a surging research topic in recent months.
NLP4IF-2021 shared task on fighting the COVID-19 infodemic has been organised
to strengthen the research in false information detection where the
participants are asked to predict seven different binary labels regarding false
information in a tweet. The shared task has been organised in three languages;
Arabic, Bulgarian and English. In this paper, we present our approach to tackle
the task objective using transformers. Overall, our approach achieves a 0.707
mean F1 score in Arabic, 0.578 mean F1 score in Bulgarian and 0.864 mean F1
score in English ranking 4th place in all the languages.
| 2,021 |
Computation and Language
|
Identifying Offensive Expressions of Opinion in Context
|
Classic information extraction techniques consist in building questions and
answers about the facts. Indeed, it is still a challenge to subjective
information extraction systems to identify opinions and feelings in context. In
sentiment-based NLP tasks, there are few resources to information extraction,
above all offensive or hateful opinions in context. To fill this important gap,
this short paper provides a new cross-lingual and contextual offensive lexicon,
which consists of explicit and implicit offensive and swearing expressions of
opinion, which were annotated in two different classes: context dependent and
context-independent offensive. In addition, we provide markers to identify hate
speech. Annotation approach was evaluated at the expression-level and achieves
high human inter-annotator agreement. The provided offensive lexicon is
available in Portuguese and English languages.
| 2,022 |
Computation and Language
|
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis
and Beyond
|
Language models are ubiquitous in current NLP, and their multilingual
capacity has recently attracted considerable attention. However, current
analyses have almost exclusively focused on (multilingual variants of) standard
benchmarks, and have relied on clean pre-training and task-specific corpora as
multilingual signals. In this paper, we introduce XLM-T, a model to train and
evaluate multilingual language models in Twitter. In this paper we provide: (1)
a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020)
model pre-trained on millions of tweets in over thirty languages, alongside
starter code to subsequently fine-tune on a target task; and (2) a set of
unified sentiment analysis Twitter datasets in eight different languages and a
XLM-T model fine-tuned on them.
| 2,022 |
Computation and Language
|
Contextual-Lexicon Approach for Abusive Language Detection
|
Since a lexicon-based approach is more elegant scientifically, explaining the
solution components and being easier to generalize to other applications, this
paper provides a new approach for offensive language and hate speech detection
on social media. Our approach embodies a lexicon of implicit and explicit
offensive and swearing expressions annotated with contextual information. Due
to the severity of the social media abusive comments in Brazil, and the lack of
research in Portuguese, Brazilian Portuguese is the language used to validate
the models. Nevertheless, our method may be applied to any other language. The
conducted experiments show the effectiveness of the proposed approach,
outperforming the current baseline methods for the Portuguese language.
| 2,022 |
Computation and Language
|
A Bi-Encoder LSTM Model For Learning Unstructured Dialogs
|
Creating a data-driven model that is trained on a large dataset of
unstructured dialogs is a crucial step in developing Retrieval-based Chatbot
systems. This paper presents a Long Short Term Memory (LSTM) based architecture
that learns unstructured multi-turn dialogs and provides results on the task of
selecting the best response from a collection of given responses. Ubuntu Dialog
Corpus Version 2 was used as the corpus for training. We show that our model
achieves 0.8%, 1.0% and 0.3% higher accuracy for Recall@1, Recall@2 and
Recall@5 respectively than the benchmark model. We also show results on
experiments performed by using several similarity functions, model
hyper-parameters and word embeddings on the proposed architecture
| 2,021 |
Computation and Language
|
Reranking Machine Translation Hypotheses with Structured and Web-based
Language Models
|
In this paper, we investigate the use of linguistically motivated and
computationally efficient structured language models for reranking N-best
hypotheses in a statistical machine translation system. These language models,
developed from Constraint Dependency Grammar parses, tightly integrate
knowledge of words, morphological and lexical features, and syntactic
dependency constraints. Two structured language models are applied for N-best
rescoring, one is an almost-parsing language model, and the other utilizes more
syntactic features by explicitly modeling syntactic dependencies between words.
We also investigate effective and efficient language modeling methods to use
N-grams extracted from up to 1 teraword of web documents. We apply all these
language models for N-best re-ranking on the NIST and DARPA GALE program 2006
and 2007 machine translation evaluation tasks and find that the combination of
these language models increases the BLEU score up to 1.6% absolutely on blind
test sets.
| 2,007 |
Computation and Language
|
A Sliding-Window Approach to Automatic Creation of Meeting Minutes
|
Meeting minutes record any subject matters discussed, decisions reached and
actions taken at meetings. The importance of minuting cannot be overemphasized
in a time when a significant number of meetings take place in the virtual
space. In this paper, we present a sliding window approach to automatic
generation of meeting minutes. It aims to tackle issues associated with the
nature of spoken text, including lengthy transcripts and lack of document
structure, which make it difficult to identify salient content to be included
in the meeting minutes. Our approach combines a sliding window and a neural
abstractive summarizer to navigate through the transcripts to find salient
content. The approach is evaluated on transcripts of natural meeting
conversations, where we compare results obtained for human transcripts and two
versions of automatic transcripts and discuss how and to what extent the
summarizer succeeds at capturing salient content.
| 2,021 |
Computation and Language
|
Explore BiLSTM-CRF-Based Models for Open Relation Extraction
|
Extracting multiple relations from text sentences is still a challenge for
current Open Relation Extraction (Open RE) tasks. In this paper, we develop
several Open RE models based on the bidirectional LSTM-CRF (BiLSTM-CRF) neural
network and different contextualized word embedding methods. We also propose a
new tagging scheme to solve overlapping problems and enhance models'
performance. From the evaluation results and comparisons between models, we
select the best combination of tagging scheme, word embedder, and BiLSTM-CRF
network to achieve an Open RE model with a remarkable extracting ability on
multiple-relation sentences.
| 2,021 |
Computation and Language
|
PanGu-$\alpha$: Large-scale Autoregressive Pretrained Chinese Language
Models with Auto-parallel Computation
|
Large-scale Pretrained Language Models (PLMs) have become the new paradigm
for Natural Language Processing (NLP). PLMs with hundreds of billions
parameters such as GPT-3 have demonstrated strong performances on natural
language understanding and generation with \textit{few-shot in-context}
learning. In this work, we present our practice on training large-scale
autoregressive language models named PanGu-$\alpha$, with up to 200 billion
parameters. PanGu-$\alpha$ is developed under the MindSpore and trained on a
cluster of 2048 Ascend 910 AI processors. The training parallelism strategy is
implemented based on MindSpore Auto-parallel, which composes five parallelism
dimensions to scale the training task to 2048 processors efficiently, including
data parallelism, op-level model parallelism, pipeline model parallelism,
optimizer model parallelism and rematerialization. To enhance the
generalization ability of PanGu-$\alpha$, we collect 1.1TB high-quality Chinese
data from a wide range of domains to pretrain the model. We empirically test
the generation ability of PanGu-$\alpha$ in various scenarios including text
summarization, question answering, dialogue generation, etc. Moreover, we
investigate the effect of model scales on the few-shot performances across a
broad range of Chinese NLP tasks. The experimental results demonstrate the
superior capabilities of PanGu-$\alpha$ in performing various tasks under
few-shot or zero-shot settings.
| 2,021 |
Computation and Language
|
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty
Dialogue Machine Reading Comprehension
|
Multiparty Dialogue Machine Reading Comprehension (MRC) differs from
traditional MRC as models must handle the complex dialogue discourse structure,
previously unconsidered in traditional MRC. To fully exploit such discourse
structure in multiparty dialogue, we present a discourse-aware dialogue graph
neural network, DADgraph, which explicitly constructs the dialogue graph using
discourse dependency links and discourse relations. To validate our model, we
perform experiments on the Molweni corpus, a large-scale MRC dataset built over
multiparty dialogue annotated with discourse structure. Experiments on Molweni
show that our discourse-aware model achieves statistically significant
improvements compared against strong neural network MRC baselines.
| 2,021 |
Computation and Language
|
A dissemination workshop for introducing young Italian students to NLP
|
We describe and make available the game-based material developed for a
laboratory run at several Italian science festivals to popularize NLP among
young students.
| 2,021 |
Computation and Language
|
Teaching NLP with Bracelets and Restaurant Menus: An Interactive
Workshop for Italian Students
|
Although Natural Language Processing (NLP) is at the core of many tools young
people use in their everyday life, high school curricula (in Italy) do not
include any computational linguistics education. This lack of exposure makes
the use of such tools less responsible than it could be and makes choosing
computational linguistics as a university degree unlikely. To raise awareness,
curiosity, and longer-term interest in young people, we have developed an
interactive workshop designed to illustrate the basic principles of NLP and
computational linguistics to high school Italian students aged between 13 and
18 years. The workshop takes the form of a game in which participants play the
role of machines needing to solve some of the most common problems a computer
faces in understanding language: from voice recognition to Markov chains to
syntactic parsing. Participants are guided through the workshop with the help
of instructors, who present the activities and explain core concepts from
computational linguistics. The workshop was presented at numerous outlets in
Italy between 2019 and 2021, both face-to-face and online.
| 2,021 |
Computation and Language
|
Attention vs non-attention for a Shapley-based explanation method
|
The field of explainable AI has recently seen an explosion in the number of
explanation methods for highly non-linear deep neural networks. The extent to
which such methods -- that are often proposed and tested in the domain of
computer vision -- are appropriate to address the explainability challenges in
NLP is yet relatively unexplored. In this work, we consider Contextual
Decomposition (CD) -- a Shapley-based input feature attribution method that has
been shown to work well for recurrent NLP models -- and we test the extent to
which it is useful for models that contain attention operations. To this end,
we extend CD to cover the operations necessary for attention-based models. We
then compare how long distance subject-verb relationships are processed by
models with and without attention, considering a number of different syntactic
structures in two different languages: English and Dutch. Our experiments
confirm that CD can successfully be applied for attention-based models as well,
providing an alternative Shapley-based attribution method for modern neural
networks. In particular, using CD, we show that the English and Dutch models
demonstrate similar processing behaviour, but that under the hood there are
consistent differences between our attention and non-attention models.
| 2,021 |
Computation and Language
|
What Makes a Message Persuasive? Identifying Adaptations Towards
Persuasiveness in Nine Exploratory Case Studies
|
The ability to persuade others is critical to professional and personal
success. However, crafting persuasive messages is demanding and poses various
challenges. We conducted nine exploratory case studies to identify adaptations
that professional and non-professional writers make in written scenarios to
increase their subjective persuasiveness. Furthermore, we identified challenges
that those writers faced and identified strategies to resolve them with
persuasive natural language generation, i.e., artificial intelligence. Our
findings show that humans can achieve high degrees of persuasiveness (more so
for professional-level writers), and artificial intelligence can complement
them to achieve increased celerity and alignment in the process.
| 2,021 |
Computation and Language
|
Easy and Efficient Transformer : Scalable Inference Solution For large
NLP model
|
Recently, large-scale transformer-based models have been proven to be
effective over various tasks across many domains. Nevertheless, applying them
in industrial production requires tedious and heavy works to reduce inference
costs. To fill such a gap, we introduce a scalable inference solution: Easy and
Efficient Transformer (EET), including a series of transformer inference
optimization at the algorithm and implementation levels. First, we design
highly optimized kernels for long inputs and large hidden sizes. Second, we
propose a flexible CUDA memory manager to reduce the memory footprint when
deploying a large model. Compared with the state-of-the-art transformer
inference library (Faster Transformer v4.0), EET can achieve an average of
1.40-4.20x speedup on the transformer decoder layer with an A100 GPU
| 2,022 |
Computation and Language
|
Evaluating the Values of Sources in Transfer Learning
|
Transfer learning that adapts a model trained on data-rich sources to
low-resource targets has been widely applied in natural language processing
(NLP). However, when training a transfer model over multiple sources, not every
source is equally useful for the target. To better transfer a model, it is
essential to understand the values of the sources. In this paper, we develop
SEAL-Shap, an efficient source valuation framework for quantifying the
usefulness of the sources (e.g., domains/languages) in transfer learning based
on the Shapley value method. Experiments and comprehensive analyses on both
cross-domain and cross-lingual transfers demonstrate that our framework is not
only effective in choosing useful transfer sources but also the source values
match the intuitive source-target similarity.
| 2,021 |
Computation and Language
|
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need
in MOOC Forums
|
Massive Open Online Courses (MOOCs) have become a popular choice for
e-learning thanks to their great flexibility. However, due to large numbers of
learners and their diverse backgrounds, it is taxing to offer real-time
support. Learners may post their feelings of confusion and struggle in the
respective MOOC forums, but with the large volume of posts and high workloads
for MOOC instructors, it is unlikely that the instructors can identify all
learners requiring intervention. This problem has been studied as a Natural
Language Processing (NLP) problem recently, and is known to be challenging, due
to the imbalance of the data and the complex nature of the task. In this paper,
we explore for the first time Bayesian deep learning on learner-based text
posts with two methods: Monte Carlo Dropout and Variational Inference, as a new
solution to assessing the need of instructor interventions for a learner's
post. We compare models based on our proposed methods with probabilistic
modelling to its baseline non-Bayesian models under similar circumstances, for
different cases of applying prediction. The results suggest that Bayesian deep
learning offers a critical uncertainty measure that is not supplied by
traditional neural networks. This adds more explainability, trust and
robustness to AI, which is crucial in education-based applications.
Additionally, it can achieve similar or better performance compared to
non-probabilistic neural networks, as well as grant lower variance.
| 2,021 |
Computation and Language
|
Non-Parametric Few-Shot Learning for Word Sense Disambiguation
|
Word sense disambiguation (WSD) is a long-standing problem in natural
language processing. One significant challenge in supervised all-words WSD is
to classify among senses for a majority of words that lie in the long-tail
distribution. For instance, 84% of the annotated words have less than 10
examples in the SemCor training data. This issue is more pronounced as the
imbalance occurs in both word and sense distributions. In this work, we propose
MetricWSD, a non-parametric few-shot learning approach to mitigate this data
imbalance issue. By learning to compute distances among the senses of a given
word through episodic training, MetricWSD transfers knowledge (a learned metric
space) from high-frequency words to infrequent ones. MetricWSD constructs the
training episodes tailored to word frequencies and explicitly addresses the
problem of the skewed distribution, as opposed to mixing all the words trained
with parametric models in previous work. Without resorting to any lexical
resources, MetricWSD obtains strong performance against parametric
alternatives, achieving a 75.1 F1 score on the unified WSD evaluation benchmark
(Raganato et al., 2017b). Our analysis further validates that infrequent words
and senses enjoy significant improvement.
| 2,021 |
Computation and Language
|
Focused Attention Improves Document-Grounded Generation
|
Document grounded generation is the task of using the information provided in
a document to improve text generation. This work focuses on two different
document grounded generation tasks: Wikipedia Update Generation task and
Dialogue response generation. Our work introduces two novel adaptations of
large scale pre-trained encoder-decoder models focusing on building context
driven representation of the document and enabling specific attention to the
information in the document. Additionally, we provide a stronger BART baseline
for these tasks. Our proposed techniques outperform existing methods on both
automated (at least 48% increase in BLEU-4 points) and human evaluation for
closeness to reference and relevance to the document. Furthermore, we perform
comprehensive manual inspection of the generated output and categorize errors
to provide insights into future directions in modeling these tasks.
| 2,021 |
Computation and Language
|
GermanQuAD and GermanDPR: Improving Non-English Question Answering and
Passage Retrieval
|
A major challenge of research on non-English machine reading for question
answering (QA) is the lack of annotated datasets. In this paper, we present
GermanQuAD, a dataset of 13,722 extractive question/answer pairs. To improve
the reproducibility of the dataset creation approach and foster QA research on
other languages, we summarize lessons learned and evaluate reformulation of
question/answer pairs as a way to speed up the annotation process. An
extractive QA model trained on GermanQuAD significantly outperforms
multilingual models and also shows that machine-translated training data cannot
fully substitute hand-annotated training data in the target language. Finally,
we demonstrate the wide range of applications of GermanQuAD by adapting it to
GermanDPR, a training dataset for dense passage retrieval (DPR), and train and
evaluate the first non-English DPR model.
| 2,021 |
Computation and Language
|
Auto Response Generation in Online Medical Chat Services
|
Telehealth helps to facilitate access to medical professionals by enabling
remote medical services for the patients. These services have become gradually
popular over the years with the advent of necessary technological
infrastructure. The benefits of telehealth have been even more apparent since
the beginning of the COVID-19 crisis, as people have become less inclined to
visit doctors in person during the pandemic. In this paper, we focus on
facilitating the chat sessions between a doctor and a patient. We note that the
quality and efficiency of the chat experience can be critical as the demand for
telehealth services increases. Accordingly, we develop a smart auto-response
generation mechanism for medical conversations that helps doctors respond to
consultation requests efficiently, particularly during busy sessions. We
explore over 900,000 anonymous, historical online messages between doctors and
patients collected over nine months. We implement clustering algorithms to
identify the most frequent responses by doctors and manually label the data
accordingly. We then train machine learning algorithms using this preprocessed
data to generate the responses. The considered algorithm has two steps: a
filtering (i.e., triggering) model to filter out infeasible patient messages
and a response generator to suggest the top-3 doctor responses for the ones
that successfully pass the triggering phase. The method provides an accuracy of
83.28\% for precision@3 and shows robustness to its parameters.
| 2,022 |
Computation and Language
|
Teaching a Massive Open Online Course on Natural Language Processing
|
This paper presents a new Massive Open Online Course on Natural Language
Processing, targeted at non-English speaking students. The course lasts 12
weeks; every week consists of lectures, practical sessions, and quiz
assignments. Three weeks out of 12 are followed by Kaggle-style coding
assignments.
Our course intends to serve multiple purposes: (i) familiarize students with
the core concepts and methods in NLP, such as language modeling or word or
sentence representations, (ii) show that recent advances, including pre-trained
Transformer-based models, are built upon these concepts; (iii) introduce
architectures for most demanded real-life applications, (iv) develop practical
skills to process texts in multiple languages. The course was prepared and
recorded during 2020, launched by the end of the year, and in early 2021 has
received positive feedback.
| 2,023 |
Computation and Language
|
Morph Call: Probing Morphosyntactic Content of Multilingual Transformers
|
The outstanding performance of transformer-based language models on a great
variety of NLP and NLU tasks has stimulated interest in exploring their inner
workings. Recent research has focused primarily on higher-level and complex
linguistic phenomena such as syntax, semantics, world knowledge, and common
sense. The majority of the studies are anglocentric, and little remains known
regarding other languages, precisely their morphosyntactic properties. To this
end, our work presents Morph Call, a suite of 46 probing tasks for four
Indo-European languages of different morphology: English, French, German and
Russian. We propose a new type of probing task based on the detection of guided
sentence perturbations. We use a combination of neuron-, layer- and
representation-level introspection techniques to analyze the morphosyntactic
content of four multilingual transformers, including their less explored
distilled versions. Besides, we examine how fine-tuning for POS-tagging affects
the model knowledge. The results show that fine-tuning can improve and decrease
the probing performance and change how morphosyntactic knowledge is distributed
across the model. The code and data are publicly available, and we hope to fill
the gaps in the less studied aspect of transformers.
| 2,021 |
Computation and Language
|
Semantic Analysis for Automated Evaluation of the Potential Impact of
Research Articles
|
Can the analysis of the semantics of words used in the text of a scientific
paper predict its future impact measured by citations? This study details
examples of automated text classification that achieved 80% success rate in
distinguishing between highly-cited and little-cited articles. Automated
intelligent systems allow the identification of promising works that could
become influential in the scientific community.
The problems of quantifying the meaning of texts and representation of human
language have been clear since the inception of Natural Language Processing.
This paper presents a novel method for vector representation of text meaning
based on information theory and show how this informational semantics is used
for text classification on the basis of the Leicester Scientific Corpus.
We describe the experimental framework used to evaluate the impact of
scientific articles through their informational semantics. Our interest is in
citation classification to discover how important semantics of texts are in
predicting the citation count. We propose the semantics of texts as an
important factor for citation prediction.
For each article, our system extracts the abstract of paper, represents the
words of the abstract as vectors in Meaning Space, automatically analyses the
distribution of scientific categories (Web of Science categories) within the
text of abstract, and then classifies papers according to citation counts
(highly-cited, little-cited).
We show that an informational approach to representing the meaning of a text
has offered a way to effectively predict the scientific impact of research
papers.
| 2,021 |
Computation and Language
|
Accounting for Agreement Phenomena in Sentence Comprehension with
Transformer Language Models: Effects of Similarity-based Interference on
Surprisal and Attention
|
We advance a novel explanation of similarity-based interference effects in
subject-verb and reflexive pronoun agreement processing, grounded in surprisal
values computed from a pretrained large-scale Transformer model, GPT-2.
Specifically, we show that surprisal of the verb or reflexive pronoun predicts
facilitatory interference effects in ungrammatical sentences, where a
distractor noun that matches in number with the verb or pronoun leads to faster
reading times, despite the distractor not participating in the agreement
relation. We review the human empirical evidence for such effects, including
recent meta-analyses and large-scale studies. We also show that attention
patterns (indexed by entropy and other measures) in the Transformer show
patterns of diffuse attention in the presence of similar distractors,
consistent with cue-based retrieval models of parsing. But in contrast to these
models, the attentional cues and memory representations are learned entirely
from the simple self-supervised task of predicting the next word.
| 2,021 |
Computation and Language
|
Extractive and Abstractive Explanations for Fact-Checking and Evaluation
of News
|
In this paper, we explore the construction of natural language explanations
for news claims, with the goal of assisting fact-checking and news evaluation
applications. We experiment with two methods: (1) an extractive method based on
Biased TextRank -- a resource-effective unsupervised graph-based algorithm for
content extraction; and (2) an abstractive method based on the GPT-2 language
model. We perform comparative evaluations on two misinformation datasets in the
political and health news domains, and find that the extractive method shows
the most promise.
| 2,021 |
Computation and Language
|
SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text
Style Transfer
|
Text style transfer aims to change the style of sentences while preserving
the semantic meanings. Due to the lack of parallel data, the Denoising
Auto-Encoder (DAE) is widely used in this task to model distributions of
different sentence styles. However, because of the conflict between the target
of the conventional denoising procedure and the target of style transfer task,
the vanilla DAE can not produce satisfying enough results. To improve the
transferability of the model, most of the existing works combine DAE with
various complicated unsupervised networks, which makes the whole system become
over-complex. In this work, we design a novel DAE model named Style-Enhanced
DAE (SE-DAE), which is specifically designed for the text style transfer task.
Compared with previous complicated style-transfer models, our model do not
consist of any complicated unsupervised networks, but only relies on the
high-quality pseudo-parallel data generated by a novel data refinement
mechanism. Moreover, to alleviate the conflict between the targets of the
conventional denoising procedure and the style transfer task, we propose
another novel style denoising mechanism, which is more compatible with the
target of the style transfer task. We validate the effectiveness of our model
on two style benchmark datasets. Both automatic evaluation and human evaluation
show that our proposed model is highly competitive compared with previous
strong the state of the art (SOTA) approaches and greatly outperforms the
vanilla DAE.
| 2,021 |
Computation and Language
|
LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with
a Gradient Boosting Decision Tree Approach
|
A LightGBM model fed with target word lexical characteristics and features
obtained from word frequency lists, psychometric data and bigram association
measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data
Prediction. It obtained the best performance of all teams on two of the five
eye-tracking measures to predict, allowing it to rank first on the official
challenge criterion and to outperform all deep-learning based systems
participating in the challenge.
| 2,021 |
Computation and Language
|
Semi-Supervised Joint Estimation of Word and Document Readability
|
Readability or difficulty estimation of words and documents has been
investigated independently in the literature, often assuming the existence of
extensive annotated resources for the other. Motivated by our analysis showing
that there is a recursive relationship between word and document difficulty, we
propose to jointly estimate word and document difficulty through a graph
convolutional network (GCN) in a semi-supervised fashion. Our experimental
results reveal that the GCN-based method can achieve higher accuracy than
strong baselines, and stays robust even with a smaller amount of labeled data.
| 2,021 |
Computation and Language
|
UoT-UWF-PartAI at SemEval-2021 Task 5: Self Attention Based Bi-GRU with
Multi-Embedding Representation for Toxicity Highlighter
|
Toxic Spans Detection(TSD) task is defined as highlighting spans that make a
text toxic. Many works have been done to classify a given comment or document
as toxic or non-toxic. However, none of those proposed models work at the token
level. In this paper, we propose a self-attention-based bidirectional gated
recurrent unit(BiGRU) with a multi-embedding representation of the tokens. Our
proposed model enriches the representation by a combination of GPT-2, GloVe,
and RoBERTa embeddings, which led to promising results. Experimental results
show that our proposed approach is very effective in detecting span tokens.
| 2,021 |
Computation and Language
|
Question-Aware Memory Network for Multi-hop Question Answering in
Human-Robot Interaction
|
Knowledge graph question answering is an important technology in intelligent
human-robot interaction, which aims at automatically giving answer to human
natural language question with the given knowledge graph. For the
multi-relation question with higher variety and complexity, the tokens of the
question have different priority for the triples selection in the reasoning
steps. Most existing models take the question as a whole and ignore the
priority information in it. To solve this problem, we propose question-aware
memory network for multi-hop question answering, named QA2MN, to update the
attention on question timely in the reasoning process. In addition, we
incorporate graph context information into knowledge graph embedding model to
increase the ability to represent entities and relations. We use it to
initialize the QA2MN model and fine-tune it in the training process. We
evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets
for complex multi-hop question answering. The result demonstrates that QA2MN
achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates
the effectiveness of our model.
| 2,021 |
Computation and Language
|
Understanding Factuality in Abstractive Summarization with FRANK: A
Benchmark for Factuality Metrics
|
Modern summarization models generate highly fluent but often factually
unreliable outputs. This motivated a surge of metrics attempting to measure the
factuality of automatically generated summaries. Due to the lack of common
benchmarks, these metrics cannot be compared. Moreover, all these methods treat
factuality as a binary concept and fail to provide deeper insights into the
kinds of inconsistencies made by different systems. To address these
limitations, we devise a typology of factual errors and use it to collect human
annotations of generated summaries from state-of-the-art summarization systems
for the CNN/DM and XSum datasets. Through these annotations, we identify the
proportion of different categories of factual errors in various summarization
models and benchmark factuality metrics, showing their correlation with human
judgment as well as their specific strengths and weaknesses.
| 2,021 |
Computation and Language
|
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