A survey on machine reading comprehension systems
Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural
Language Processing. The goal of this field is to develop systems for answering the …
Language Processing. The goal of this field is to develop systems for answering the …
MRQA 2019 shared task: Evaluating generalization in reading comprehension
We present the results of the Machine Reading for Question Answering (MRQA) 2019
shared task on evaluating the generalization capabilities of reading comprehension …
shared task on evaluating the generalization capabilities of reading comprehension …
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
The effect of natural distribution shift on question answering models
We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and
evaluate the ability of question-answering systems to generalize to new data. Our first test …
evaluate the ability of question-answering systems to generalize to new data. Our first test …
Contrastive domain adaptation for question answering using limited text corpora
Question generation has recently shown impressive results in customizing question
answering (QA) systems to new domains. These approaches circumvent the need for …
answering (QA) systems to new domains. These approaches circumvent the need for …
On the domain adaptation and generalization of pretrained language models: A survey
Recent advances in NLP are brought by a range of large-scale pretrained language models
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …
Out-of-distribution generalization in natural language processing: Past, present, and future
Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …
[PDF][PDF] Unsupervised energy-based adversarial domain adaptation for cross-domain text classification
Transferring knowledge from a label-rich domain (source domain) to a label-scarce domain
(target domain) for pervasive cross-domain Text Classification (TC) is a non-trivial task. To …
(target domain) for pervasive cross-domain Text Classification (TC) is a non-trivial task. To …
Domain adaptation for question answering via question classification
Question answering (QA) has demonstrated impressive progress in answering questions
from customized domains. Nevertheless, domain adaptation remains one of the most elusive …
from customized domains. Nevertheless, domain adaptation remains one of the most elusive …
Unsupervised domain adaptation via contrastive adversarial domain mixup: A case study on covid-19
Training large deep learning (DL) models with high performance for natural language
downstream tasks usually requires rich-labeled data. However, in a real-world application of …
downstream tasks usually requires rich-labeled data. However, in a real-world application of …