Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2022

Abstract

Multiple-Choice Question Answering (MCQA) is one of the challenging tasks in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In OpenbookQA dataset [1], the requirement of extracting "evidence" is particularly important due to the mutual independence of sentences in the context. Existing work tackles this problem by annotated evidence or distant supervision with rules which overly rely on human efforts. To address the challenge, we propose a simple yet effective approach termed evidence filtering to model the relationships between the encoded contexts with respect to different options collectively, and to potentially highlight the evidence sentences and filter out unrelated sentences. In addition to the effective reduction of human efforts of our approach compared, through extensive experiments on OpenbookQA, we show that the proposed approach outperforms the models that use the same backbone and more training data; and our parameter analysis also demonstrates the interpretability of our approach.

Keywords

Evidence Extraction, Machine Reading Comprehension, Natural Language Processing, Question Answering

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

ICASSP 2022: IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, May 23-27: Proceedings

First Page

8212

Last Page

8216

ISBN

9781665405416

Identifier

10.1109/ICASSP43922.2022.9747889

Publisher

IEEE

City or Country

Singapore

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICASSP43922.2022.9747889

Download

Share

COinS