Paper Info
Title
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge
Abstract
To perform well on a machine reading comprehension (MRC) task, machine readers usually require commonsense knowledge that is not explicitly mentioned in the given documents. This paper aims to extract a new kind of structured knowledge from scripts and use it to improve MRC. We focus on scripts as they contain rich verbal and nonverbal messages, and two relevant messages originally conveyed by different modalities during a short time period may serve as arguments of a piece of commonsense knowledge as they function together in daily communications. To save human efforts to name relations, we propose to represent relations implicitly by situating such an argument pair in a context and call it contextualized knowledge. To use the extracted knowledge to improve MRC, we compare several fine-tuning strategies to use the weakly-labeled MRC data constructed based on contextualized knowledge and further design a teacher-student paradigm with multiple teachers to facilitate the transfer of knowledge in weakly-labeled MRC data. Experimental results show that our paradigm outperforms other methods that use weaklylabeled data and improves a state-of-the-art baseline by 4:3% in accuracy on a Chinese multiple-choice MRC dataset C3, wherein most of the questions require unstated prior knowledge. We also seek to transfer the knowledge to other tasks by simply adapting the resulting student reader, yielding a 2:9% improvement in F1 on a relation extraction dataset DialogRE, demonstrating the potential usefulness of the knowledge for non-MRC tasks that require document comprehension.
Year
DOI
Venue
2022
10.18653/v1/2022.acl-long.598
PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS)
DocType
Volume
Citations 
Conference
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Kai Sun1567.06
Dian Yu26411.49
Jianshu Chen388352.94
Dong Yu46264475.73
Claire Cardie55591555.20