Improving Question Answering with External Knowledge

Xiaoman Pan, Kai Sun, Dian Yu, Jianshu Chen, Heng Ji, Claire Cardie, Dong Yu


Abstract
We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus. In this work, we explore simple yet effective methods for exploiting two sources of external knowledge for subject-area QA. The first enriches the original subject-area reference corpus with relevant text snippets extracted from an open-domain resource (i.e., Wikipedia) that cover potentially ambiguous concepts in the question and answer options. As in other QA research, the second method simply increases the amount of training data by appending additional in-domain subject-area instances. Experiments on three challenging multiple-choice science QA tasks (i.e., ARC-Easy, ARC-Challenge, and OpenBookQA) demonstrate the effectiveness of our methods: in comparison to the previous state-of-the-art, we obtain absolute gains in accuracy of up to 8.1%, 13.0%, and 12.8%, respectively. While we observe consistent gains when we introduce knowledge from Wikipedia, we find that employing additional QA training instances is not uniformly helpful: performance degrades when the added instances exhibit a higher level of difficulty than the original training data. As one of the first studies on exploiting unstructured external knowledge for subject-area QA, we hope our methods, observations, and discussion of the exposed limitations may shed light on further developments in the area.
Anthology ID:
D19-5804
Volume:
Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–37
Language:
URL:
https://aclanthology.org/D19-5804
DOI:
10.18653/v1/D19-5804
Bibkey:
Cite (ACL):
Xiaoman Pan, Kai Sun, Dian Yu, Jianshu Chen, Heng Ji, Claire Cardie, and Dong Yu. 2019. Improving Question Answering with External Knowledge. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 27–37, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Improving Question Answering with External Knowledge (Pan et al., 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/D19-5804.pdf
Code
 nlpdata/external
Data
ARCOpenBookQARACE