Careful Selection of Knowledge to Solve Open Book Question Answering

Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra, Chitta Baral


Abstract
Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.
Anthology ID:
P19-1615
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6120–6129
Language:
URL:
https://www.aclweb.org/anthology/P19-1615
DOI:
10.18653/v1/P19-1615
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1615.pdf