Answering Complex Questions Using Open Information Extraction

Tushar Khot, Ashish Sabharwal, Peter Clark


Abstract
While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such knowledge has only been used to answer simple questions with retrieval-based methods. We overcome this limitation by presenting a method for reasoning with Open IE knowledge, allowing more complex questions to be handled. Using a recently proposed support graph optimization framework for QA, we develop a new inference model for Open IE, in particular one that can work effectively with multiple short facts, noise, and the relational structure of tuples. Our model significantly outperforms a state-of-the-art structured solver on complex questions of varying difficulty, while also removing the reliance on manually curated knowledge.
Anthology ID:
P17-2049
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
311–316
Language:
URL:
https://aclanthology.org/P17-2049
DOI:
10.18653/v1/P17-2049
Bibkey:
Cite (ACL):
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2017. Answering Complex Questions Using Open Information Extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 311–316, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Answering Complex Questions Using Open Information Extraction (Khot et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2049.pdf
Note:
 P17-2049.Notes.pdf
Data
TupleInf Open IE Dataset