Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge

Yue Feng, Zhen Han, Mingming Sun, Ping Li


Abstract
Open-domain question answering systems need to answer question of our interests with structured and unstructured information. However, existing approaches only select one source to generate answer or only conduct reasoning on structured information. In this paper, we pro- pose a Document-Entity Heterogeneous Graph Network, referred to as DEHG, to effectively integrate different sources of information, and conduct reasoning on heterogeneous information. DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question. Experimental results on HybirdQA dataset show that DEHG outperforms the state-of-the-art methods.
Anthology ID:
2022.findings-naacl.12
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
151–156
Language:
URL:
https://aclanthology.org/2022.findings-naacl.12
DOI:
10.18653/v1/2022.findings-naacl.12
Bibkey:
Cite (ACL):
Yue Feng, Zhen Han, Mingming Sun, and Ping Li. 2022. Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 151–156, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge (Feng et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.12.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.12.mp4
Data
HybridQA