@inproceedings{feng-etal-2022-multi,
title = "Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge",
author = "Feng, Yue and
Han, Zhen and
Sun, Mingming and
Li, Ping",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.12",
doi = "10.18653/v1/2022.findings-naacl.12",
pages = "151--156",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge
%A Feng, Yue
%A Han, Zhen
%A Sun, Mingming
%A Li, Ping
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F feng-etal-2022-multi
%X 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.
%R 10.18653/v1/2022.findings-naacl.12
%U https://aclanthology.org/2022.findings-naacl.12
%U https://doi.org/10.18653/v1/2022.findings-naacl.12
%P 151-156
Markdown (Informal)
[Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge](https://aclanthology.org/2022.findings-naacl.12) (Feng et al., Findings 2022)
ACL