@inproceedings{qin-etal-2021-improving-query,
title = "Improving Query Graph Generation for Complex Question Answering over Knowledge Base",
author = "Qin, Kechen and
Li, Cheng and
Pavlu, Virgil and
Aslam, Javed",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.346",
doi = "10.18653/v1/2021.emnlp-main.346",
pages = "4201--4207",
abstract = "Most of the existing Knowledge-based Question Answering (KBQA) methods first learn to map the given question to a query graph, and then convert the graph to an executable query to find the answer. The query graph is typically expanded progressively from the topic entity based on a sequence prediction model. In this paper, we propose a new solution to query graph generation that works in the opposite manner: we start with the entire knowledge base and gradually shrink it to the desired query graph. This approach improves both the efficiency and the accuracy of query graph generation, especially for complex multi-hop questions. Experimental results show that our method achieves state-of-the-art performance on ComplexWebQuestion (CWQ) dataset.",
}
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<abstract>Most of the existing Knowledge-based Question Answering (KBQA) methods first learn to map the given question to a query graph, and then convert the graph to an executable query to find the answer. The query graph is typically expanded progressively from the topic entity based on a sequence prediction model. In this paper, we propose a new solution to query graph generation that works in the opposite manner: we start with the entire knowledge base and gradually shrink it to the desired query graph. This approach improves both the efficiency and the accuracy of query graph generation, especially for complex multi-hop questions. Experimental results show that our method achieves state-of-the-art performance on ComplexWebQuestion (CWQ) dataset.</abstract>
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%0 Conference Proceedings
%T Improving Query Graph Generation for Complex Question Answering over Knowledge Base
%A Qin, Kechen
%A Li, Cheng
%A Pavlu, Virgil
%A Aslam, Javed
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F qin-etal-2021-improving-query
%X Most of the existing Knowledge-based Question Answering (KBQA) methods first learn to map the given question to a query graph, and then convert the graph to an executable query to find the answer. The query graph is typically expanded progressively from the topic entity based on a sequence prediction model. In this paper, we propose a new solution to query graph generation that works in the opposite manner: we start with the entire knowledge base and gradually shrink it to the desired query graph. This approach improves both the efficiency and the accuracy of query graph generation, especially for complex multi-hop questions. Experimental results show that our method achieves state-of-the-art performance on ComplexWebQuestion (CWQ) dataset.
%R 10.18653/v1/2021.emnlp-main.346
%U https://aclanthology.org/2021.emnlp-main.346
%U https://doi.org/10.18653/v1/2021.emnlp-main.346
%P 4201-4207
Markdown (Informal)
[Improving Query Graph Generation for Complex Question Answering over Knowledge Base](https://aclanthology.org/2021.emnlp-main.346) (Qin et al., EMNLP 2021)
ACL