@inproceedings{jiawei-etal-2023-ask,
title = "Ask to Understand: Question Generation for Multi-hop Question Answering",
author = "Jiawei, Li and
Mucheng, Ren and
Yang, Gao and
Yizhe, Yang",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.50",
pages = "569--582",
abstract = "{``}Multi-hop Question Answering (QA) requires the machine to answer complex questions by find-ing scattering clues and reasoning from multiple documents. Graph Network (GN) and Ques-tion Decomposition (QD) are two common approaches at present. The former uses the {``}black-box{''} reasoning process to capture the potential relationship between entities and sentences, thusachieving good performance. At the same time, the latter provides a clear reasoning logical routeby decomposing multi-hop questions into simple single-hop sub-questions. In this paper, wepropose a novel method to complete multi-hop QA from the perspective of Question Genera-tion (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classicalQA module, which could help the model understand the context by asking inherently logicalsub-questions, thus inheriting interpretability from the QD-based method and showing superiorperformance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of ourproposed QG module, human evaluation further clarifies its interpretability quantitatively, andthorough analysis shows that the QG module could generate better sub-questions than QD meth-ods in terms of fluency, consistency, and diversity.{''}",
language = "English",
}
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<abstract>“Multi-hop Question Answering (QA) requires the machine to answer complex questions by find-ing scattering clues and reasoning from multiple documents. Graph Network (GN) and Ques-tion Decomposition (QD) are two common approaches at present. The former uses the “black-box” reasoning process to capture the potential relationship between entities and sentences, thusachieving good performance. At the same time, the latter provides a clear reasoning logical routeby decomposing multi-hop questions into simple single-hop sub-questions. In this paper, wepropose a novel method to complete multi-hop QA from the perspective of Question Genera-tion (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classicalQA module, which could help the model understand the context by asking inherently logicalsub-questions, thus inheriting interpretability from the QD-based method and showing superiorperformance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of ourproposed QG module, human evaluation further clarifies its interpretability quantitatively, andthorough analysis shows that the QG module could generate better sub-questions than QD meth-ods in terms of fluency, consistency, and diversity.”</abstract>
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%0 Conference Proceedings
%T Ask to Understand: Question Generation for Multi-hop Question Answering
%A Jiawei, Li
%A Mucheng, Ren
%A Yang, Gao
%A Yizhe, Yang
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F jiawei-etal-2023-ask
%X “Multi-hop Question Answering (QA) requires the machine to answer complex questions by find-ing scattering clues and reasoning from multiple documents. Graph Network (GN) and Ques-tion Decomposition (QD) are two common approaches at present. The former uses the “black-box” reasoning process to capture the potential relationship between entities and sentences, thusachieving good performance. At the same time, the latter provides a clear reasoning logical routeby decomposing multi-hop questions into simple single-hop sub-questions. In this paper, wepropose a novel method to complete multi-hop QA from the perspective of Question Genera-tion (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classicalQA module, which could help the model understand the context by asking inherently logicalsub-questions, thus inheriting interpretability from the QD-based method and showing superiorperformance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of ourproposed QG module, human evaluation further clarifies its interpretability quantitatively, andthorough analysis shows that the QG module could generate better sub-questions than QD meth-ods in terms of fluency, consistency, and diversity.”
%U https://aclanthology.org/2023.ccl-1.50
%P 569-582
Markdown (Informal)
[Ask to Understand: Question Generation for Multi-hop Question Answering](https://aclanthology.org/2023.ccl-1.50) (Jiawei et al., CCL 2023)
ACL