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.”- Anthology ID:
- 2023.ccl-1.50
- Volume:
- Proceedings of the 22nd Chinese National Conference on Computational Linguistics
- Month:
- August
- Year:
- 2023
- Address:
- Harbin, China
- Editors:
- Maosong Sun, Bing Qin, Xipeng Qiu, Jing Jiang, Xianpei Han
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 569–582
- Language:
- English
- URL:
- https://aclanthology.org/2023.ccl-1.50
- DOI:
- Cite (ACL):
- Li Jiawei, Ren Mucheng, Gao Yang, and Yang Yizhe. 2023. Ask to Understand: Question Generation for Multi-hop Question Answering. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 569–582, Harbin, China. Chinese Information Processing Society of China.
- Cite (Informal):
- Ask to Understand: Question Generation for Multi-hop Question Answering (Jiawei et al., CCL 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.ccl-1.50.pdf