Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases

Xixin Hu, Xuan Wu, Yiheng Shu, Yuzhong Qu


Abstract
Question answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing. Recently, generation-based methods that translate natural language questions to executable logical forms have achieved promising performance. These methods use auxiliary information to augment the logical form generation of questions with unseen KB items or novel combinations, but the noise introduced can also leads to more incorrect results. In this work, we propose GMT-KBQA, a Generation-based KBQA method via Multi-Task learning, to better retrieve and utilize auxiliary information. GMT-KBQA first obtains candidate entities and relations through dense retrieval, and then introduces a multi-task model which jointly learns entity disambiguation, relation classification, and logical form generation. Experimental results show that GMT-KBQA achieves state-of-the-art results on both ComplexWebQuestions and WebQuestionsSP datasets. Furthermore, the detailed evaluation demonstrates that GMT-KBQA benefits from the auxiliary tasks and has a strong generalization capability.
Anthology ID:
2022.coling-1.145
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1687–1696
Language:
URL:
https://aclanthology.org/2022.coling-1.145
DOI:
Bibkey:
Cite (ACL):
Xixin Hu, Xuan Wu, Yiheng Shu, and Yuzhong Qu. 2022. Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1687–1696, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases (Hu et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.145.pdf
Code
 hxx97/gmt-kbqa