Abstract
Multi-hop relation detection in Knowledge Base Question Answering (KBQA) aims at retrieving the relation path starting from the topic entity to the answer node based on a given question, where the relation path may comprise multiple relations. Most of the existing methods treat it as a single-label learning problem while ignoring the fact that for some complex questions, there exist multiple correct relation paths in knowledge bases. Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem. However, performing multi-label multi-hop relation detection is challenging since the numbers of both the labels and the hops are unknown. To tackle this challenge, multi-label multi-hop relation detection is formulated as a sequence generation task. A relation-aware sequence relation generation model is proposed to solve the problem in an end-to-end manner. Experimental results show the effectiveness of the proposed method for relation detection and KBQA.- Anthology ID:
- 2021.findings-emnlp.404
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4713–4719
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.404
- DOI:
- 10.18653/v1/2021.findings-emnlp.404
- Cite (ACL):
- Linhai Zhang, Deyu Zhou, Chao Lin, and Yulan He. 2021. A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4713–4719, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (Zhang et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.findings-emnlp.404.pdf
- Data
- SimpleQuestions