Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering
Jpliu@wtu.edu.cn Jpliu@wtu.edu.cn, Shijie Mei, Xinrong Hu, Xun Yao, Jack Yang, Yi Guo
Abstract
Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB. Despite sophisticated retrieval algorithms, the impact of the low-resource (incomplete) KB is not fully exploited, where contributing components (. key entities and/or relations) may be absent for question answering. To effectively address this problem, we propose a contrastive regularization based method, which is motivated by the learn-by-analogy capability from human readers. Specifically, the proposed work includes two major modules: the knowledge extension and sMoCo module. The former aims at exploiting the latent knowledge from the context KB and generating auxiliary information in the form of question-answer pairs. The later module utilizes those additional pairs and applies the contrastive regularization to learn informative representations, that making hard positive pairs attracted and hard negative pairs separated. Empirically, we achieved the state-of-the-art performance on the WebQuestionsSP dataset and the effectiveness of proposed modules is also evaluated.- Anthology ID:
- 2022.findings-naacl.82
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1085–1094
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.82
- DOI:
- 10.18653/v1/2022.findings-naacl.82
- Cite (ACL):
- Jpliu@wtu.edu.cn Jpliu@wtu.edu.cn, Shijie Mei, Xinrong Hu, Xun Yao, Jack Yang, and Yi Guo. 2022. Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1085–1094, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering (Jpliu@wtu.edu.cn et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.findings-naacl.82.pdf
- Code
- jakeymei/smoco
- Data
- WebQuestionsSP