Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering

Peng Wu, Shujian Huang, Rongxiang Weng, Zaixiang Zheng, Jianbing Zhang, Xiaohui Yan, Jiajun Chen


Abstract
Relation detection is a core step in many natural language process applications including knowledge base question answering. Previous efforts show that single-fact questions could be answered with high accuracy. However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data. But for unseen relations, the performance will drop rapidly. The main reason for this problem is that the representations for unseen relations are missing. In this paper, we propose a simple mapping method, named representation adapter, to learn the representation mapping for both seen and unseen relations based on previously learned relation embedding. We employ the adversarial objective and the reconstruction objective to improve the mapping performance. We re-organize the popular SimpleQuestion dataset to reveal and evaluate the problem of detecting unseen relations. Experiments show that our method can greatly improve the performance of unseen relations while the performance for those seen part is kept comparable to the state-of-the-art.
Anthology ID:
P19-1616
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6130–6139
Language:
URL:
https://aclanthology.org/P19-1616
DOI:
10.18653/v1/P19-1616
Bibkey:
Cite (ACL):
Peng Wu, Shujian Huang, Rongxiang Weng, Zaixiang Zheng, Jianbing Zhang, Xiaohui Yan, and Jiajun Chen. 2019. Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6130–6139, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (Wu et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1616.pdf
Code
 wudapeng268/KBQA-Adapter