Abstract
Few-shot relation classification aims to classify the relation type between two given entities in a sentence by training with a few labeled instances for each relation. However, most of existing models fail to distinguish multiple relations that co-exist in one sentence. This paper presents a novel dependency-aware prototype learning (DAPL) method for few-shot relation classification. Concretely, we utilize dependency trees and shortest dependency paths (SDP) as structural information to complement the contextualized representations of input sentences by using the dependency-aware embedding as attention inputs to learn attentive sentence representations. In addition, we introduce a gate controlled update mechanism to update the dependency-aware representations according to the output of each network layer. Extensive experiments on the FewRel dataset show that DAPL achieves substantially better performance than strong baselines. For reproducibility, we will release our code and data upon the publication of this paper at https://github.com/publicstaticvo/DAPL.- Anthology ID:
- 2022.coling-1.205
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2339–2345
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.205
- DOI:
- Cite (ACL):
- Tianshu Yu, Min Yang, and Xiaoyan Zhao. 2022. Dependency-aware Prototype Learning for Few-shot Relation Classification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2339–2345, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Dependency-aware Prototype Learning for Few-shot Relation Classification (Yu et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.coling-1.205.pdf
- Code
- publicstaticvo/dapl
- Data
- FewRel