Dependency-aware Prototype Learning for Few-shot Relation Classification

Tianshu Yu, Min Yang, Xiaoyan Zhao


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
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2339–2345
Language:
URL:
https://aclanthology.org/2022.coling-1.205
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.205.pdf
Code
 publicstaticvo/dapl
Data
FewRel