DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction

Mengru Wang, Jianming Zheng, Fei Cai, Taihua Shao, Honghui Chen


Abstract
Few-shot relation extraction aims to identify the relation type between entities in a given text in the low-resource scenario. Albeit much progress, existing meta-learning methods still fall into prediction confusions owing to the limited inference ability over shallow text features. To relieve these confusions, this paper proposes a discriminative rule-based knowledge (DRK) method. Specifically, DRK adopts a logic-aware inference module to ease the word-overlap confusion, which introduces a logic rule to constrain the inference process, thereby avoiding the adverse effect of shallow text features. Also, DRK employs a discrimination finding module to alleviate the entity-type confusion, which explores distinguishable text features via a hierarchical contrastive learning. We conduct extensive experiments on four types of meta tasks and the results show promising improvements from DRK (6.0% accuracy gains on average). Besides, error analyses reveal the word-overlap and entity-type errors are the main courses of mispredictions in few-shot relation extraction.
Anthology ID:
2022.coling-1.186
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:
2129–2140
Language:
URL:
https://aclanthology.org/2022.coling-1.186
DOI:
Bibkey:
Cite (ACL):
Mengru Wang, Jianming Zheng, Fei Cai, Taihua Shao, and Honghui Chen. 2022. DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2129–2140, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction (Wang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.186.pdf
Data
FewRel