Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction through Relation-Aware Prompt Learning
Ge Bai, Chenji Lu, Daichi Guo, Shilong Li, Ying Liu, Zhang Zhang, Guanting Dong, Ruifang Liu, Sun Yong
Abstract
Cross-domain few-shot Relation Extraction (RE) aims to transfer knowledge from a source domain to a different target domain to address low-resource problems.Previous work utilized label descriptions and entity information to leverage the knowledge of the source domain.However, these models are prone to confusion when directly applying this knowledge to a target domain with entirely new types of relations, which becomes particularly pronounced when facing similar relations.In this work, we propose a relation-aware prompt learning method with pre-training.Specifically, we empower the model to clear confusion by decomposing various relation types through an innovative label prompt, while a context prompt is employed to capture differences in different scenarios, enabling the model to further discern confusion. Two pre-training tasks are designed to leverage the prompt knowledge and paradigm.Experiments show that our method outperforms previous sota methods, yielding significantly better results on cross-domain few-shot RE tasks.- Anthology ID:
- 2024.naacl-short.6
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 70–78
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.6
- DOI:
- Cite (ACL):
- Ge Bai, Chenji Lu, Daichi Guo, Shilong Li, Ying Liu, Zhang Zhang, Guanting Dong, Ruifang Liu, and Sun Yong. 2024. Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction through Relation-Aware Prompt Learning. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 70–78, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction through Relation-Aware Prompt Learning (Bai et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2024.naacl-short.6.pdf