Abstract
Continual relation extraction (CRE) aims to continuously learn relations in new tasks without forgetting old relations in previous tasks.Current CRE methods are all rehearsal-based which need to store samples and thus may encounter privacy and security issues.This paper targets rehearsal-free continual relation extraction for the first time and decomposes it into task identification and within-task prediction sub-problems. Existing rehearsal-free methods focus on training a model (expert) for within-task prediction yet neglect to enhance models’ capability of task identification.In this paper, we propose an Ensemble-of-Experts (EoE) framework for rehearsal-free continual relation extraction. Specifically, we first discriminatively train each expert by augmenting analogous relations across tasks to enhance the expert’s task identification ability. We then propose a cascade voting mechanism to form an ensemble of experts for effectively aggregating their abilities.Extensive experiments demonstrate that our method outperforms current rehearsal-free methods and is even better than rehearsal-based CRE methods.- Anthology ID:
- 2024.findings-acl.83
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1410–1423
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.83
- DOI:
- Cite (ACL):
- Shen Zhou, Yongqi Li, Xin Miao, and Tieyun Qian. 2024. An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction. In Findings of the Association for Computational Linguistics ACL 2024, pages 1410–1423, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction (Zhou et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2024.findings-acl.83.pdf