Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
Ngoc Dang Nguyen, Lan Du, Wray Buntine, Changyou Chen, Richard Beare
Abstract
Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic characteristics that clinical narratives possess, which leads to unsatsifactory performance. In this paper, we present a simple yet effective hardness-guided domain adaptation framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in the low-resource scenarios. Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published state-of-the-art (SOTA) MetaNER model.- Anthology ID:
- 2022.emnlp-main.271
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4063–4071
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.271
- DOI:
- 10.18653/v1/2022.emnlp-main.271
- Cite (ACL):
- Ngoc Dang Nguyen, Lan Du, Wray Buntine, Changyou Chen, and Richard Beare. 2022. Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4063–4071, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios (Nguyen et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.271.pdf