Abstract
Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods.- Anthology ID:
- P19-1236
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2464–2474
- Language:
- URL:
- https://aclanthology.org/P19-1236
- DOI:
- 10.18653/v1/P19-1236
- Cite (ACL):
- Chen Jia, Xiaobo Liang, and Yue Zhang. 2019. Cross-Domain NER using Cross-Domain Language Modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2464–2474, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Domain NER using Cross-Domain Language Modeling (Jia et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/P19-1236.pdf
- Code
- jiachenwestlake/Cross-Domain_NER
- Data
- CoNLL-2003