Semi-supervised Domain Adaptation for Dependency Parsing
Abstract
During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing. This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-aware datasets. We propose a simple domain embedding approach to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning. In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.- Anthology ID:
- P19-1229
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2386–2395
- Language:
- URL:
- https://aclanthology.org/P19-1229
- DOI:
- 10.18653/v1/P19-1229
- Cite (ACL):
- Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, and Luo Si. 2019. Semi-supervised Domain Adaptation for Dependency Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2386–2395, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Semi-supervised Domain Adaptation for Dependency Parsing (Li et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P19-1229.pdf
- Code
- SUDA-LA/ACL2019-dp-cross-domain