Risk Minimization for Zero-shot Sequence Labeling
Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
Abstract
Zero-shot sequence labeling aims to build a sequence labeler without human-annotated datasets. One straightforward approach is utilizing existing systems (source models) to generate pseudo-labeled datasets and train a target sequence labeler accordingly. However, due to the gap between the source and the target languages/domains, this approach may fail to recover the true labels. In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels. By making the risk function trainable, we draw a connection between minimum risk training and latent variable model learning. We propose a unified learning algorithm based on the expectation maximization (EM) algorithm. We extensively evaluate our proposed approaches on cross-lingual/domain sequence labeling tasks over twenty-one datasets. The results show that our approaches outperform state-of-the-art baseline systems.- Anthology ID:
- 2021.acl-long.380
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4909–4920
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.380
- DOI:
- 10.18653/v1/2021.acl-long.380
- Cite (ACL):
- Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, and Kewei Tu. 2021. Risk Minimization for Zero-shot Sequence Labeling. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4909–4920, Online. Association for Computational Linguistics.
- Cite (Informal):
- Risk Minimization for Zero-shot Sequence Labeling (Hu et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.380.pdf