Abstract
Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.- Anthology ID:
- 2020.acl-main.777
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8801–8811
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.777
- DOI:
- 10.18653/v1/2020.acl-main.777
- Cite (ACL):
- Luoxin Chen, Weitong Ruan, Xinyue Liu, and Jianhua Lu. 2020. SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8801–8811, Online. Association for Computational Linguistics.
- Cite (Informal):
- SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling (Chen et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.acl-main.777.pdf
- Data
- CoNLL, CoNLL-2000