Zero-shot Text Classification via Reinforced Self-training
Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen
Abstract
Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data. Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets. We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection. Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification- Anthology ID:
- 2020.acl-main.272
- 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:
- 3014–3024
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.272
- DOI:
- 10.18653/v1/2020.acl-main.272
- Cite (ACL):
- Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, and Huajun Chen. 2020. Zero-shot Text Classification via Reinforced Self-training. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3014–3024, Online. Association for Computational Linguistics.
- Cite (Informal):
- Zero-shot Text Classification via Reinforced Self-training (Ye et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.acl-main.272.pdf