Zero-Shot Text Classification with Self-Training
Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, Noam Slonim
Abstract
Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference datasets have been widely adopted as zero-shot classifiers due to their promising results and off-the-shelf availability. However, the fact that such models are unfamiliar with the target task can lead to instability and performance issues. We propose a plug-and-play method to bridge this gap using a simple self-training approach, requiring only the class names along with an unlabeled dataset, and without the need for domain expertise or trial and error. We show that fine-tuning the zero-shot classifier on its most confident predictions leads to significant performance gains across a wide range of text classification tasks, presumably since self-training adapts the zero-shot model to the task at hand.- Anthology ID:
- 2022.emnlp-main.73
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1107–1119
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.73
- DOI:
- 10.18653/v1/2022.emnlp-main.73
- Cite (ACL):
- Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, and Noam Slonim. 2022. Zero-Shot Text Classification with Self-Training. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1107–1119, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Zero-Shot Text Classification with Self-Training (Gera et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2022.emnlp-main.73.pdf