Abstract
Recently, prompt-based fine-tuning has garnered considerable interest as a core technique for few-shot text classification task. This approach reformulates the fine-tuning objective to align with the Masked Language Modeling (MLM) objective. Leveraging unlabeled data, prompt-based self-training has shown greater effectiveness in binary and three-class classification. However, prompt-based self-training for multi-class classification has not been adequately investigated, despite its significant applicability to real-world scenarios. Moreover, extending current methods to multi-class classification suffers from the verbalizer that extracts the predicted value of manually pre-defined single label word for each class from MLM predictions. Consequently, we introduce a novel, efficient verbalizer structure, named Mapping-free Automatic Verbalizer (MAV). Comprising two fully connected layers, MAV serves as a trainable verbalizer that automatically extracts the requisite word features for classification by capitalizing on all available information from MLM predictions. Experimental results on five multi-class classification datasets indicate MAV’s superior self-training efficacy.- Anthology ID:
- 2023.findings-emnlp.921
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13786–13800
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.921
- DOI:
- 10.18653/v1/2023.findings-emnlp.921
- Cite (ACL):
- Yookyung Kho, Jaehee Kim, and Pilsung Kang. 2023. Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13786–13800, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification (Kho et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.921.pdf