Abstract
Pre-trained masked language models have demonstrated remarkable ability as few-shot learners. In this paper, as an alternative, we propose a novel approach to few-shot learning with pre-trained token-replaced detection models like ELECTRA. In this approach, we reformulate a classification or a regression task as a token-replaced detection problem. Specifically, we first define a template and label description words for each task and put them into the input to form a natural language prompt. Then, we employ the pre-trained token-replaced detection model to predict which label description word is the most original (i.e., least replaced) among all label description words in the prompt. A systematic evaluation on 16 datasets demonstrates that our approach outperforms few-shot learners with pre-trained masked language models in both one-sentence and two-sentence learning tasks.- Anthology ID:
- 2022.coling-1.289
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3274–3284
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.289
- DOI:
- Cite (ACL):
- Zicheng Li, Shoushan Li, and Guodong Zhou. 2022. Pre-trained Token-replaced Detection Model as Few-shot Learner. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3274–3284, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Pre-trained Token-replaced Detection Model as Few-shot Learner (Li et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.coling-1.289.pdf
- Code
- cjfarmer/trd_fsl
- Data
- CoLA, GLUE, QNLI, SNLI