Revisiting Self-training for Few-shot Learning of Language Model

Yiming Chen, Yan Zhang, Chen Zhang, Grandee Lee, Ran Cheng, Haizhou Li


Abstract
As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM. Given two views of a text sample via weak and strong augmentation techniques, SFLM generates a pseudo label on the weakly augmented version. Then, the model predicts the same pseudo label when fine-tuned with the strongly augmented version. This simple approach is shown to outperform other state-of-the-art supervised and semi-supervised counterparts on six sentence classification and six sentence-pair classification benchmarking tasks. In addition, SFLM only relies on a few in-domain unlabeled data. We conduct a comprehensive analysis to demonstrate the robustness of our proposed approach under various settings, including augmentation techniques, model scale, and few-shot knowledge transfer across tasks.
Anthology ID:
2021.emnlp-main.718
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9125–9135
Language:
URL:
https://aclanthology.org/2021.emnlp-main.718
DOI:
10.18653/v1/2021.emnlp-main.718
Bibkey:
Cite (ACL):
Yiming Chen, Yan Zhang, Chen Zhang, Grandee Lee, Ran Cheng, and Haizhou Li. 2021. Revisiting Self-training for Few-shot Learning of Language Model. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9125–9135, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Revisiting Self-training for Few-shot Learning of Language Model (Chen et al., EMNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.718.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.emnlp-main.718.mp4
Code
 matthewcym/sflm
Data
GLUEMPQA Opinion CorpusMRPCMultiNLIQNLISNLISST