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Revisiting Self-training for Few-shot Learning of Language Model ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chen, Yiming
;
Cheng, Ran
;
Lee, Grandee
;
Li, Haizhou
;
Zhang, Chen
;
Zhang, Yan
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.718/ 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 ...
Keyword:
Computational Linguistics
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://underline.io/lecture/38073-revisiting-self-training-for-few-shot-learning-of-language-model
https://dx.doi.org/10.48448/n28v-0y36
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