Prompt-Free Few-Shot Learning with ELECTRA for Acceptability Judgment.

Few-shot learning remains a great challenge for the task of acceptability judgment that identifies whether a sentence is acceptable or unacceptable. In this paper, we propose a prompt-free learning approach, namely PF-ELECTRA, to few-shot acceptability judgment. First, we leverage a pre-trained token replaced detection model, ELECTRA, as our basic few-shot learner to deal with the challenge of data distribution difference. Second, we design a prompt-free few-shot learning strategy that uses both the maximal unacceptability score for a single token and the overall unacceptability score for the whole sentence to judge the acceptability. Empirical studies validate the effectiveness of PF-ELECTRA on challenging few-shot acceptability judgment. To the best of our knowledge, it is the first work that improves the performance of few-shot acceptability judgment based on standard fine-tuning.