Entailment Method Based on Template Selection for Chinese Text Few-shot Learning

The limit of labeled data has become the bottleneck in numerous text-related tasks. Recently, few-shot learning based on pre-trained language model has become an attractive topic. Entailment-based Few-shot Learning (i.e., EFL) is an effective way through transforming a text classification task into a textual entailment task, which bridges the gap between down-stream tasks and pre-trained tasks. However, the performance of the downstream task is sensitive to the manually selected templates in this type of approaches. To alleviate this problem, we improve the EFL method by applying a naïve template selection mechanism, leveraging masked language model to assess the quality of candidate templates. Moreover, we evaluate our method on FewCLUE shared tasks. Extensive experiments demonstrate the effectiveness of our proposed method.