While the prompt-based fine-tuning methods had advanced few-shot natural language understanding tasks, self-training methods are also being explored. This work revisits the consistency regularization in self-training and presents explicit and implicit consistency regularization enhanced language model (EICO). By employing both explicit and implicit consistency regularization, EICO advances the performance of prompt-based few-shot text classification. For implicit consistency regularization, we generate pseudo-label from the weakly-augmented view and predict pseudo-label from the strongly-augmented view. For explicit consistency regularization, we minimize the difference between the prediction of the augmentation view and the prediction of the original view. We conducted extensive experiments on six text classification datasets and found that with sixteen labeled examples, EICO achieves competitive performance compared to existing self-training few-shot learning methods.

Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity. Existing efforts often deal with the two problems separately regardless of their close essential correlations. In this paper, we investigate the possibility of jointly solving zero pronoun resolution and coreference resolution via a novel end-to-end neural model. Specifically, we design a gap-masked self-attention model that encodes gaps and tokens in the same space, where gaps could capture valuable contextual information according to their surrounding tokens while tokens could maintain original sequential information without disturbance. Additionally, we also propose a two-stage interaction mechanism to make full use of the exclusive relationship between zero pronouns and mentions. Our empirical study conducted on the OntoNotes 5.0 Chinese dataset shows that our model could outperform corresponding state-of-the-art approaches on both tasks.