Experiments on binary and multi-classĬlassification tasks show that SimPLE leads to more robust self-training We also found that both pretrainedĮntailment-based models and the self-trained models are robust againstĪdversarial evaluation data. Pseudo-labeling quality in self-training. We propose the Simple Pseudo-Label Editing (SimPLE) algorithm for better Secondly, we notice that self-trainingĮntailment-based models with unlabeled data can significantly improve theĪdaptation performance on downstream tasks. This approach improves the zero-shot adaptation of In this work, weĭesign a prompting strategy that formulates a number of different NLU tasks asĬontextual entailment. Language understanding (NLU) models, and recent studies have found thatĮntailment pretraining benefits weakly supervised fine-tuning.

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