Few-Shot Text Classification

Pattern-Exploiting Training is a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. These phrases are then used to assign soft labels to a large set of unlabeled examples. Finally, standard supervised training is performed on the resulting training set. In the case of PET for sentiment classification, first a number of patterns encoding some form of task description are created to convert training examples to cloze questions; for each pattern, a pretrained language model is finetuned. Secondly, the ensemble of trained models annotates unlabeled data. Lastly, a classifier is trained on the resulting soft-labeled dataset.