Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

Understand

A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels.

• Manually defining this mapping between words and labels requires both domain expertise and an understanding of the language model's abilities.
• To mitigate this issue, we devise an approach that automatically finds such a mapping given small amounts of training data.
• For a number of tasks, the mapping found by our approach performs almost as well as hand-crafted label-to-word mappings.