  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.
