Can Yes-No Question-Answering Models be Useful for Few-Shot Metaphor Detection?

Metaphor detection has been a challenging task in the NLP domain both before and after the emergence of transformer-based language models. The difficulty lies in subtle semantic nuances that are required to be able to detect metaphor and in the scarcity of labeled data. We explore few-shot setups for metaphor detection, and also introduce new question-answering data that can enhance classifiers that are trained on a small amount of data. We formulate the classification task as a question-answering one, and train a question-answering model. We perform extensive experiments for few shot on several architectures and report the results of several strong baselines.