Merely Judging Metaphor is Not Enough: Research on Reasonable Metaphor Detection

Abstract

Metaphor, as an advanced form of cognition, is challenging to understand their meaning. Current metaphor detection tasks only provide labels (i.e., metaphor or literal) without interpreting how to understand them. In this paper, we improve the metaphor detection task and explore the reason of metaphor. To the best of our knowledge, we are the first work to reason about metaphor using mainstream Large Language Models (LLMs). Specifically, we utilized ChatGPT3.5 to expand the mainstream datasets in current metaphor detection, including VUA ALL, TroFi, and MOH-X. We input the original sentence, target word, and usage (metaphor or literal) into ChatGPT, guiding it to generate corresponding metaphor reason. Then, we designed supervised baseline experiments (e.g., RoBERTa, GPT-2) and zero-shot experiments with LLMs (e.g., LLaMA3). For the results generated by the above experiments, we provided the case study. We devised four methods that include manual evaluation to evaluate the reason performance of the model, and discussed extensively the advantages and disadvantages of these evaluation methods. Our code is available at https://github.com/yc-cy/Metaphorical-Reasoning.