This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. By utilizing the word senses derived from a WSD model, our model enhances the metaphor detection process and outperforms other methods that rely solely on contextual embeddings or integrate only the basic definitions and other external knowledge. We evaluate our approach on various benchmark datasets and compare it with strong baselines, indicating the effectiveness in advancing metaphor detection.

翻译：本文提出ContrastWSD，一种基于RoBERTa的隐喻检测模型，该模型整合了隐喻识别程序（MIP）与词义消歧（WSD），通过提取并对比词语的语境义与基本义，判断其在句子中是否被隐喻性使用。通过利用WSD模型获取的词义信息，我们的模型增强了隐喻检测过程，并优于仅依赖语境嵌入或仅整合基本定义及其他外部知识的现有方法。我们在多个基准数据集上评估了该方法，并与强基线模型进行对比，实验结果表明了该模型在推进隐喻检测研究中的有效性。