Fine-Grained Discourse for Metaphor Detection

Most current metaphor detection methods use restricted context, such as modeling the context of a single sentence. Considering the language environment of metaphors, we argue that combining broader discourse features has a greater impact on the improvement of metaphor detection performance. We propose a metaphor detection method based on fine-grained discourse, which embeds the current sentence and surrounding context in a weighted manner. With the help of fine-grained discourse, our model learns local and remote information as a reference for decision-making, and provides an efficient and natural method for metaphor detection tasks. Experimental results on VU Amsterdam Metaphor Corpus show that our technique surpasses the state-of-the-art models.