A Dual-Perspective Metaphor Detection Framework Using Large Language Models

TL;DR

This paper introduces DMD, a dual-perspective framework leveraging large language models and metaphor theories for more transparent and reliable metaphor detection, achieving state-of-the-art results.

Contribution

The paper presents a novel dual-perspective framework that combines implicit and explicit metaphor theories with self-judgment to improve metaphor detection using LLMs.

Findings

DMD outperforms previous methods on benchmark datasets.

The framework enhances transparency in metaphor detection.

Experimental results show improved accuracy and reliability.

Abstract

Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in their decision-making processes, which undermines the reliability of their predictions. Recent research indicates that LLMs (large language models) exhibit significant potential in metaphor detection. Nevertheless, their reasoning capabilities are constrained by predefined knowledge graphs. To overcome these limitations, we propose DMD, a novel dual-perspective framework that harnesses both implicit and explicit applications of metaphor theories to guide LLMs in metaphor detection and adopts a self-judgment mechanism to validate the responses from the aforementioned forms of guidance. In comparison to previous methods, our framework offers more transparent reasoning processes and delivers more reliable predictions. Experimental results prove the effectiveness of DMD, demonstrating state-of-the-art performance across widely-used datasets.