RDVI: A Retrieval–Detection Framework for Verbal Irony Detection

Abstract

Verbal irony is a common form of expression used in daily communication, where the intended meaning is often opposite to the literal meaning. Accurately recognizing verbal irony is essential for any NLP application for which the understanding of the true user intentions is key to performing the underlying tasks. While existing research has made progress in this area, verbal irony often involves connotative knowledge that cannot be directly inferred from the text or its context, which limits the detection model’s ability to recognize and comprehend verbal irony. To address this issue, we propose a Retrieval–Detection method for Verbal Irony (RDVI). This approach improves the detection model’s ability to recognize and comprehend verbal irony by retrieving the connotative knowledge from the open domain and incorporating it into the model using prompt learning. The experimental results demonstrate that our proposed method outperforms state-of-the-art models.