FGVIrony: A Chinese Dataset of Fine-grained Verbal Irony

Abstract

Verbal irony, identified as an incongruity between a speaker’s intended meaning and their explicit linguistic expression, often manifests in nuanced forms such as irony, sarcasm, and satire. Current research often fails to differentiate among these fine-grained categories of verbal irony, primarily focusing on generic detection in texts. Therefore, in this work, we introduce a new task for fine-grained verbal irony recognition, aims not only to identify the presence of verbal irony but also distinguish among its various types. Besides, a notable gap in existing research is the lack of datasets tailored to fine-grained verbal irony, particularly in the context of the Chinese language. To tackle this issue, we have developed the FGVIrony dataset, which comprises 10,252 samples, including 6,790 non-ironic and 3,462 verbal ironic instances, further classified into 1,796 instances of irony, 362 of sarcasm, 577 of satire, 192 overstatements, 79 understatements, and 456 rhetorical questions. On the FGVIrony dataset, we explore the challenges of accurately identifying fine-grained verbal irony. Additionally, to investigate the limitations inherent in current methodologies, we propose a cascaded multi-prompt learning approach, CMP, designed to enhance recognition accuracy. The FGVIrony dataset is available at .