Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation
Qingqing Hong, Dongyu Zhang, Jiayi Lin, Dapeng Yin, Shuyue Zhu, Junli Wang · 2025
Sarcasm is a complex form of sentiment expression widely used in human daily life.Previous work primarily defines sarcasm as a form of verbal irony, which covers only a subset of real-world sarcastic expressions.However, sarcasm serves multifaceted functions and manifests itself through various rhetorical devices, such as echoic mention, rhetorical question and hyperbole.To fully capture its complexity, this paper investigates fine-grained sarcasm classification through the lens of rhetorical devices, and introduces RedSD, a RhEtorical Device-Aware Sarcasm Dataset with counterfactually augmented data.To construct the dataset, we extract sarcastic dialogues from situation comedies (i.e., sitcoms), and summarize nine rhetorical devices commonly employed in sarcasm.We then propose a rhetorical deviceaware counterfactual data generation pipeline facilitated by both Large Language Models (LLMs) and human revision.Additionally, we propose duplex counterfactual augmentation that generates counterfactuals for both sarcastic and non-sarcastic dialogues, to further enhance the scale and diversity of the dataset.Experimental results on the dataset demonstrate that fine-tuned models exhibit a more balanced performance compared to zero-shot models, including GPT-3.5 and LLaMA 3.1, underscoring the importance of integrating various rhetorical devices in sarcasm detection.1