@inproceedings{yang-etal-2025-sarcasm,
    title = "Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning",
    author = "Yang, Qi  and
      Zeng, Jingjie  and
      Yang, Liang  and
      Ma, Kai  and
      Lin, Hongfei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.570/",
    doi = "10.18653/v1/2025.findings-emnlp.570",
    pages = "10773--10785",
    ISBN = "979-8-89176-335-7",
    abstract = "Sarcasm detection is a crucial yet challenging task in natural language processing. Existing methods primarily rely on supervised learning or prompt engineering, which often struggle to capture the complex reasoning process required for effective sarcasm detection. This paper proposes a novel approach that decomposes sarcasm detection into three fundamental dimensions: language, context, and emotion, meticulously modeling the sarcasm reasoning process. To enhance the quality of reasoning, we employ reinforcement learning algorithms and design customized reward models for each dimension. We utilize five widely used sarcasm detection datasets and annotate the sarcasm reasoning process from these three dimensions to improve the performance of the reward models. Experiments demonstrate that our method outperforms state-of-the-art baseline methods in most cases. Additionally, we observe the central role of emotional contrast in sarcasm detection. Our research provides empirical insights into the mechanism of sarcasm, emphasizing that emotional contrast is at its core, supported by linguistic and contextual cues."
}