Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning

摘要

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.