Sarcasm is a pragmatic phenomenon in which speakers convey meanings that diverge from literal content, relying on an interaction between semantics and prosodic expression. However, how these cues jointly contribute to the recognition of sarcasm remains poorly understood. We propose a computational framework that models sarcasm as the integration of semantic interpretation and prosodic realization. Semantic cues are derived from an LLaMA 3 model fine-tuned to capture discourse-level markers of sarcastic intent, while prosodic cues are extracted through semantically aligned utterances drawn from a database of sarcastic speech, providing prosodic exemplars of sarcastic delivery. Using a speech synthesis testbed, perceptual evaluations show that semantic and prosodic cues enhance perceived sarcasm, with the combined system achieving the best downstream F1 while maintaining high subjective sarcasm ratings. These findings highlight the complementary roles of semantics and prosody in pragmatic interpretation and illustrate how modeling can shed light on the mechanisms underlying sarcastic communication.

翻译：讽刺是一种语用现象，说话者通过语义与韵律表达之间的相互作用，传递偏离字面含义的意义。然而，这些线索如何共同促成讽刺的识别仍缺乏深入理解。我们提出一个计算框架，将讽刺建模为语义理解与韵律实现的融合。语义线索源自经过微调的LLaMA 3模型，该模型能够捕捉讽刺意图的话语层面标记；韵律线索则通过从讽刺性言语数据库中提取的语义对齐话语获得，提供讽刺表达的韵律范例。基于语音合成测试平台的感知评估表明，语义与韵律线索能增强讽刺感知，其中组合系统在保持较高主观讽刺评分的同时取得了最佳下游F1值。这些发现凸显了语义与韵律在语用理解中的互补作用，并揭示了建模如何阐释讽刺交流背后的机制。