arXiv:2510.07096v3 Announce Type: replace Abstract: 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 extracte
This research builds on recent advances in large language models and speech synthesis, indicating a growing sophistication in AI's ability to model complex human communication nuances.
The ability of AI to accurately model and synthesize 'sarcastic speech' signifies an advancement in emotional and contextual understanding, critical for more natural human-AI interaction and nuanced communication systems.
AI models are moving beyond literal interpretation towards understanding and generating pragmatic phenomena, enhancing their applicability in sensitive communicative tasks.
- · AI speech synthesis developers
- · AI language model researchers
- · Customer service automation
More realistic and context-aware AI conversational agents become feasible.
Potential for AI to detect and generate sarcasm in various applications, from content creation to social interaction analysis.
Ethical considerations around deceptive AI communication and the weaponization of emotional intelligence in AI grow more complex.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL