Did You Really Mean That? Teaching Robots to Catch Sarcasm

Humans heavily rely on subtle pragmatic cues, such as sarcasm, in their social interactions, including with robots. When robots fail to recognise nonliteral intent, their responses can appear rigid or socially inappropriate. This work proposes a proof-of-concept real-time sarcasm detection system that integrates textual and audio cues to enable appropriate responses by the robot. Multiple sarcasm detection approaches were selected and comparatively evaluated with respect to their latency under real-time conditions. Results from an online study show that robots employing text- and tone-based sarcasm detection and reacting according to social rules were perceived as significantly more anthropomorphic and intelligent than those not understanding sarcasm. The findings suggest that sarcasm-awareness through text and tone input enhances the perceived social intelligence and humanlikeness of robots. Furthermore, the style of sarcasm used by the human interaction partner influences how anthropomorphic and animate the robot's responses are perceived to be, but does not significantly affect likeability.