Can Conversational Agents Express Big Five Personality Traits through Language?: Evaluating a Psychologically-Informed Language Generator

Conversation is an essential component of social behavior, one of the primary means by which humans express intentions, beliefs, emotions, attitudes and personality. Thus a key technical capability for dialogue-based conversational agents for interactive entertainment, therapeutic, or learning applications is the ability to support natural conversational interaction. To do so, natural language processing is often applied to allow users flexibility in what they say to the system, but the system’s output typically consists of highly handcrafted utterances, designed to portray a particular system personality or linguistic style. Although this approach produces high quality output, it leads to an authoring bottleneck, and makes it difficult to personalize or adapt the interaction. We propose a computational framework for system utterance generation that builds on the “Big Five” model of personality traits, and findings from psychology on the linguistic reflexes of personality. Our results show that our framework reliably generates utterances that humans perceive as manifesting each extreme of all Big Five traits, suggesting that the linguistic markers identified in previous work in naturally occurring language genres can be used to manifest personality in a controlled discourse situation of a conversational agent.