Expressions of style in information-seeking conversation with an embodied agent - Thomas, Paul; McDuff, Daniel; Czerwinski, Mary; Craswell, Nick (Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020)
Past work in information-seeking conversation has demonstrated that people exhibit different conversational styles – for example, in word choice or prosody – that differences in style lead to poorer conversations, and that partners actively align their styles over time. One might assume that this would also be true for conversations with an artificial agent such as Cortana, Siri, or Alexa; and that agents should therefore track and mimic a user's style. We examine this hypothesis with reference to a lab study, where 24 participants carried out relatively long information-seeking tasks with an embodied conversational agent. The agent combined topical language models with a conversational dialogue engine, style recognition and alignment modules. We see that "style" can be measured in human-to-agent conversation, although it looks somewhat different to style in human-to-human conversation and does not correlate with self-reported preferences. There is evidence that people align their style to the agent, and that conversations run more smoothly if the agent detects, and aligns to, the human's style as well.