The Bot on Speaking Terms: The Effects of Conversation Architecture on Perceptions of Conversational Agents

Abstract

Conversational agents mimic natural conversation to interact with users. Since the effectiveness of interactions strongly depends on users’ perception of agents, it is crucial to design agents’ behaviors to provide the intended user perceptions. Research on human-agent and human-human communication suggests that speech specifics are associated with perceptions of communicating parties, but there is a lack of systematic understanding of how speech specifics of agents affect users’ perceptions. To address this gap, we present a framework outlining the relationships between elements of agents’ conversation architecture (dialog strategy, content affectiveness, content style and speech format) and aspects of users’ perception (interaction, ability, sociability and humanness). Synthesized based on literature reviewed from the domains of HCI, NLP and linguistics (n=57), this framework demonstrates both the identified relationships and the areas lacking empirical evidence. We discuss the implications of the framework for conversation design and highlight the inconsistencies with terminology and measurements.