Consequences and Factors of Stylistic Differences in Human-Robot Dialogue
This work addresses the problem of designing robust dialogue systems for human-robot interaction by understanding natural user variation, though it is incremental as it identifies correlations without proposing new methods.
The paper analyzed stylistic differences in human-robot dialogue, finding that variations in verbosity and structure led to different miscommunication rates and correlated with user factors like trust and experience.
This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue. Differences in verbosity and structure (i.e., single-intent vs. multi-intent instructions) arose naturally without restrictions or prior guidance on how users should speak with the robot. Different styles were found to produce different rates of miscommunication, and correlations were found between style differences and individual user variation, trust, and interaction experience with the robot. Understanding potential consequences and factors that influence style can inform design of dialogue systems that are robust to natural variation from human users.