Consequences and Factors of Stylistic Differences in Human-Robot Dialogue

TL;DR

This study investigates natural stylistic variations in human-robot instruction dialogue, examining how differences in verbosity and structure affect communication effectiveness and relate to user trust and experience.

Contribution

It provides insights into how natural stylistic differences impact dialogue quality and identifies factors influencing these styles, informing more robust robot dialogue system design.

Findings

Different styles lead to varying miscommunication rates.

User trust and experience correlate with stylistic choices.

Natural variation in speech styles affects robot understanding.

Abstract

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.