Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions

TL;DR

This study compares lexical alignment in human-robot and human-human-robot interactions, revealing that students align more with robots and that the link between alignment and rapport is complex.

Contribution

It introduces data-driven measures of lexical alignment and compares alignment patterns across different interaction types involving robots.

Findings

Students align more with robots than with humans in mixed interactions.

The relationship between lexical alignment and rapport is more complex than previously thought.

Alignment measures based on shared expressions provide new insights into human-robot communication.

Abstract

Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work.