Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions (2022)

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.

## introduction and related work alignment is the convergence of behavior among speakers and plays an important role in designing the strategies of dialogue systems because it is associated with user engagement @xcite and task success @xcite @xcite . however, few studies have looked at how this relationship differs in multiparty versus dyadic task-oriented dialogues involving humans and a dialogue agent. this gap prevents us from inferring appropriate alignment strategies for dialogue agents across different group sizes. teachable agents act as peers that learners teach via dialogue. these agents have been shown to facilitate learning due to the effect of learning by teaching @xcite and the rapport the agents build with learners @xcite . inspired by theories suggesting that rapport is tied to verbal and non-verbal alignment @xcite , prior educational research has explored relationships between rapport with agents and various forms of alignment such as lexical @xcite and acoustic-prosodic @xcite alignment. while lexical alignment (the focus of this paper) in educational dialogue has been an active research area, prior studies are limited by 1) alignment measures (repetition of single words @xcite @xcite or manual annotations of semantics @xcite ) or 2) dialogue settings (they studied only dyadic interactions with an agent @xcite @xcite , dyadic interactions between humans @xcite @xcite , or multi-party human interactions @xcite ). multi-party interactions involving an agent remain to be explored with more sophisticated automated measures that can deal with the alignment of a sequence of words. therefore, we extend the past work on lexical alignment in educational dialogue in two ways. first, we view lexical alignment as initiation and repetition of shared lexical expressions, which are automatically extracted from dialogue excerpts and can consist of multiple words @xcite . along with these metrics, we propose another viewpoint, activeness, which quantifies to what extent a speaker is involved in the establishment of shared expressions independent of their partner. second, we investigate collaborative teaching where two learners co-teach a teachable nao robot named emma. we compare how individual learners align with emma and how alignment relates to rapport with her in this human-human-robot (h-h-r) setting versus in a one-on-one humanrobot (h-r) setting. although, outside of education, some researchers have also investigated h-h-r interactions (e.g., @xcite , exploring alignment specifically in educational settings is useful because optimal alignment strategies differ from task to task @xcite . through our comparisons, this paper provides the groundwork for designing different alignment strategies for teachable agents in the h-r and h-h-r settings. ## results and discussion individual alignment across h-r and h-h-r conditions (h1). we tested h1 by comparing mean (sd) h-r (@xmath0.594 (.052) .462 (.077) ee_student .189 (.033) .171 (.050) er_emma** .494 (.031) .421 (.079) ee_emma* .087 (.024) .119 (.037) ied .155 (.111) .158 (.127) ## conclusion we examined relationships between lexical alignment and rapport with a teachable agent in oneon-one (h-r) and collaborative (h-h-r) teaching. our methods expand prior literature by comparing alignment behavior in h-r and h-h-r settings and extending recent work by @xcite to the speaker-level act of activeness in the alignment process. our results imply learners' lexical alignment with teachable agents may not always increase rapport with a teachable agent, unlike predictions from alignment theories @xcite largely based on human-human interactions. future work can expand our work by looking at the role of h-h portions of h-h-r interactions in their h-r portion and the effect of miscommunication as an intermediate variable on the negative correlations between rapport and learners' alignment and by extending the measures to multi-party settings without disentanglement.