@inproceedings{asano-etal-2022-comparison,
title = "Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions",
author = "Asano, Yuya and
Litman, Diane and
Yu, Mingzhi and
Lobczowski, Nikki and
Nokes-Malach, Timothy and
Kovashka, Adriana and
Walker, Erin",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.57/",
doi = "10.18653/v1/2022.sigdial-1.57",
pages = "615--622",
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."
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%0 Conference Proceedings
%T Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions
%A Asano, Yuya
%A Litman, Diane
%A Yu, Mingzhi
%A Lobczowski, Nikki
%A Nokes-Malach, Timothy
%A Kovashka, Adriana
%A Walker, Erin
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F asano-etal-2022-comparison
%X 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.
%R 10.18653/v1/2022.sigdial-1.57
%U https://aclanthology.org/2022.sigdial-1.57/
%U https://doi.org/10.18653/v1/2022.sigdial-1.57
%P 615-622
Markdown (Informal)
[Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions](https://aclanthology.org/2022.sigdial-1.57/) (Asano et al., SIGDIAL 2022)
ACL