@inproceedings{nakae-inaba-2025-task,
title = "Task Proficiency-Aware Dialogue Analysis in a Real-Time Cooking Game Environment",
author = "Nakae, Kaito and
Inaba, Michimasa",
editor = "B{\'e}chet, Fr{\'e}d{\'e}ric and
Lef{\`e}vre, Fabrice and
Asher, Nicholas and
Kim, Seokhwan and
Merlin, Teva",
booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2025",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sigdial-1.57/",
pages = "764--779",
abstract = "Real-time collaborative dialogue tasks require dynamic, instantaneous decision-making and seamless coordination between participants, yet most existing studies on cooperative dialogues primarily focus on turn-based textual environments. This study addresses the critical gap in understanding human-human interaction patterns within dynamic, real-time collaborative scenarios. In this paper, we present a novel dataset collected from a real-time collaborative cooking game environment inspired by the popular game ``Overcooked.'' Our dataset comprises detailed annotations of participants' task proficiency levels, game scores, game action logs, and transcribed voice dialogues annotated with dialogue act tags. Participants exhibited a broad range of gaming experience, from highly proficient players to those with minimal exposure to gaming controls. Through comprehensive analysis, we explore how individual differences in task proficiency influence dialogue patterns and collaborative outcomes. Our findings reveal key dialogue acts and adaptive communication strategies crucial for successful real-time collaboration. Furthermore, this study provides valuable insights into designing adaptive dialogue systems capable of dynamically adjusting interaction strategies based on user proficiency, paving the way for more effective human-AI collaborative systems. The dataset introduced in this study is publicly available at: https://github.com/UEC-InabaLab/OverCookedChat."
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<abstract>Real-time collaborative dialogue tasks require dynamic, instantaneous decision-making and seamless coordination between participants, yet most existing studies on cooperative dialogues primarily focus on turn-based textual environments. This study addresses the critical gap in understanding human-human interaction patterns within dynamic, real-time collaborative scenarios. In this paper, we present a novel dataset collected from a real-time collaborative cooking game environment inspired by the popular game “Overcooked.” Our dataset comprises detailed annotations of participants’ task proficiency levels, game scores, game action logs, and transcribed voice dialogues annotated with dialogue act tags. Participants exhibited a broad range of gaming experience, from highly proficient players to those with minimal exposure to gaming controls. Through comprehensive analysis, we explore how individual differences in task proficiency influence dialogue patterns and collaborative outcomes. Our findings reveal key dialogue acts and adaptive communication strategies crucial for successful real-time collaboration. Furthermore, this study provides valuable insights into designing adaptive dialogue systems capable of dynamically adjusting interaction strategies based on user proficiency, paving the way for more effective human-AI collaborative systems. The dataset introduced in this study is publicly available at: https://github.com/UEC-InabaLab/OverCookedChat.</abstract>
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%0 Conference Proceedings
%T Task Proficiency-Aware Dialogue Analysis in a Real-Time Cooking Game Environment
%A Nakae, Kaito
%A Inaba, Michimasa
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F nakae-inaba-2025-task
%X Real-time collaborative dialogue tasks require dynamic, instantaneous decision-making and seamless coordination between participants, yet most existing studies on cooperative dialogues primarily focus on turn-based textual environments. This study addresses the critical gap in understanding human-human interaction patterns within dynamic, real-time collaborative scenarios. In this paper, we present a novel dataset collected from a real-time collaborative cooking game environment inspired by the popular game “Overcooked.” Our dataset comprises detailed annotations of participants’ task proficiency levels, game scores, game action logs, and transcribed voice dialogues annotated with dialogue act tags. Participants exhibited a broad range of gaming experience, from highly proficient players to those with minimal exposure to gaming controls. Through comprehensive analysis, we explore how individual differences in task proficiency influence dialogue patterns and collaborative outcomes. Our findings reveal key dialogue acts and adaptive communication strategies crucial for successful real-time collaboration. Furthermore, this study provides valuable insights into designing adaptive dialogue systems capable of dynamically adjusting interaction strategies based on user proficiency, paving the way for more effective human-AI collaborative systems. The dataset introduced in this study is publicly available at: https://github.com/UEC-InabaLab/OverCookedChat.
%U https://aclanthology.org/2025.sigdial-1.57/
%P 764-779
Markdown (Informal)
[Task Proficiency-Aware Dialogue Analysis in a Real-Time Cooking Game Environment](https://aclanthology.org/2025.sigdial-1.57/) (Nakae & Inaba, SIGDIAL 2025)
ACL