@inproceedings{isaka-etal-2024-analysis,
title = "Analysis of Sensation-transfer Dialogues in Motorsports",
author = "Isaka, Takeru and
Otsuka, Atsushi and
Toshima, Iwaki",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.79/",
pages = "876--886",
abstract = "Clarifying the effects of subjective ideas on group performance is essential for future dialogue systems to improve mutual understanding among humans and group creativity. However, there has been little focus on dialogue research on quantitatively analyzing the effects of the quality and quantity of subjective information contained in dialogues on group performance. We hypothesize that the more subjective information interlocutors exchange, the better the group performance in collaborative work. We collected dialogues between drivers and engineers in motorsports when deciding how the car should be tuned as a suitable case to verify this hypothesis. Our analysis suggests that the greater the amount of subjective information (which we defined as {\textquotedblleft}sensation{\textquotedblright}) in the driver`s utterances, the greater the race performance and driver satisfaction with the car`s tuning. The results indicate that it is essential for the development of dialogue research to create a corpus of situations that require high performance through collaboration among experts with different backgrounds but who have mastered their respective fields."
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<abstract>Clarifying the effects of subjective ideas on group performance is essential for future dialogue systems to improve mutual understanding among humans and group creativity. However, there has been little focus on dialogue research on quantitatively analyzing the effects of the quality and quantity of subjective information contained in dialogues on group performance. We hypothesize that the more subjective information interlocutors exchange, the better the group performance in collaborative work. We collected dialogues between drivers and engineers in motorsports when deciding how the car should be tuned as a suitable case to verify this hypothesis. Our analysis suggests that the greater the amount of subjective information (which we defined as “sensation”) in the driver‘s utterances, the greater the race performance and driver satisfaction with the car‘s tuning. The results indicate that it is essential for the development of dialogue research to create a corpus of situations that require high performance through collaboration among experts with different backgrounds but who have mastered their respective fields.</abstract>
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%0 Conference Proceedings
%T Analysis of Sensation-transfer Dialogues in Motorsports
%A Isaka, Takeru
%A Otsuka, Atsushi
%A Toshima, Iwaki
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F isaka-etal-2024-analysis
%X Clarifying the effects of subjective ideas on group performance is essential for future dialogue systems to improve mutual understanding among humans and group creativity. However, there has been little focus on dialogue research on quantitatively analyzing the effects of the quality and quantity of subjective information contained in dialogues on group performance. We hypothesize that the more subjective information interlocutors exchange, the better the group performance in collaborative work. We collected dialogues between drivers and engineers in motorsports when deciding how the car should be tuned as a suitable case to verify this hypothesis. Our analysis suggests that the greater the amount of subjective information (which we defined as “sensation”) in the driver‘s utterances, the greater the race performance and driver satisfaction with the car‘s tuning. The results indicate that it is essential for the development of dialogue research to create a corpus of situations that require high performance through collaboration among experts with different backgrounds but who have mastered their respective fields.
%U https://aclanthology.org/2024.lrec-main.79/
%P 876-886
Markdown (Informal)
[Analysis of Sensation-transfer Dialogues in Motorsports](https://aclanthology.org/2024.lrec-main.79/) (Isaka et al., LREC-COLING 2024)
ACL
- Takeru Isaka, Atsushi Otsuka, and Iwaki Toshima. 2024. Analysis of Sensation-transfer Dialogues in Motorsports. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 876–886, Torino, Italia. ELRA and ICCL.