@inproceedings{kalashnikova-etal-2024-linguistic-nudges,
title = "Linguistic Nudges and Verbal Interaction with Robots, Smart-Speakers, and Humans",
author = "Kalashnikova, Natalia and
Vasilescu, Ioana and
Devillers, Laurence",
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.923",
pages = "10555--10564",
abstract = "This paper describes a data collection methodology and emotion annotation of dyadic interactions between a human, a Pepper robot, a Google Home smart-speaker, or another human. The collected 16 hours of audio recordings were used to analyze the propensity to change someone{'}s opinions about ecological behavior regarding the type of conversational agent, the kind of nudges, and the speaker{'}s emotional state. We describe the statistics of data collection and annotation. We also report the first results, which showed that humans change their opinions on more questions with a human than with a device, even against mainstream ideas. We observe a correlation between a certain emotional state and the interlocutor and a human{'}s propensity to be influenced. We also reported the results of the studies that investigated the effect of human likeness on speech using our data.",
}
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<abstract>This paper describes a data collection methodology and emotion annotation of dyadic interactions between a human, a Pepper robot, a Google Home smart-speaker, or another human. The collected 16 hours of audio recordings were used to analyze the propensity to change someone’s opinions about ecological behavior regarding the type of conversational agent, the kind of nudges, and the speaker’s emotional state. We describe the statistics of data collection and annotation. We also report the first results, which showed that humans change their opinions on more questions with a human than with a device, even against mainstream ideas. We observe a correlation between a certain emotional state and the interlocutor and a human’s propensity to be influenced. We also reported the results of the studies that investigated the effect of human likeness on speech using our data.</abstract>
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%0 Conference Proceedings
%T Linguistic Nudges and Verbal Interaction with Robots, Smart-Speakers, and Humans
%A Kalashnikova, Natalia
%A Vasilescu, Ioana
%A Devillers, Laurence
%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 kalashnikova-etal-2024-linguistic-nudges
%X This paper describes a data collection methodology and emotion annotation of dyadic interactions between a human, a Pepper robot, a Google Home smart-speaker, or another human. The collected 16 hours of audio recordings were used to analyze the propensity to change someone’s opinions about ecological behavior regarding the type of conversational agent, the kind of nudges, and the speaker’s emotional state. We describe the statistics of data collection and annotation. We also report the first results, which showed that humans change their opinions on more questions with a human than with a device, even against mainstream ideas. We observe a correlation between a certain emotional state and the interlocutor and a human’s propensity to be influenced. We also reported the results of the studies that investigated the effect of human likeness on speech using our data.
%U https://aclanthology.org/2024.lrec-main.923
%P 10555-10564
Markdown (Informal)
[Linguistic Nudges and Verbal Interaction with Robots, Smart-Speakers, and Humans](https://aclanthology.org/2024.lrec-main.923) (Kalashnikova et al., LREC-COLING 2024)
ACL