@inproceedings{katada-etal-2024-collecting,
title = "Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment",
author = "Katada, Shun and
Takeda, Ryu and
Komatani, Kazunori",
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.312",
pages = "3518--3528",
abstract = "Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) sensor have been the subject of focus in affective computing regions to capture unexpressed emotional changes in a controlled experimental environment. In this study, we collect and analyze multimodal data with an EEG during a human-agent dialogue toward capturing unexpressed sentiment. Our contributions are as follows: (1) a new multimodal human-agent dialogue dataset is created, which includes not only text and audiovisual data but also frontal EEGs and physiological signals during the dialogue. In total, about 500-minute chat dialogues were collected from thirty participants aged 20 to 70. (2) We present a novel method for dealing with eye-blink noise for frontal EEGs denoising. This method applies facial landmark tracking to detect and delete eye-blink noise. (3) An experimental evaluation showed the effectiveness of the frontal EEGs. It improved sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels.",
}
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%0 Conference Proceedings
%T Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment
%A Katada, Shun
%A Takeda, Ryu
%A Komatani, Kazunori
%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 katada-etal-2024-collecting
%X Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) sensor have been the subject of focus in affective computing regions to capture unexpressed emotional changes in a controlled experimental environment. In this study, we collect and analyze multimodal data with an EEG during a human-agent dialogue toward capturing unexpressed sentiment. Our contributions are as follows: (1) a new multimodal human-agent dialogue dataset is created, which includes not only text and audiovisual data but also frontal EEGs and physiological signals during the dialogue. In total, about 500-minute chat dialogues were collected from thirty participants aged 20 to 70. (2) We present a novel method for dealing with eye-blink noise for frontal EEGs denoising. This method applies facial landmark tracking to detect and delete eye-blink noise. (3) An experimental evaluation showed the effectiveness of the frontal EEGs. It improved sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels.
%U https://aclanthology.org/2024.lrec-main.312
%P 3518-3528
Markdown (Informal)
[Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment](https://aclanthology.org/2024.lrec-main.312) (Katada et al., LREC-COLING 2024)
ACL