@inproceedings{ochs-etal-2020-brain,
title = "The Brain-{IHM} Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations",
author = "Ochs, Magalie and
Bertrand, Roxane and
Goujon, Aur{\'e}lie and
Bolger, Deirdre and
Dubarry, Anne-Sophie and
Blache, Philippe",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.85/",
pages = "676--683",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "This paper presents an original dataset of controlled interactions, focusing on the study of feedback items. It consists on recordings of different conversations between a doctor and a patient, played by actors. In this corpus, the patient is mainly a listener and produces different feedbacks, some of them being (voluntary) incongruent. Moreover, these conversations have been re-synthesized in a virtual reality context, in which the patient is played by an artificial agent. The final corpus is made of different movies of human-human conversations plus the same conversations replayed in a human-machine context, resulting in the first human-human/human-machine parallel corpus. The corpus is then enriched with different multimodal annotations at the verbal and non-verbal levels. Moreover, and this is the first dataset of this type, we have designed an experiment during which different participants had to watch the movies and give an evaluation of the interaction. During this task, we recorded participant`s brain signal. The Brain-IHM dataset is then conceived with a triple purpose: 1/ studying feedbacks by comparing congruent vs. incongruent feedbacks 2/ comparing human-human and human-machine production of feedbacks 3/ studying the brain basis of feedback perception."
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<abstract>This paper presents an original dataset of controlled interactions, focusing on the study of feedback items. It consists on recordings of different conversations between a doctor and a patient, played by actors. In this corpus, the patient is mainly a listener and produces different feedbacks, some of them being (voluntary) incongruent. Moreover, these conversations have been re-synthesized in a virtual reality context, in which the patient is played by an artificial agent. The final corpus is made of different movies of human-human conversations plus the same conversations replayed in a human-machine context, resulting in the first human-human/human-machine parallel corpus. The corpus is then enriched with different multimodal annotations at the verbal and non-verbal levels. Moreover, and this is the first dataset of this type, we have designed an experiment during which different participants had to watch the movies and give an evaluation of the interaction. During this task, we recorded participant‘s brain signal. The Brain-IHM dataset is then conceived with a triple purpose: 1/ studying feedbacks by comparing congruent vs. incongruent feedbacks 2/ comparing human-human and human-machine production of feedbacks 3/ studying the brain basis of feedback perception.</abstract>
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%0 Conference Proceedings
%T The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations
%A Ochs, Magalie
%A Bertrand, Roxane
%A Goujon, Aurélie
%A Bolger, Deirdre
%A Dubarry, Anne-Sophie
%A Blache, Philippe
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G eng
%F ochs-etal-2020-brain
%X This paper presents an original dataset of controlled interactions, focusing on the study of feedback items. It consists on recordings of different conversations between a doctor and a patient, played by actors. In this corpus, the patient is mainly a listener and produces different feedbacks, some of them being (voluntary) incongruent. Moreover, these conversations have been re-synthesized in a virtual reality context, in which the patient is played by an artificial agent. The final corpus is made of different movies of human-human conversations plus the same conversations replayed in a human-machine context, resulting in the first human-human/human-machine parallel corpus. The corpus is then enriched with different multimodal annotations at the verbal and non-verbal levels. Moreover, and this is the first dataset of this type, we have designed an experiment during which different participants had to watch the movies and give an evaluation of the interaction. During this task, we recorded participant‘s brain signal. The Brain-IHM dataset is then conceived with a triple purpose: 1/ studying feedbacks by comparing congruent vs. incongruent feedbacks 2/ comparing human-human and human-machine production of feedbacks 3/ studying the brain basis of feedback perception.
%U https://aclanthology.org/2020.lrec-1.85/
%P 676-683
Markdown (Informal)
[The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations](https://aclanthology.org/2020.lrec-1.85/) (Ochs et al., LREC 2020)
ACL