@inproceedings{koutsombogera-etal-2014-tutorbot,
title = "The Tutorbot Corpus {---} A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue",
author = {Koutsombogera, Maria and
Moubayed, Samer Al and
Bollepalli, Bajibabu and
Abdelaziz, Ahmed Hussen and
Johansson, Martin and
Lopes, Jos{\'e} David Aguas and
Novikova, Jekaterina and
Oertel, Catharine and
Stefanov, Kalin and
Varol, G{\"u}l},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/832_Paper.pdf",
pages = "4196--4201",
abstract = "This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutors behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.",
}
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<abstract>This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutors behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.</abstract>
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%0 Conference Proceedings
%T The Tutorbot Corpus — A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue
%A Koutsombogera, Maria
%A Moubayed, Samer Al
%A Bollepalli, Bajibabu
%A Abdelaziz, Ahmed Hussen
%A Johansson, Martin
%A Lopes, José David Aguas
%A Novikova, Jekaterina
%A Oertel, Catharine
%A Stefanov, Kalin
%A Varol, Gül
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F koutsombogera-etal-2014-tutorbot
%X This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutors behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/832_Paper.pdf
%P 4196-4201
Markdown (Informal)
[The Tutorbot Corpus — A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue](http://www.lrec-conf.org/proceedings/lrec2014/pdf/832_Paper.pdf) (Koutsombogera et al., LREC 2014)
ACL
- Maria Koutsombogera, Samer Al Moubayed, Bajibabu Bollepalli, Ahmed Hussen Abdelaziz, Martin Johansson, José David Aguas Lopes, Jekaterina Novikova, Catharine Oertel, Kalin Stefanov, and Gül Varol. 2014. The Tutorbot Corpus — A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4196–4201, Reykjavik, Iceland. European Language Resources Association (ELRA).