@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 = "https://aclanthology.org/L14-1641/",
    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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        <title>The Tutorbot Corpus — A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue</title>
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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 https://aclanthology.org/L14-1641/
%P 4196-4201
Markdown (Informal)
[The Tutorbot Corpus — A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue](https://aclanthology.org/L14-1641/) (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).