The Tutorbot Corpus — A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue

Maria Koutsombogera, Samer Al Moubayed, Bajibabu Bollepalli, Ahmed Hussen Abdelaziz, Martin Johansson, José David Aguas Lopes, Jekaterina Novikova, Catharine Oertel, Kalin Stefanov, Gül Varol


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 tutor’s 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.
Anthology ID:
L14-1641
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4196–4201
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/832_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/832_Paper.pdf