Journal of Natural Language Processing
Online ISSN : 2185-8314
Print ISSN : 1340-7619
ISSN-L : 1340-7619
Technical Report (Peer-Reviewed)
Domain Knowledge Elicitation in a Corpus of Online Interview Dialogues on Culinary Arts
Taro OkahisaRibeka TanakaTakashi KodamaYin Jou HuangYugo MurawakiSadao Kurohashi
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2023 Volume 30 Issue 2 Pages 773-799

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Abstract

Interviews are an efficient way to elicit knowledge from experts in different domains. In this paper, we introduce a corpus of interview dialogues on subjects in culinary arts called CIDC. In collecting this data, interviewers played an active role in eliciting culinary knowledge from cooking experts. The corpus consists of 308 interview dialogues, each about 13 minutes long, comprising a total of 64,000 utterances. We used a videoconferencing tool to collect the data, which included the content that the participants shared on their screens. This approach allowed us to classify the facial expressions of the interlocutors from the video data. We also categorized the experts as either professionals or enthusiasts and the interviewers as skilled or unskilled. We expect this corpus to facilitate future research on knowledge elicitation mechanisms in interview dialogues.

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© 2023 The Association for Natural Language Processing
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