Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences

Filip Radlinski, Krisztian Balog, Bill Byrne, Karthik Krishnamoorthi

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Abstract
Conversational recommendation has recently attracted significant attention. As systems must understand users’ preferences, training them has called for conversational corpora, typically derived from task-oriented conversations. We observe that such corpora often do not reflect how people naturally describe preferences. We present a new approach to obtaining user preferences in dialogue: Coached Conversational Preference Elicitation. It allows collection of natural yet structured conversational preferences. Studying the dialogues in one domain, we present a brief quantitative analysis of how people describe movie preferences at scale. Demonstrating the methodology, we release the CCPE-M dataset to the community with over 500 movie preference dialogues expressing over 10,000 preferences.
Anthology ID:
W19-5941
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Editors:
Satoshi Nakamura, Milica Gasic, Ingrid Zukerman, Gabriel Skantze, Mikio Nakano, Alexandros Papangelis, Stefan Ultes, Koichiro Yoshino
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
353–360
Language:
URL:
https://aclanthology.org/W19-5941
DOI:
10.18653/v1/W19-5941
Bibkey:
Cite (ACL):
Filip Radlinski, Krisztian Balog, Bill Byrne, and Karthik Krishnamoorthi. 2019. Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 353–360, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences (Radlinski et al., SIGDIAL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W19-5941.pdf
Data
CCPE-MCoached Conversational Preference Elicitation