@inproceedings{radlinski-etal-2019-coached,
title = "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences",
author = "Radlinski, Filip and
Balog, Krisztian and
Byrne, Bill and
Krishnamoorthi, Karthik",
editor = "Nakamura, Satoshi and
Gasic, Milica and
Zukerman, Ingrid and
Skantze, Gabriel and
Nakano, Mikio and
Papangelis, Alexandros and
Ultes, Stefan and
Yoshino, Koichiro",
booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
month = sep,
year = "2019",
address = "Stockholm, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5941",
doi = "10.18653/v1/W19-5941",
pages = "353--360",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences
%A Radlinski, Filip
%A Balog, Krisztian
%A Byrne, Bill
%A Krishnamoorthi, Karthik
%Y Nakamura, Satoshi
%Y Gasic, Milica
%Y Zukerman, Ingrid
%Y Skantze, Gabriel
%Y Nakano, Mikio
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Yoshino, Koichiro
%S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
%D 2019
%8 September
%I Association for Computational Linguistics
%C Stockholm, Sweden
%F radlinski-etal-2019-coached
%X 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.
%R 10.18653/v1/W19-5941
%U https://aclanthology.org/W19-5941
%U https://doi.org/10.18653/v1/W19-5941
%P 353-360
Markdown (Informal)
[Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences](https://aclanthology.org/W19-5941) (Radlinski et al., SIGDIAL 2019)
ACL