@inproceedings{kodama-etal-2020-generating,
title = "Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs",
author = "Kodama, Takashi and
Higashinaka, Ryuichiro and
Mitsuda, Koh and
Masumura, Ryo and
Aono, Yushi and
Nakamura, Ryuta and
Adachi, Noritake and
Kawabata, Hidetoshi",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.668",
pages = "5433--5441",
abstract = "This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.</abstract>
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%0 Conference Proceedings
%T Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs
%A Kodama, Takashi
%A Higashinaka, Ryuichiro
%A Mitsuda, Koh
%A Masumura, Ryo
%A Aono, Yushi
%A Nakamura, Ryuta
%A Adachi, Noritake
%A Kawabata, Hidetoshi
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F kodama-etal-2020-generating
%X This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.
%U https://aclanthology.org/2020.lrec-1.668
%P 5433-5441
Markdown (Informal)
[Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs](https://aclanthology.org/2020.lrec-1.668) (Kodama et al., LREC 2020)
ACL
- Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, and Hidetoshi Kawabata. 2020. Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5433–5441, Marseille, France. European Language Resources Association.