Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs
Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata
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.- Anthology ID:
- 2020.lrec-1.668
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5433–5441
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.668
- DOI:
- Bibkey:
- Cite (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.
- Cite (Informal):
- Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (Kodama et al., LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.668.pdf
Export citation
@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",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
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 %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %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)
- Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (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.