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)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2020.lrec-1.668.pdf
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