Takashi Kodama author Ryuichiro Higashinaka author Koh Mitsuda author Ryo Masumura author Yushi Aono author Ryuta Nakamura author Noritake Adachi author Hidetoshi Kawabata author 2020-may text English eng European Language Resources Association Marseille, France conference publication 979-10-95546-34-4 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. kodama-etal-2020-generating https://www.aclweb.org/anthology/2020.lrec-1.668 2020-may 5433 5441