Abstract
Many surely dream of being able to chat with his/her favorite anime characters from an early age. To make such a dream possible, we propose an efficient method for constructing a system that enables users to text chat with existing anime characters. We tackled two research problems to generate verbal and nonverbal behaviors for a text-chat agent system of an existing character. In the generation of verbal behavior, it is a major issue to be able to generate utterance text that reflects the personality of existing characters in response to any user questions. For this problem, we propose the use role play-based question-answering to efficiently collect high-quality paired data of user’s questions and system’s answers reflecting the personality of an anime character. We also propose a new utterance generation method that uses a neural translation model with the collected data. Rich and natural expressions of nonverbal behavior greatly enhance the appeal of agent systems. However, not all existing anime characters move as naturally and as diversely as humans. Therefore, we propose a method that can automatically generate whole-body motion from spoken text in order to make it so that anime characters have human-like and natural movements. In addition to these movements, we try to add a small amount of characteristic movement on a rule basis to reflect personality. We created a text-dialogue agent system of a popular existing anime character using our proposed generation methods. As a result of a subjective evaluation of the implemented system, our models for generating verbal and nonverbal behavior improved the impression of the agent’s responsiveness and reflected the personality of the character. In addition, generating characteristic motions with a small amount of on the basis of heuristic rules was not effective, but rather the character generated by our generation model that reflects the average motion of persons had more personality. Therefore, our proposed methods for generating verbal and nonverbal behaviors and the construction method will greatly contribute to the realization of text-dialogue-agent systems of existing characters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fuchi, T., Takagi, S.: Japanese morphological analyzer using word cooccurrence -JTAG. In: International Conference on Computational Linguistics, pp. 409–413 (1998)
Higashinaka, R., et al.: Towards an open-domain conversational system fully based on natural language processing. In: International Conference on Computational Linguistics, pp. 928–939 (2014)
Higashinaka, R., Sadamitsu, K., Saito, K., Kobayashi, N.: Question answering technology for pinpointing answers to a wide range of questions. NTT Tech. Rev. 11(7) (2013)
Imamura, K.: Analysis of Japanese dependency analysis of semi-spoken words by series labeling. In: Proceedings of the Annual Meeting of the Association for Natural Language Processing, pp. 518–521 (2007)
Ishi, C.T., Haas, J., Wilbers, F.P., Ishiguro, H., Hagita, N.: Analysis of head motions and speech, and head motion control in an android. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 548–553 (2007)
Ishi, C.T., Ishiguro, H., Hagita, N.: Head motion during dialogue speech and nod timing control in humanoid robots. In: ACM/IEEE International Conference on Human-Robot Interaction, pp. 293–300 (2010)
Kadono, Y., Takase, Y., Nakano, Y.I.: Generating iconic gestures based on graphic data analysis and clustering. In: The Eleventh ACM/IEEE International Conference on Human Robot Interaction, HRI 2016, Piscataway, NJ, USA, pp. 447–448. IEEE Press (2016)
Leuski, A., Patel, R., Traum, D., Kennedy, B.: Building effective question answering characters. In: Proceedings of the SIGDIAL, pp. 18–27 (2009)
Lohse, M., Rothuis, R., Gallego-Pérez, J., Karreman, D.E., Evers, V.: Robot gestures make difficult tasks easier: the impact of gestures on perceived workload and task performance. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2014, pp. 1459–1466. ACM, New York (2014)
McNeill, D.: Hand and Mind: What Gestures Reveal About Thought. University of Chicago, Chicago Press (1996)
Meguro, T., Higashinaka, R., Minami, Y., Dohsaka, K.: Controlling listening-oriented dialogue using partially observable Markov decision processes. In: International Conference on Computational Linguistics, pp. 761–769 (2010)
Van Ments, M.: The Effective Use of Role Play: Practical Techniques for Improving Learning. Kogan Page Publishers, London (1999)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the NIPS, pp. 3111–3119 (2013)
Miyazaki, C., Hirano, T., Higashinaka, R., Matsuo, Y.: Towards an entertaining natural language generation system: linguistic peculiarities of Japanese fictional characters. In: Proceedings of the SIGDIAL, pp. 319–328 (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. CoRR, abs/1503.03832 (2015)
Sekine, S., Sudo, K., Nobata, C.: Extended named entity hierarchy. In: Proceedings of the LREC (2002)
Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015)
Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., Sloetjes, H : Elan a professional framework for multimodality research. In: International Conference on Language Resources and Evaluation (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ishii, R. et al. (2020). Methods of Efficiently Constructing Text-Dialogue-Agent System Using Existing Anime Character. In: Stephanidis, C., et al. HCI International 2020 – Late Breaking Papers: Interaction, Knowledge and Social Media. HCII 2020. Lecture Notes in Computer Science(), vol 12427. Springer, Cham. https://doi.org/10.1007/978-3-030-60152-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-60152-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60151-5
Online ISBN: 978-3-030-60152-2
eBook Packages: Computer ScienceComputer Science (R0)