no code implementations • ACL 2022 • Koh Mitsuda, Ryuichiro Higashinaka, Tingxuan Li, Sen Yoshida
Creating chatbots to behave like real people is important in terms of believability.
no code implementations • LREC 2022 • Saki Sudo, Kyoshiro Asano, Koh Mitsuda, Ryuichiro Higashinaka, Yugo Takeuchi
This study investigates how the grounding process is composed and explores new interaction approaches that adapt to human cognitive processes that have not yet been significantly studied.
no code implementations • LREC 2022 • Yuki Furuya, Koki Saito, Kosuke Ogura, Koh Mitsuda, Ryuichiro Higashinaka, Kazunori Takashio
Building common ground with users is essential for dialogue agent systems and robots to interact naturally with people.
no code implementations • LREC 2022 • Koh Mitsuda, Ryuichiro Higashinaka, Yuhei Oga, Sen Yoshida
To develop a dialogue system that can build common ground with users, the process of building common ground through dialogue needs to be clarified.
no code implementations • 2 Apr 2024 • Kei Sawada, Tianyu Zhao, Makoto Shing, Kentaro Mitsui, Akio Kaga, Yukiya Hono, Toshiaki Wakatsuki, Koh Mitsuda
AI democratization aims to create a world in which the average person can utilize AI techniques.
no code implementations • 6 Dec 2023 • Yukiya Hono, Koh Mitsuda, Tianyu Zhao, Kentaro Mitsui, Toshiaki Wakatsuki, Kei Sawada
Advances in machine learning have made it possible to perform various text and speech processing tasks, including automatic speech recognition (ASR), in an end-to-end (E2E) manner.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • LREC 2020 • Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata
This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data.
no code implementations • IJCNLP 2017 • Koh Mitsuda, Ryuichiro Higashinaka, Junji Tomita
In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects.