Search Results for author: Koh Mitsuda

Found 10 papers, 0 papers with code

Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task

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.

A Speculative and Tentative Common Ground Handling for Efficient Composition of Uncertain Dialogue

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.

An Integration of Pre-Trained Speech and Language Models for End-to-End Speech Recognition

no code implementations6 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

Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs

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.

Question Answering

Investigating the Effect of Conveying Understanding Results in Chat-Oriented Dialogue Systems

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.

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