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of 61
pro vyhledávání: '"Higashinaka, Ryuichiro"'
Dialogue datasets are crucial for deep learning-based task-oriented dialogue system research. While numerous English language multi-domain task-oriented dialogue datasets have been developed and contributed to significant advancements in task-oriente
Externí odkaz:
http://arxiv.org/abs/2403.17319
Autor:
Minato, Takashi, Higashinaka, Ryuichiro, Sakai, Kurima, Funayama, Tomo, Nishizaki, Hiromitsu, Naga, Takayuki
We have held dialogue robot competitions in 2020 and 2022 to compare the performances of interactive robots using an android that closely resembles a human. In 2023, the third competition DRC2023 was held. The task of DRC2023 was designed to be more
Externí odkaz:
http://arxiv.org/abs/2401.03547
The Dialogic Robot Competition 2023 (DRC2023) is a competition for humanoid robots (android robots that closely resemble humans) to compete in interactive capabilities. This is the third year of the competition. The top four teams from the preliminar
Externí odkaz:
http://arxiv.org/abs/2312.14430
Autor:
Hirai, Ryu, Iizuka, Shinya, Iseno, Haruhisa, Guo, Ao, Jiang, Jingjing, Ohashi, Atsumoto, Higashinaka, Ryuichiro
At the Dialogue Robot Competition 2023 (DRC2023), which was held to improve the capability of dialogue robots, our team developed a system that could build common ground and take more natural turns based on user utterance texts. Our system generated
Externí odkaz:
http://arxiv.org/abs/2312.13816
Publikováno v:
Proceedings of the 2023 AAAI Fall Symposia, vol.2, no. 1, 2023
For generative AIs to be trustworthy, establishing transparent common grounding with humans is essential. As a preparation toward human-model common grounding, this study examines the process of model-model common grounding. In this context, common g
Externí odkaz:
http://arxiv.org/abs/2311.05851
Autor:
Minato, Takashi, Higashinaka, Ryuichiro, Sakai, Kurima, Funayama, Tomo, Nishizaki, Hiromitsu, Nagai, Takayuki
Although many competitions have been held on dialogue systems in the past, no competition has been organized specifically for dialogue with humanoid robots. As the first such attempt in the world, we held a dialogue robot competition in 2020 to compa
Externí odkaz:
http://arxiv.org/abs/2210.12863
The proceedings contain papers on the dialogue systems developed by the twelve teams participating in DRC2022, as well as an overview paper summarizing the competition.
Comment: Proceedings of the Dialogue Robot Competition 2022
Comment: Proceedings of the Dialogue Robot Competition 2022
Externí odkaz:
http://arxiv.org/abs/2210.12034
Autor:
Hirai, Ryu, Ohashi, Atsumoto, Guo, Ao, Shiroma, Hideki, Zhou, Xulin, Tone, Yukihiko, Iizuka, Shinya, Higashinaka, Ryuichiro
To improve the interactive capabilities of a dialogue system, e.g., to adapt to different customers, the Dialogue Robot Competition (DRC2022) was held. As one of the teams, we built a dialogue system with a pipeline structure containing four modules.
Externí odkaz:
http://arxiv.org/abs/2210.09518
When a natural language generation (NLG) component is implemented in a real-world task-oriented dialogue system, it is necessary to generate not only natural utterances as learned on training data but also utterances adapted to the dialogue environme
Externí odkaz:
http://arxiv.org/abs/2209.07873
Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning. However, these methods are limited in that they can
Externí odkaz:
http://arxiv.org/abs/2207.12185