Toshinori Sato


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A Follow-up Study on Evaluation Metrics Using Follow-up Utterances
Toshiki Kawamoto | Yuki Okano | Takato Yamazaki | Toshinori Sato | Kotaro Funakoshi | Manabu Okumura
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

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Bridging the Gap between Subword and Character Segmentation in Pretrained Language Models
Shun Kiyono | Sho Takase | Shengzhe Li | Toshinori Sato
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Pretrained language models require the use of consistent segmentation (e.g., subword- or character-level segmentation) in pretraining and finetuning. In NLP, many tasks are modeled by subword-level segmentation better than by character-level segmentation. However, because of their format, several tasks require the use of character-level segmentation. Thus, in order to tackle both types of NLP tasks, language models must be independently pretrained for both subword and character-level segmentation. However, this is an inefficient and costly procedure. Instead, this paper proposes a method for training a language model with unified segmentation. This means that the trained model can be finetuned on both subword- and character-level segmentation. The principle of the method is to apply the subword regularization technique to generate a mixture of subword- and character-level segmentation. Through experiment on BERT models, we demonstrate that our method can halve the computational cost of pretraining.

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An Open-Domain Avatar Chatbot by Exploiting a Large Language Model
Takato Yamazaki | Tomoya Mizumoto | Katsumasa Yoshikawa | Masaya Ohagi | Toshiki Kawamoto | Toshinori Sato
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

With the ambition to create avatars capable of human-level casual conversation, we developed an open-domain avatar chatbot, situated in a virtual reality environment, that employs a large language model (LLM). Introducing the LLM posed several challenges for multimodal integration, such as developing techniques to align diverse outputs and avatar control, as well as addressing the issue of slow generation speed. To address these challenges, we integrated various external modules into our system. Our system is based on the award-winning model from the Dialogue System Live Competition 5. Through this work, we hope to stimulate discussions within the research community about the potential and challenges of multimodal dialogue systems enhanced with LLMs.


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Building a Personalized Dialogue System with Prompt-Tuning
Tomohito Kasahara | Daisuke Kawahara | Nguyen Tung | Shengzhe Li | Kenta Shinzato | Toshinori Sato
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop

Dialogue systems without consistent responses are not attractive. In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring consistency. Considering the trend of the rapidly increasing scale of language models, we propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models. The results of the automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses with less computational resources than fine-tuning.