@inproceedings{ohashi-2024-towards,
title = "Towards Robust and Multilingual Task-Oriented Dialogue Systems",
author = "Ohashi, Atsumoto",
editor = "Inoue, Koji and
Fu, Yahui and
Axelsson, Agnes and
Ohashi, Atsumoto and
Madureira, Brielen and
Zenimoto, Yuki and
Mohapatra, Biswesh and
Stricker, Armand and
Khosla, Sopan",
booktitle = "Proceedings of the 20th Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.yrrsds-1.13/",
pages = "35--36",
abstract = "In this position paper, I present my research interests regarding the field of task-oriented dialogue systems. My work focuses on two main aspects: optimizing the task completion ability of dialogue systems using reinforcement learning, and developing language resources and exploring multilinguality to support the advancement of dialogue systems across different languages. I discuss the limitations of current approaches in achieving robust task completion performance and propose a novel optimization approach called Post-Processing Networks. Furthermore, I highlight the importance of multilingual dialogue datasets and describe our work on constructing JMultiWOZ, the first large-scale Japanese task-oriented dialogue dataset."
}
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<abstract>In this position paper, I present my research interests regarding the field of task-oriented dialogue systems. My work focuses on two main aspects: optimizing the task completion ability of dialogue systems using reinforcement learning, and developing language resources and exploring multilinguality to support the advancement of dialogue systems across different languages. I discuss the limitations of current approaches in achieving robust task completion performance and propose a novel optimization approach called Post-Processing Networks. Furthermore, I highlight the importance of multilingual dialogue datasets and describe our work on constructing JMultiWOZ, the first large-scale Japanese task-oriented dialogue dataset.</abstract>
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%0 Conference Proceedings
%T Towards Robust and Multilingual Task-Oriented Dialogue Systems
%A Ohashi, Atsumoto
%Y Inoue, Koji
%Y Fu, Yahui
%Y Axelsson, Agnes
%Y Ohashi, Atsumoto
%Y Madureira, Brielen
%Y Zenimoto, Yuki
%Y Mohapatra, Biswesh
%Y Stricker, Armand
%Y Khosla, Sopan
%S Proceedings of the 20th Workshop of Young Researchers’ Roundtable on Spoken Dialogue Systems
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F ohashi-2024-towards
%X In this position paper, I present my research interests regarding the field of task-oriented dialogue systems. My work focuses on two main aspects: optimizing the task completion ability of dialogue systems using reinforcement learning, and developing language resources and exploring multilinguality to support the advancement of dialogue systems across different languages. I discuss the limitations of current approaches in achieving robust task completion performance and propose a novel optimization approach called Post-Processing Networks. Furthermore, I highlight the importance of multilingual dialogue datasets and describe our work on constructing JMultiWOZ, the first large-scale Japanese task-oriented dialogue dataset.
%U https://aclanthology.org/2024.yrrsds-1.13/
%P 35-36
Markdown (Informal)
[Towards Robust and Multilingual Task-Oriented Dialogue Systems](https://aclanthology.org/2024.yrrsds-1.13/) (Ohashi, YRRSDS 2024)
ACL