@inproceedings{qin-etal-2023-end,
title = "End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions",
author = "Qin, Libo and
Pan, Wenbo and
Chen, Qiguang and
Liao, Lizi and
Yu, Zhou and
Zhang, Yue and
Che, Wanxiang and
Li, Min",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.363",
doi = "10.18653/v1/2023.emnlp-main.363",
pages = "5925--5941",
abstract = "End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.",
}
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<abstract>End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.</abstract>
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%0 Conference Proceedings
%T End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
%A Qin, Libo
%A Pan, Wenbo
%A Chen, Qiguang
%A Liao, Lizi
%A Yu, Zhou
%A Zhang, Yue
%A Che, Wanxiang
%A Li, Min
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F qin-etal-2023-end
%X End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.
%R 10.18653/v1/2023.emnlp-main.363
%U https://aclanthology.org/2023.emnlp-main.363
%U https://doi.org/10.18653/v1/2023.emnlp-main.363
%P 5925-5941
Markdown (Informal)
[End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://aclanthology.org/2023.emnlp-main.363) (Qin et al., EMNLP 2023)
ACL
- Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, and Min Li. 2023. End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5925–5941, Singapore. Association for Computational Linguistics.