Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2023

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.

Keywords

Abundant resources, End to end, End-to-end task, Modulars

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

2023 Conference on Empirical Methods in Natural Language Processing: Singapore, December 6-10: Proceedings

First Page

5925

Last Page

5941

ISBN

9798891760608

Identifier

10.18653/v1/2023.emnlp-main.363

Publisher

Association for Computational Linguistics (ACL)

City or Country

Stroudsburg, PA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.18653/v1/2023.emnlp-main.363

Download

Share

COinS