End-to-end task-oriented dialogue: A survey of tasks, methods, and future directions

• Main Authors: QIN, Libo, PAN, Wenbo, CHEN, Qiguang, LIAO, Lizi, YU, Zhou, ZHANG, Yue, CHE, Wanxiang, LI, Min
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Abundant resources; End to end; End-to-end task; Modulars; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8582; https://ink.library.smu.edu.sg/context/sis_research/article/9585/viewcontent/End_to_end_Task_oriented_Dialogue_A_Survey_of_Tasks__Methods__and_Future_Directions.pdf
• Institution: Singapore Management University

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.