End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li
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.- Anthology ID:
- 2023.emnlp-main.363
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5925–5941
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.363
- DOI:
- 10.18653/v1/2023.emnlp-main.363
- Cite (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.
- Cite (Informal):
- End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (Qin et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.emnlp-main.363.pdf