Research Area:  Machine Learning

Abstract:

Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities. In this paper, we survey recent advances and challenges in task-oriented dialog systems. We also discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning to achieve better task-completion performance, and (3) integrating domain ontology knowledge into the dialog model. Besides, we review the recent progresses in dialog evaluation and some widely-used corpora. We believe that this survey, though incomplete, can shed a light on future research in task-oriented dialog systems.

Keywords:  

Author(s) Name:  Zheng Zhang, Ryuichi Takanobu, Qi Zhu, MinLie Huang & XiaoYan Zhu

Publisher name:  Springer

DOI:  10.1007/s11431-020-1692-3

Volume Information:  volume 63, pages: 2011–2027 (2020)

Paper Link:   https://link.springer.com/article/10.1007/s11431-020-1692-3