Recent advances and challenges in task-oriented dialog systems

By Zheng Zhang, Ryuichi Takanobu, Minlie Huang, Xiaoyan Zhu. 2020

204 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

Dynamic Dialogue Policy for Continual Reinforcement Learning (2022, methodology): “Similar to existing work (Shu et al., 2019; Zhang et al., 2020a) we separately predict domains, intents and slots for action prediction.”
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022, methodology): “Task-oriented dialogue (ToD) systems are of great importance in advanced AI applications (Zhang et al., 2020b; Dai et al., 2020, 2021; He et al., 2022a,b,c).”
Pre-Trained Language Models for Text Generation: A Survey (2022, background): “, natural language understanding, dialogue state tracking, dialogue policy learning and natural language generation [220].”