A Survey on Dialog Management: Recent Advances and Challenges

2020/05/05 by Yinpei Dai, Dai, Yinpei, Huihua Yu +9
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multi-Agent Systems and Negotiation #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2005.02233

openalex publication_date 2020/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely formulated as a Reinforcement Learning (RL) problem, and more works focus on the applicability of DM. In this paper, we survey recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance . We believe that this survey can shed a light on future research in dialog management.

Citations

• A User Simulator for Task-Completion Dialogues
• A Simple Language Model for Task-Oriented Dialogue
• Transferable Dialogue Systems and User Simulators

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