A Survey on Dialog Management: Recent Advances and Challenges
It provides a comprehensive overview for researchers and practitioners in dialog systems, but it is incremental as it synthesizes existing work rather than introducing new methods.
This paper surveys recent advances and challenges in dialog management, focusing on improving scalability for new scenarios, addressing data scarcity in policy learning, and enhancing training efficiency for better task-completion performance.
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.