论文标题
对话管理的调查：最新进展和挑战
A Survey on Dialog Management: Recent Advances and Challenges
论文作者
论文摘要
对话框管理（DM）是面向任务的对话系统中的关键组件。鉴于对话框历史记录，DM预测对话框状态并决定对话代理应采取的下一个动作。最近，对话策略学习已被广泛提出为加强学习（RL）问题，更多的工作着重于DM的适用性。在本文中，我们调查了DM的三个关键主题中的最新进展和挑战：（1）提高模型可伸缩性以促进对话系统在新场景中的建模，（2）处理对话策略学习的数据稀缺问题，（3）提高培训效率以实现更好的任务完成效果。我们认为，这项调查可以阐明对话管理中未来的研究。
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.
