There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI [33]. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This article reviews the recent work on neural approaches that are devoted to addressing three challenges in developing such systems: semantics , consistency , and interactiveness . Semantics requires a dialog system to not only understand the content of the dialog but also identify users’ emotional and social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users’ trust and gain their long-term confidence. Interactiveness refers to the system’s ability to generate interpersonal responses to achieve particular social goals such as entertainment and conforming. The studies we select to present in this survey are based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent open-domain dialog systems.

中文翻译：

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构建智能开放域对话系统的挑战

由于大量对话数据的可用性以及对话人工智能神经方法的最新进展，人们对开发智能开放域对话系统的兴趣重新燃起 [33]。与传统的面向任务的机器人不同，开放域对话系统旨在通过满足人类对交流、情感和社会归属感的需求，与用户建立长期联系。本文回顾了最近关于神经方法的工作，这些工作致力于解决开发此类系统的三个挑战：语义,一致性， 和交互性.语义需要一个对话系统，不仅要理解对话的内容，还要识别用户在对话过程中的情感和社交需求。一致性要求系统表现出一致的个性，以赢得用户的信任并获得他们的长期信任。交互性指系统产生人际反应以实现特定社会目标（如娱乐和顺从）的能力。我们选择在本次调查中展示的研究基于我们的独特观点，绝不是完整的。尽管如此，我们希望讨论将激发新的研究，以开发更智能的开放域对话系统。