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. 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 paper reviews the recent works 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 user's 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, conforming, and task completion. The works we select to present
here is 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 dialog systems.

本文回顾了近期关于神经网络方法在开放领域对话系统中解决语义、一致性和互动性挑战的工作，以实现与用户建立长期连结并满足人类沟通、情感和社会归属需求。