  Dialogue systems are a popular natural language processing (NLP) task as it
is promising in real-life applications. It is also a complicated task since
many NLP tasks deserving study are involved. As a result, a multitude of novel
works on this task are carried out, and most of them are deep learning based
due to the outstanding performance. In this survey, we mainly focus on the deep
learning based dialogue systems. We comprehensively review state-of-the-art
research outcomes in dialogue systems and analyze them from two angles: model
type and system type. Specifically, from the angle of model type, we discuss
the principles, characteristics, and applications of different models that are
widely used in dialogue systems. This will help researchers acquaint these
models and see how they are applied in state-of-the-art frameworks, which is
rather helpful when designing a new dialogue system. From the angle of system
type, we discuss task-oriented and open-domain dialogue systems as two streams
of research, providing insight into the hot topics related. Furthermore, we
comprehensively review the evaluation methods and datasets for dialogue systems
to pave the way for future research. Finally, some possible research trends are
identified based on the recent research outcomes. To the best of our knowledge,
this survey is the most comprehensive and up-to-date one at present for deep
learning based dialogue systems, extensively covering the popular techniques.
We speculate that this work is a good starting point for academics who are new
to the dialogue systems or those who want to quickly grasp up-to-date
techniques in this area.
