1 Introduction  
2 Neural Models in Dialogue Systems  
2.1 Convolutional Neural Networks  
2.2 Recurrent Neural Networks and Vanilla Sequence-to-sequence Models  
2.3 Hierarchical Recurrent Encoder-Decoder (HRED)  
2.4 Memory Networks  
2.5 Attention and Transformer  
2.6 Pointer Net and CopyNet  
2.7 Deep Reinforcement Learning Models and Generative Adversarial Networks  
2.8 Knowledge Graph Augmented Neural Networks  
3 Task-oriented Dialogue Systems  
3.1 Natural Language Understanding  
3.2 Dialogue State Tracking  
3.3 Policy Learning  
3.4 Natural Language Generation  
3.5 End-to-end Methods  
3.6 Research Challenges and Hot Topics  
4 Open-Domain Dialogue Systems  
4.1 Context Awareness  
4.2 Response Coherence  
4.3 Response Diversity  
4.4 Speaker Consistency and Personality-based Response  
4.5 Empathetic Response  
4.6 Controllable Generation  
4.7 Conversation Topic  
4.8 Knowledge-Grounded System  
4.9 Interactive Training  
4.10 Visual Dialogue  
5 Evaluation Approaches  
5.1 Evaluation Methods for Task-oriented Dialogue Systems  
5.2 Evaluation Methods for Open-domain Dialogue Systems  
6 Datasets  
6.1 Datasets for Task-oriented Dialogue Systems  
7 Conclusions and Trends