Towards information-rich, logical dialogue systems with knowledge-enhanced neural models

Dialogue systems have made massive promising progress contributed by deep learning techniques and have been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic responses because they cannot ground dialogue context with background knowledge. In order to solve this problem, many researchers begin to consider combining external knowledge in dialogue systems, namely knowledge-enhanced dialogue systems. The challenges of knowledge-enhanced dialogue systems include how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate knowledge into responses generation process. Combined with external knowledge, dialogue systems can deeply understand the dialogue context, and generate more informative and logical responses. This survey gives a comprehensive review of knowledge-enhanced dialogue systems, summarizes research progress to solve these challenges and proposes some open issues and research directions.