The most fundamental communication mechanism for interaction is dialogues involving speech, gesture, semantic and pragmatic knowledge. Various researches on dialogue management have been conducted focusing on standardized model for goal oriented applications

Emotion intelligence plays an important role in building a successful human–machine dialogue system. However, the extreme difficulty of capturing emotional information of social text, as well as the weakness of generative models for learning emotional

In many practical applications, the machine needs to actively ask humans to obtain their intents. The process that the machine raises questions and users return answers is called reverse QA, which is an important part of a human–machine dialogue. However,

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