  Training machines to understand natural language and interact with humans is
an elusive and essential task of artificial intelligence. A diversity of
dialogue systems has been designed with the rapid development of deep learning
techniques, especially the recent pre-trained language models (PrLMs). Among
these studies, the fundamental yet challenging type of task is dialogue
comprehension whose role is to teach the machines to read and comprehend the
dialogue context before responding. In this paper, we review the previous
methods from the technical perspective of dialogue modeling for the dialogue
comprehension task. We summarize the characteristics and challenges of dialogue
comprehension in contrast to plain-text reading comprehension. Then, we discuss
three typical patterns of dialogue modeling. In addition, we categorize
dialogue-related pre-training techniques which are employed to enhance PrLMs in
dialogue scenarios. Finally, we highlight the technical advances in recent
years and point out the lessons from the empirical analysis and the prospects
towards a new frontier of researches.
