  Training machines to understand natural language and interact with humans is
an elusive and essential task in the field of artificial intelligence. In
recent years, a diversity of dialogue systems has been designed with the rapid
development of deep learning researches, especially the recent pre-trained
language models. Among these studies, the fundamental yet challenging part 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 perspective of dialogue modeling. 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 that are widely-used in dialogue comprehension tasks such as
response selection and conversation question-answering, as well as
dialogue-related language modeling techniques to enhance PrLMs in dialogue
scenarios. Finally, we highlight the technical advances in recent years and
point out the lessons we can learn from the empirical analysis and the
prospects towards a new frontier of researches.
