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.

中文翻译：

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多回合对话理解的进展：一项调查

训练机器以理解自然语言并与人类互动是人工智能领域中一项难以捉摸且必不可少的任务。近年来，随着深度学习研究的迅速发展，特别是最近的预训练语言模型，设计了多种对话系统。在这些研究中，基本但具有挑战性的部分是对话理解，其作用是教机器在响应之前阅读和理解对话上下文。在本文中，我们从对话建模的角度回顾了以前的方法。与普通文本阅读理解相比，我们总结了对话理解的特征和挑战。然后，我们讨论了在对话理解任务中广泛使用的三种典型对话建模模式，例如响应选择和对话问答，以及与对话相关的语言建模技术，以增强对话场景中的PrLM。最后，我们重点介绍了近年来的技术进步，并指出了我们可以从实证分析中汲取的经验教训以及在新的研究领域中的前景。