Recent conditional language models are able to continue any kind of text source in an often seemingly fluent way. This fact encouraged research in the area of open-domain conversational systems that are based on powerful language models and aim to imitate an interlocutor by generating appropriate contributions to a written dialogue. From a linguistic perspective, however, the complexity of contributing to a conversation is high. In this survey, we interpret Grice's maxims of cooperative conversation from the perspective of this specific research area and systematize the literature under the aspect of what makes a contribution appropriate: A neural conversation model has to be fluent, informative, consistent, coherent, and follow social norms. In order to ensure these qualities, recent approaches try to tame the underlying language models at various intervention points, such as data, training regime or decoding. Sorted by these categories and intervention points, we discuss promising attempts and suggest novel ways for future research.

翻译：近期条件语言模型能够以看似流畅的方式续写任意文本源。这一事实推动了基于强大语言模型的开放域对话系统研究，该类系统旨在通过生成契合书面对话的合适回应来模仿对话者。然而从语言学视角看，参与对话的复杂性极高。本综述从该特定研究领域角度解读格莱斯合作对话原则，并围绕"何为恰切回应"这一维度系统化梳理相关文献：神经对话模型需兼具流畅性、信息性、一致性、连贯性，并遵循社会规范。为确保这些特质，近期研究尝试在数据、训练范式或解码等不同干预点对底层语言模型进行驯化。按照这些类别与干预点，我们探讨了具有前景的研究尝试，并为未来研究提出新方向。