Computational approach to politeness is the task of automatically predicting and/or generating politeness in text. This is a pivotal task for conversational analysis, given the ubiquity and challenges of politeness in interactions. The computational approach to politeness has witnessed great interest from the conversational analysis community. This article is a compilation of past works in computational politeness in natural language processing. We view four milestones in the research so far, viz. supervised and weakly supervised feature extraction to identify and induce politeness in a given text, incorporation of context beyond the target text, study of politeness across different social factors, and study the relationship between politeness and various socio-linguistic cues. In this article, we describe the datasets, approaches, trends, and issues in computational politeness research. We also discuss representative performance values and provide pointers to future works, as given in the prior works. In terms of resources to understand the state of the art, this survey presents several valuable illustrations—most prominently, a table summarizing the past papers along different dimensions, such as the types of features, annotation techniques, and datasets used.

礼貌的计算方法是自动预测和/或生成文本中的礼貌的任务。考虑到交互中礼貌的普遍性和挑战，这是会话分析的一项关键任务。礼貌的计算方法引起了会话分析界的极大兴趣。本文是自然语言处理中计算礼貌方面过去著作的汇编。迄今为止，我们认为该研究有四个里程碑，即。监督和弱监督特征提取，以识别和诱导给定文本中的礼貌，纳入目标文本之外的上下文，跨不同社会因素的礼貌研究，以及研究礼貌与各种社会语言线索之间的关系。在本文中，我们描述了计算礼貌研究中的数据集、方法、趋势和问题。我们还讨论了代表性的性能值，并为未来的工作提供了指导，如先前的工作所示。在了解最新技术水平的资源方面，本次调查提供了几个有价值的插图，最突出的是一个表格，从不同维度总结了过去的论文，例如特征类型、注释技术和使用的数据集。