Social norms fundamentally shape interpersonal communication. We present NormDial, a high-quality dyadic dialogue dataset with turn-by-turn annotations of social norm adherences and violations for Chinese and American cultures. Introducing the task of social norm observance detection, our dataset is synthetically generated in both Chinese and English using a human-in-the-loop pipeline by prompting large language models with a small collection of expert-annotated social norms. We show that our generated dialogues are of high quality through human evaluation and further evaluate the performance of existing large language models on this task. Our findings point towards new directions for understanding the nuances of social norms as they manifest in conversational contexts that span across languages and cultures.

翻译：社会规范从根本上塑造了人际沟通。我们提出了NormDial——一个高质量的双人对话数据集，其中包含针对中美文化的社会规范遵守与违反行为逐轮标注。引入社会规范遵守检测任务后，本数据集通过人机协同流程，借助少量专家标注的社会规范提示大语言模型，以中文和英语两种语言合成生成。通过人工评估，我们证明所生成的对话具有高质量，并进一步评估了现有大语言模型在此任务上的表现。我们的研究发现为理解社会规范在跨语言、跨文化对话语境中呈现的细微差异指明了新的方向。