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.

社会规范对人际交往产生根本性的影响。本研究介绍了NormDial，一个高质量的双向对话数据集，其中具有中美文化中社会规范的每个交互回合的注释。通过引入社会规范遵守检测任务，我们使用人机协同流程使用少量由专家注释的社会规范提示大型语言模型在中英文中合成生成了数据集。我们通过人工评估表明我们生成的对话质量很高，并进一步评估现有大型语言模型在此任务上的性能。研究结果指向了跨语言和文化对话背景中了解社会规范微妙之处的新方向。

NormDial: 用于建模社会规范遵守与违规的可比较双语合成对话数据集