  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.
