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Social norms fundamentally shape interpersonal communication. We presentNormDial, a high-quality dyadic dialogue dataset with turn-by-turn annotationsof social norm adherences and violations for Chinese and American cultures.Introducing the task of social norm observance detection, our dataset issynthetically generated in both Chinese and English using a human-in-the-looppipeline by prompting large language models with a small collection ofexpert-annotated social norms. We show that our generated dialogues are of highquality through human evaluation and further evaluate the performance ofexisting large language models on this task. Our findings point towards newdirections for understanding the nuances of social norms as they manifest inconversational contexts that span across languages and cultures.

NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation