  Designing systems that can reason across cultures requires that they are
grounded in the norms of the contexts in which they operate. However, current
research on developing computational models of social norms has primarily
focused on American society. Here, we propose a novel approach to discover and
compare descriptive social norms across Chinese and American cultures. We
demonstrate our approach by leveraging discussions on a Chinese Q&A platform
(Zhihu) and the existing SocialChemistry dataset as proxies for contrasting
cultural axes, align social situations cross-culturally, and extract social
norms from texts using in-context learning. Embedding Chain-of-Thought
prompting in a human-AI collaborative framework, we build a high-quality
dataset of 3,069 social norms aligned with social situations across Chinese and
American cultures alongside corresponding free-text explanations. To test the
ability of models to reason about social norms across cultures, we introduce
the task of explainable social norm entailment, showing that existing models
under 3B parameters have significant room for improvement in both automatic and
human evaluation. Further analysis of cross-cultural norm differences based on
our dataset shows empirical alignment with the social orientations framework,
revealing several situational and descriptive nuances in norms across these
cultures.
