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Designing systems that can reason across cultures requires that they aregrounded in the norms of the contexts in which they operate. However, currentresearch on developing computational models of social norms has primarilyfocused on American society. Here, we propose a novel approach to discover andcompare descriptive social norms across Chinese and American cultures. Wedemonstrate our approach by leveraging discussions on a Chinese Q&A platform(Zhihu) and the existing SocialChemistry dataset as proxies for contrastingcultural axes, align social situations cross-culturally, and extract socialnorms from texts using in-context learning. Embedding Chain-of-Thoughtprompting in a human-AI collaborative framework, we build a high-qualitydataset of 3,069 social norms aligned with social situations across Chinese andAmerican cultures alongside corresponding free-text explanations. To test theability of models to reason about social norms across cultures, we introducethe task of explainable social norm entailment, showing that existing modelsunder 3B parameters have significant room for improvement in both automatic andhuman evaluation. Further analysis of cross-cultural norm differences based onour dataset shows empirical alignment with the social orientations framework,revealing several situational and descriptive nuances in norms across thesecultures.

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment