NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
Yi R. Fung, Tuhin Chakrabarty, Hao Guo, Owen Rambow, Smaranda Muresan, Heng Ji
Abstract
Knowledge of norms is needed to understand and reason about acceptable behavior in human communication and interactions across sociocultural scenarios. Most computational research on norms has focused on a single culture, and manually built datasets, from non-conversational settings. We address these limitations by proposing a new framework, NORMSAGE<sup>1</sup>, to automatically extract culture-specific norms from multi-lingual conversations. NORMSAGE uses GPT-3 prompting to 1) extract candidate norms directly from conversations and 2) provide explainable self-verification to ensure correctness and relevance. Comprehensive empirical results show the promise of our approach to extract high-quality culture-aware norms from multi-lingual conversations (English and Chinese), across several quality metrics. Further, our relevance verification can be extended to assess the adherence and violation of any norm with respect to a conversation on-the-fly, along with textual explanation. NORMSAGE achieves an AUC of 94.6% in this grounding setup, with generated explanations matching human-written quality.