NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly

Yi Fung, Tuhin Chakrabarty, Hao Guo, Owen Rambow, Smaranda Muresan, Heng Ji

Submission Type: Regular Long Paper

Submission Track: Computational Social Science and Cultural Analytics

Submission Track 2: Human-Centered NLP

Keywords: social norms, discovery, grounding

TL;DR: Norm Discovery from Conversations On-the-Fly with language model self-verification mechanism

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, 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.

Submission Number: 2827