用大模型构建中文文化规范数据集，提升对话理解的跨文化准确性
LLM-Human Pipeline for Cultural Context Grounding of Conversations
- 基于大模型生成11万条中文文化规范与违规描述，覆盖2.3万段对话
- 通过人机协同框架提炼出结构化‘规范概念’，并用符号标注到对话中
- 该数据集显著提升情绪、情感和对话行为识别的性能，适合跨文化NLP研究
对话常遵循不同文化的社交规范。例如，西方人常直呼父母姓名，而多数亚洲文化中则少见。遵守或违反这些规范往往决定对话基调。人类能自如应对需要文化敏感性的场景，但自然语言模型难以做到。本文提出一种“文化语境架构”，包含对话信息（如情绪、对话行为）和文化信息（如社会规范、违规行为）。我们利用大模型生成约11万条来自中国文化的情境规范与违规描述，覆盖约2.3万段对话，并通过自动化验证策略进行筛选，其结果经由具备文化意识的人类判断评估。通过人机交互框架将这些描述组织为有意义的“规范概念”，并以符号标注方式嵌入对话。最终，该数据集用于下游任务如情绪、情感及对话行为检测，实证表明显著提升模型表现。
原文摘要 · Abstract (English)
Conversations often adhere to well-understood social norms that vary across cultures. For example, while "addressing parents by name" is commonplace in the West, it is rare in most Asian cultures. Adherence or violation of such norms often dictates the tenor of conversations. Humans are able to navigate social situations requiring cultural awareness quite adeptly. However, it is a hard task for NLP models. In this paper, we tackle this problem by introducing a "Cultural Context Schema" for conversations. It comprises (1) conversational information such as emotions, dialogue acts, etc., and (2) cultural information such as social norms, violations, etc. We generate ~110k social norm and violation descriptions for ~23k conversations from Chinese culture using LLMs. We refine them using automated verification strategies which are evaluated against culturally aware human judgements. We organize these descriptions into meaningful structures we call "Norm Concepts", using an interactive human-in-loop framework. We ground the norm concepts and the descriptions in conversations using symbolic annotation. Finally, we use the obtained dataset for downstream tasks such as emotion, sentiment, and dialogue act detection. We show that it significantly improves the empirical performance.
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