D3CODE：在冒犯性检测与评估中拆解跨文化数据里的标注分歧
D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation
AI总结：
针对NLP冒犯性检测标注主观性研究忽略跨文化及个体道德价值差异的问题，本文提出含4500余条句子、8个文化区域4000余名标注者平行标注的D3CODE数据集，揭示区域认知差异，助力构建文化敏感的多元NLP模型。
摘要：
尽管人工标注在语言技术中发挥着关键作用，但标注者的主观性在数据收集中长期被忽视。近期批判性审视这一问题的研究多以西方语境为背景，且仅记录了年龄、性别或种族群体间的差异。因此，关于主观性的自然语言处理（NLP）研究忽略了一个事实：人口统计群体内的个体可能持有多元价值观，这些价值观对其认知的影响可能超出群体规范。为将这些考量有效纳入NLP流程，我们需要包含来自不同社会文化群体的大量平行标注的数据集。本文提出D3CODE数据集：这是一个大规模跨文化冒犯性语言平行标注数据集，包含逾4500条句子，由来自21个国家、代表8个地缘文化区域的4000余名标注者完成标注，标注者在性别和年龄上分布均衡。该数据集还包含沿六个道德基础（关怀、公平、 proportionality、权威、忠诚、纯洁）采集的标注者道德价值观信息。我们的分析揭示，受个体道德价值观影响，标注者的认知存在显著的区域差异，这为构建多元化、具备文化敏感性的NLP模型提供了关键洞见。
英文摘要：
While human annotations play a crucial role in language technologies, annotator subjectivity has long been overlooked in data collection. Recent studies that have critically examined this issue are often situated in the Western context, and solely document differences across age, gender, or racial groups. As a result, NLP research on subjectivity have overlooked the fact that individuals within demographic groups may hold diverse values, which can influence their perceptions beyond their group norms. To effectively incorporate these considerations into NLP pipelines, we need datasets with extensive parallel annotations from various social and cultural groups. In this paper we introduce the \dataset dataset: a large-scale cross-cultural dataset of parallel annotations for offensive language in over 4.5K sentences annotated by a pool of over 4k annotators, balanced across gender and age, from across 21 countries, representing eight geo-cultural regions. The dataset contains annotators' moral values captured along six moral foundations: care, equality, proportionality, authority, loyalty, and purity. Our analyses reveal substantial regional variations in annotators' perceptions that are shaped by individual moral values, offering crucial insights for building pluralistic, culturally sensitive NLP models.
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