统一数据透视主义与个性化：在社交规范中的应用
Unifying Data Perspectivism and Personalization: An Application to Social Norms
AI总结：
本研究针对社交规范感知预测，将个性化方法应用于13k名标注者和210k条判断的语料库，比较不同方法的有效性，并分析在冲突双方关系亲密度不同的社交情境子集上的表现。
摘要：
近期的研究不再在语言处理任务中使用单一真值，而是探讨如何表示和预测标注者集合的标签。然而，通常对标注者知之甚少，或标注者集合很小。在本研究中，我们考察了一个关于冲突的社交媒体帖子语料库，包含13k名标注者和210k条社交规范判断。我们提供了一种新颖的实验设置，将个性化方法应用于标注者建模，并比较它们在预测社交规范感知方面的有效性。我们进一步分析了在按冲突双方关系亲密度划分的社交情境子集上的表现，评估个性化在何处帮助最大。
英文摘要：
Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms. We provide a novel experimental setup that applies personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms. We further provide an analysis of performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict, and assess where personalization helps the most.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
