Social norms -- the unspoken commonsense rules about acceptable social behavior -- are crucial in understanding the underlying causes and intents of people's actions in narratives. For example, underlying an action such as "wanting to call cops on my neighbors" are social norms that inform our conduct, such as "It is expected that you report crimes." We present Social Chemistry, a new conceptual formalism to study people's everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language. We introduce Social-Chem-101, a large-scale corpus that catalogs 292k rules-of-thumb such as "it is rude to run a blender at 5am" as the basic conceptual units. Each rule-of-thumb is further broken down with 12 different dimensions of people's judgments, including social judgments of good and bad, moral foundations, expected cultural pressure, and assumed legality, which together amount to over 4.5 million annotations of categorical labels and free-text descriptions. Comprehensive empirical results based on state-of-the-art neural models demonstrate that computational modeling of social norms is a promising research direction. Our model framework, Neural Norm Transformer, learns and generalizes Social-Chem-101 to successfully reason about previously unseen situations, generating relevant (and potentially novel) attribute-aware social rules-of-thumb.
翻译:社会规范 -- -- 有关可接受的社会行为的不言而喻的常识规则 -- -- 对理解人们行动的基本原因和意图至关重要。例如,“希望召警察来对付我的邻居”等社会规范是指导我们行为的社会规范,例如“预期你会举报犯罪”。我们介绍社会化学,这是研究人们日常社会规范和道德判断的一种新的概念化形式主义,它涉及以自然语言描述的丰富多彩的现实生活情况。我们引入了社会-Chem-101,这是一个大型的集合,将292k规则汇编成书状,例如“在5点钟运行一个混合器是粗鲁的”作为基本概念单位。每个规则都随着人们判断的12个不同方面而进一步破碎,包括善恶的社会判断、道德基础、预期的文化压力和假定合法性,这加起来相当于450多万条直线标签和自由文字描述的描述。基于州-艺术模型的全面经验结果显示,社会规范的计算模型是一个有希望的新式的社会规范的模型和新式结构。我们之前的模型和新社会原理的模型,可以成功地形成一个有潜力的模型。