Abstract
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 neighbor 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.
Highlights
Understanding and reasoning about social situations relies on unspoken commonsense rules about social norms, i.e., acceptable social behavior (Haidt, 2012)
We investigate how state-of-the-art neural language models can learn and generalize out of SOCIAL-CHEM-101 to accurately reason about social norms with respect to a previously unseen situation
We gather a total of 104k real life situations from four domains: scraped titles of posts in the subreddits r/confessions (32k) and r/amitheasshole (r/AITA, 30k), which largely focus on moral quandaries and interpersonal conflicts; 30k sentences from the ROCStories corpus; and scraped titles from the Dear Abby advice column archives3.4
Summary
Understanding and reasoning about social situations relies on unspoken commonsense rules about social norms, i.e., acceptable social behavior (Haidt, 2012). When faced with situations like “wanting to call the cops on my neighbors,” (Figure 1), we perform a rich variety of reasoning about about legality, cultural pressure, Social Judgment. All together, these annotations comprise SOCIALCHEM-101, a new type of NLP resource that catalogs 292k RoTs over 104k real life situations, along with 365k sets of structural annotations, which break each RoT into 12 dimensions of norm attributes. We investigate how state-of-the-art neural language models can learn and generalize out of SOCIAL-CHEM-101 to accurately reason about social norms with respect to a previously unseen situation We term this modeling framework NEURAL NORM TRANSFORMER, and find it is able to generate relevant (and potentially novel) rulesof-thumb conditioned on all attribute dimensions. SOCIAL-CHEM-101 provides a new resource to teach AI models to learn people’s norms, as well as to support novel interdisciplinary research across NLP, computational norms, and descriptive ethics
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