Social Chemistry 101: Learning to Reason about Social and Moral Norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap, Yejin Choi

TL;DR
This paper introduces Social Chemistry, a large dataset and model framework for understanding and reasoning about social and moral norms expressed in natural language, with promising results in predicting social rules.
Contribution
The paper presents Social-Chem-101, a comprehensive corpus of social norms with multi-dimensional annotations and a neural model that generalizes to unseen social situations.
Findings
Neural Norm Transformer effectively predicts social norms.
The dataset contains over 4.5 million annotations.
Model generalizes to novel social scenarios.
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 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsLinear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Adam · Residual Connection · Dropout · Multi-Head Attention · Byte Pair Encoding · Softmax · Dense Connections
