SOCIAL SCAFFOLDS: A Generalization Framework for Social Understanding Tasks

ACL ARR 2025 May Submission6697 Authors

Abstract: Effective human communication in social settings is contingent on recognizing subtle cues, such as intents or implications. Without such cues, NLP models risk missing social signals, instead relying on surface patterns. We introduce SOCIAL SCAFFOLDS, an automated framework for facilitating generalization across social reasoning tasks by generating rationales that make these social cues explicit. Grounded in narrative modeling principles, we generate task-agnostic rationales that capture different perspectives, i.e. that of the speaker, the listener, and the general world-view. Our experimental suite showcases that providing rationales as augmentations aids task performance for both supervised fine-tuning and in-context learning paradigms. Notably, providing all three rationale types significantly improves cross-task performance in 44\% of cases, and inferred speaker intent in 31.3\% of cases. We conduct statistical and ablation analyses that show how rationales complement the input text and are used effectively by models.

Paper Type: Long

Research Area: Discourse and Pragmatics

Research Area Keywords: Dialogue Understanding, Rationales, Generalizability

Contribution Types: Approaches to low-resource settings, Data analysis

Languages Studied: English

Submission Number: 6697