Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

This research introduces a perspectivist framework for modeling social meaning in language, addressing the common NLP limitation of collapsing diverse human interpretations into a single ground truth. The study utilizes the P1SCO dataset, comprising 28k human annotations from 543 demographically diverse participants across Reddit, YouTube, and Instagram, to capture how interpretations vary across demographic groups. Researchers benchmarked zero-shot, few-shot, and fine-tuned approaches, proposing demographic-conditioned fusion embeddings that integrate textual and demographic representations. These fusion models consistently achieved statistically significant improvements over text-only baselines, yielding +\$5.9$–\$6.5$% relative macro PR-AUC gains. Shuffle ablations confirmed that demographic profiles provide genuine predictive signals, with the largest improvements observed for semantically ambiguous dimensions like Power (+\$51.9$% relative) and Trust (+\$30.1$% relative).

Key takeaway

For NLP engineers developing systems that interpret social meaning, especially in high-stakes applications, you should integrate explicit demographic conditioning into your models. This approach, which yields +\$5.9$–\$6.5$% relative macro PR-AUC gains, allows your system to predict how different demographic groups perceive social dimensions, moving beyond single ground-truth labels. However, treat model outputs as soft, distributional signals over groups, not individual predictions, to mitigate stereotyping risks.

Key insights

Social meaning is perspectival; models can predict how interpretations vary across demographic groups using fusion embeddings.

Principles

• Annotator disagreement in social meaning tasks is signal, not noise.
• Soft annotation targets improve model discrimination and probability calibration.
• Demographic conditioning is most valuable for semantically ambiguous dimensions.

Method

Encode gender, age, and nationality into 64-dim vectors, then integrate with RoBERTa-large text representations via additive, early, or concat-then-encode fusion for perspectivist prediction.

In practice

• Train with soft annotation fractions to improve probability estimates.
• Condition models with demographic profiles for context-aware social meaning.
• Consider additive fusion for sparse demographic data due to its inductive bias.

Topics

• Perspectivist NLP
• Social Meaning
• Demographic Conditioning
• Fusion Embeddings
• RoBERTa-large
• P1SCO Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.