Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales

TL;DR

This paper introduces a framework for modeling annotator-specific perspectives and explanations in natural language inference, improving prediction accuracy and explanation fidelity by incorporating individual rationales and demographic data.

Contribution

It proposes novel architectures for jointly modeling label prediction and explanations conditioned on annotator identity, advancing fine-grained perspectivist modeling.

Findings

Incorporating explanations improves predictive performance.

Prefixed bridge approach achieves higher semantic consistency.

Post-hoc explainer yields stronger lexical similarity.

Abstract

Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework for jointly modeling annotator-specific label prediction and corresponding explanations, fine-tuned on the annotators' provided rationales. Using a dataset with disaggregated natural language inference (NLI) annotations and annotator-provided explanations, we condition predictions on both annotator identity and demographic metadata through a representation-level User Passport mechanism. We further introduce two explainer architectures: a post-hoc prompt-based explainer and a prefixed bridge explainer that transfers annotator-conditioned classifier representations directly into a generative model. This design enables explanation generation aligned with individual annotator perspectives. Our results show that incorporating explanation modeling substantially improves predictive performance over a baseline annotator-aware classifier, with the prefixed bridge approach achieving more stable label alignment and higher semantic consistency, while the post-hoc approach yields stronger lexical similarity. These findings indicate that modeling explanations as expressions of fine-grained perspective provides a richer and more faithful representation of disagreement. The proposed approaches advance perspectivist modeling by integrating annotator-specific rationales into both predictive and generative components.