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This paper introduces a framework for jointly modeling annotator-specific NLI label predictions and explanations, leveraging annotator-provided rationales as fine-grained signals of individual perspectives. They condition predictions on annotator identity and demographic metadata using a "User Passport" and explore two explainer architectures: a post-hoc prompt-based explainer and a prefixed bridge explainer. Results demonstrate that incorporating explanation modeling improves predictive performance over annotator-aware classifiers, with the prefixed bridge showing better label alignment and semantic consistency.
Modeling annotator-specific explanations substantially boosts NLI prediction accuracy and provides a richer understanding of disagreement compared to simply conditioning on annotator identity.
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.
