This paper presents a system for SemEval-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Sentences through Narrative Understanding. The task involves predicting the plausibility of a specific word sense within a short story where context provided by the ending resolves a deliberate ambiguity. We model this as a regression problem, fine-tuning a DeBERTa-v3 transformer to predict the distribution of human judgments rather than a single hard label. To address the challenge of limited training data and potential overfitting, we employ R-Drop (Consistency Regularization) to enforce prediction stability across dropout masks and Layer-wise Learning Rate Decay (LLRD) to preserve the model’s pre-trained linguistic knowledge. Our experiments demonstrate that treating plausibility as a soft-label distribution, combined with aggressive regularization, improves generalization on ambiguous samples. The submitted system achieves a Spearman correlation of 0.56 and an Accuracy (within SD) of 0.74 on the official test set.