Team Habib Disambiguators at SemEval-2026 Task 5: Assessing Semantic Plausibility using Regularized Transformer Fine-Tuning
Year: 2026
Type: conference-paper
Abstract: This paper presents a system for SemEval-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Sentences through Narrative Understanding (Gehring et al., 2026;Gehring and Roth, 2025).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.
Authors: Zohaib Aslam, Ahsan Siddiqui, Ayesha Enayet
Institution: Habib University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166883988