@inproceedings{aslam-etal-2026-team,
title = "Team Habib Disambiguators at {S}em{E}val-2026 Task 5: Assessing Semantic Plausibility using Regularized Transformer Fine-Tuning",
author = "Aslam, Zohaib and
Siddiqui, Ahsan and
Enayet, Ayesha",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.269/",
pages = "2126--2129",
ISBN = "979-8-89176-414-9",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Team Habib Disambiguators at SemEval-2026 Task 5: Assessing Semantic Plausibility using Regularized Transformer Fine-Tuning
%A Aslam, Zohaib
%A Siddiqui, Ahsan
%A Enayet, Ayesha
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F aslam-etal-2026-team
%X 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.
%U https://aclanthology.org/2026.semeval-1.269/
%P 2126-2129
Markdown (Informal)
[Team Habib Disambiguators at SemEval-2026 Task 5: Assessing Semantic Plausibility using Regularized Transformer Fine-Tuning](https://aclanthology.org/2026.semeval-1.269/) (Aslam et al., SemEval 2026)
ACL