Rating Plausibility of Word Senses in Ambiguous Sentences Using Multi-Architecture Analysis

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, medium

Summary

Word sense disambiguation in narrative contexts requires systems to reason about subtle semantic relationships. This paper addresses SemEval 2026 Task 5, reformulating Word Sense Disambiguation (WSD) as a graded plausibility prediction problem on a 1–5 Likert scale using the AmbiStory dataset. The authors present two approaches: a DeBERTa-v3-Large encoder with attention-weighted pooling and ordinal regression, achieving a Spearman correlation of 0.718, and a rank-based ensemble combining FLAN-T5 and RoBERTa, achieving 0.692. Ablation studies show that explicitly modeling ordinal structure improves performance over standard regression by 17.3%. The analysis indicates fine-tuned encoders capture fine-grained semantic relationships, while ensemble methods provide robustness through complementary modeling biases, offering an empirical analysis of design choices for graded plausibility prediction.

Key takeaway

For NLP Engineers developing word sense disambiguation systems, explicitly incorporating ordinal regression into your models is crucial. This approach, demonstrated to improve performance by 17.3% over standard regression, enhances the accuracy of graded plausibility predictions on a 1-5 Likert scale. Consider fine-tuning models like DeBERTa-v3-Large for nuanced semantic capture or employing rank-based ensembles with models like FLAN-T5 and RoBERTa for increased robustness in narrative understanding tasks.

Key insights

Explicitly modeling ordinal structure significantly improves word sense plausibility prediction.

Principles

• Ordinal structure improves WSD performance.
• Ensemble methods provide modeling robustness.
• Fine-tuned encoders capture semantic nuances.

Method

Two approaches: a DeBERTa-v3-Large encoder with attention-weighted pooling and ordinal regression, or a rank-based ensemble of FLAN-T5 and RoBERTa.

In practice

• Use ordinal regression for WSD tasks.
• Combine diverse models for robustness.
• Fine-tune encoders for semantic tasks.

Topics

• Word Sense Disambiguation
• Graded Plausibility Prediction
• Ordinal Regression
• DeBERTa-v3-Large
• FLAN-T5
• Semantic Evaluation

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.