ZYC at SemEval-2026 Task 5: Application of BERT-based Contextual Embeddings Similarity for WSD

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

Summary

ZYC's contribution to SemEval-2026 Task 5 investigates contextual embedding manipulation for Word Sense Disambiguation (WSD). The team proposes four distinct approaches, all built upon BERT-like pretrained models, to explore the effectiveness of various similarity calculations and classification methods. A key innovation involves scratch-trained cross-attention mechanisms, inspired by GLiNER, designed to compute similarity between definition or synonym representations and the full context of a word. Their best performing model achieved 57% accuracy with a Spearman correlation of 0.20. The findings indicate that the finetuning strategy and training curriculum are more critical for this novel WSD task than the initial choice of pretrained model. The researchers also identified several areas for future improvements.

Key takeaway

For NLP Engineers developing Word Sense Disambiguation systems, you should prioritize your finetuning strategy and training curriculum over selecting a specific BERT-like pretrained model. The research suggests these factors significantly influence performance, achieving 57% accuracy. Consider implementing scratch-trained cross-attention mechanisms, inspired by GLiNER, to enhance similarity calculations between definitions and context. This approach could improve your WSD model's accuracy and robustness.

Key insights

Finetuning and training curriculum are more impactful for WSD than the choice of BERT-like pretrained model.

Principles

• Contextual embedding manipulation aids WSD.
• Cross-attention can compute context-definition similarity.
• Finetuning strategy is critical for WSD performance.

Method

Four BERT-like approaches were developed, incorporating scratch-trained GLiNER-inspired cross-attention to calculate similarity between definition/synonym representations and full context for WSD.

In practice

• Explore GLiNER-inspired cross-attention.
• Prioritize finetuning over model selection.
• Investigate various similarity calculations.

Topics

• Word Sense Disambiguation
• Contextual Embeddings
• BERT-based Models
• Cross-Attention Mechanisms
• SemEval-2026
• Finetuning Strategies

Code references

• heliosraz/SemEval52026

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.