Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling

📰 ArXiv cs.AI

Improve lexical difficulty prediction using context-aligned contrastive learning and ridge ensembling for better language learning and readability assessment

advanced Published 12 May 2026

Action Steps

Apply contrastive learning to align contextual representations
Use ridge ensembling to combine predictions from multiple models
Train models with ordinal supervision to capture cross-lingual alignment
Evaluate models using metrics such as mean absolute error and Spearman correlation
Integrate the improved lexical difficulty prediction model into language learning platforms

Who Needs to Know This

NLP researchers and language learning platform developers can benefit from this approach to enhance their models' ability to predict word difficulty across different languages

Key Insight

💡 Context-aligned contrastive learning and ridge ensembling can improve lexical difficulty prediction by structuring the representation space and capturing cross-lingual alignment

Key Takeaways

Improve lexical difficulty prediction using context-aligned contrastive learning and ridge ensembling for better language learning and readability assessment

Full Article

Title: Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling

Abstract:
arXiv:2605.08950v1 Announce Type: cross Abstract: Lexical difficulty prediction is a fundamental problem in language learning and readability assessment, requiring models to estimate word difficulty across different first-language (L1) backgrounds. However, existing approaches rely on regression-only training with scalar supervision, which does not explicitly structure the representation space, limiting their ability to capture cross-lingual alignment and ordinal difficulty. To mitigate these is

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