Context-Aligned Contrastive Regression combines cross-view context alignment and ordinal soft contrastive learning with ridge ensembles to improve lexical difficulty prediction across L1 backgrounds on three datasets.
S em E val-2021 Task 1: Lexical Complexity Prediction
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Fine-tuned LLM and explainable models predict vocabulary difficulty with correlations r > 0.91 and r > 0.77, showing spelling difficulty and test item construction as key influences in addition to word production difficulty.
Gradient-boosted models with SHAP analysis find word familiarity as the dominant predictor of English vocabulary difficulty across Spanish, German, and Chinese L1 learners, with orthographic transfer adding value only for the first two groups.
citing papers explorer
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Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling
Context-Aligned Contrastive Regression combines cross-view context alignment and ordinal soft contrastive learning with ridge ensembles to improve lexical difficulty prediction across L1 backgrounds on three datasets.
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Sakura at BEA 2026 Shared Task 1: What Makes Vocabulary Difficult?
Fine-tuned LLM and explainable models predict vocabulary difficulty with correlations r > 0.91 and r > 0.77, showing spelling difficulty and test item construction as key influences in addition to word production difficulty.
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What makes a word hard to learn? Modeling L1 influence on English vocabulary difficulty
Gradient-boosted models with SHAP analysis find word familiarity as the dominant predictor of English vocabulary difficulty across Spanish, German, and Chinese L1 learners, with orthographic transfer adding value only for the first two groups.