contrastive-learning
Filtering by topic contrastive-learning(1)Clear all filters
- PaperarXiv — Language & NLP (cs.CL)9 May 2026
Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling
Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tsamarah Rana Nugraha, Ahmad Cahyono Adi et al.
A new model named Context-Aligned Contrastive Regression improves lexical difficulty prediction by integrating contrastive learning objectives with a Ridge regression ensemble. Experiments on three L1 datasets demonstrate enhanced cross-lingual representation alignment, better capture of ordinal difficulty structure, and more stable performance across difficulty levels.
Original 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 issues, we propose Context-Aligned Contrastive Regression, which integrates Ridge regression ensemble with two complementary objectives, i.e., Cross-View Context and Ordinal Soft Contrastive Learning. Experiments on three L1 datasets show that (i) contrastive objectives improve cross-lingual representation alignment while preserving language-specific nuances, (ii) the learned representations capture the ordinal structure of lexical difficulty, and (iii) the ensemble effectively mitigates systematic biases of individual models, leading to more stable performance across difficulty levels.