A feature-rich regression model using multilingual embeddings and features for frequency, cognate similarity, and predictability reports RMSE scores of 1.132, 1.037, and 0.891 for L1-aware vocabulary difficulty prediction on Spanish, German, and Chinese.
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UOL@IDEM at BEA 2026 Shared Task 1: Neural Fusion and Feature-Rich Modeling for L1-Aware Vocabulary Difficulty Prediction
A feature-rich regression model using multilingual embeddings and features for frequency, cognate similarity, and predictability reports RMSE scores of 1.132, 1.037, and 0.891 for L1-aware vocabulary difficulty prediction on Spanish, German, and Chinese.