Structural similarity between languages is crucial for enhancing a language model's multilingual generalization capabilities, providing the backbone that allows for effective cross-lingual processing.
While models like mBERT introduced significant advancements, the under-representation of Arabic and Hebrew in pre-training data limits their performance compared to monolingual models.
Monolingual models often outperform multilingual counterparts across various NLP tasks, as highlighted by the results from models like CAMeLBERT and AlephBERT.
The OSCAR dataset serves as a pivotal resource for training language models in Hebrew and Arabic, despite the challenges posed by size limitations.
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