What makes multilingual BERT multilingual?
Abstract
An experimental study comparing multilingual BERT to static non-contextualized embeddings reveals that datashize and context window size are key factors for cross-lingual transferability.
Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth experimental study to supplement the existing literature of cross-lingual ability. We compare the cross-lingual ability of non-contextualized and contextualized representation model with the same data. We found that datasize and context window size are crucial factors to the transferability.
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