2019

Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT

Wu, Shijie, Dredze, Mark

Understand

Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks.

A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task.
This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing.
We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task.

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