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

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

19 Apr 2019IJCNLP 2019 11arXiv:1904.09077archive 2025-07-28

Shijie Wu, Mark Dredze

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. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.

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Code

shijie-wu/crosslingual-nlp officialmentioned in papermentioned on GitHubpytorchMIT report

stefan-it/fine-tuned-berts-seq mentioned on GitHub report

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Tasks

Cross-Lingual NER Cross-Lingual Transfer Dependency Parsing Document Classification NER Natural Language Inference POS POS Tagging Zero-Shot Cross-Lingual Transfer

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Cross-Lingual NER	CoNLL Dutch	mBERT	F1	77.57	#9 of 10	Archive leaderboard	report
Cross-Lingual NER	CoNLL German	mBERT	F1	69.56	#9 of 10	Archive leaderboard	report
Cross-Lingual NER	CoNLL Spanish	mBERT	F1	74.96	#8 of 10	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece mBERT

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