Transformers Power Cross-Lingual NLP Models

Multilingual Transformers like mBERT learn shared representations across languages for zero-shot transfer.

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🤯 Did You Know (click to read)

mBERT was trained on Wikipedia text in 104 languages and can perform zero-shot cross-lingual tasks.

Self-attention layers encode text in multiple languages, capturing shared semantic structure. Fine-tuning on one language enables transfer to other languages without additional training data, supporting translation, classification, and entity recognition.

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💥 Impact (click to read)

Cross-lingual Transformers improve multilingual applications, supporting global NLP deployment in translation, search, and social media.

Developers and researchers can leverage pretrained multilingual models to implement applications in low-resource languages efficiently.

Source

Devlin et al., 2019 - Multilingual BERT

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