BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
Abstract
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. The pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Key Findings
- 1Introduced bidirectional pre-training for language understanding
- 2Achieved state-of-the-art results on 11 NLP benchmarks simultaneously
- 3Demonstrated the effectiveness of masked language modeling pre-training
- 4Showed that fine-tuning a pre-trained model beats task-specific architectures
- 5Popularized the pre-train then fine-tune paradigm in NLP
Impact & Significance
BERT revolutionized NLP by establishing the pre-training and fine-tuning paradigm. It became the backbone of Google Search and influenced virtually every NLP system built after 2018. BERT models power search, classification, and understanding tasks globally.
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