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BERT Bidirectional Transformer Encoder Pretraining for Language Representation

A pre-training method for language representation in which a multi-layer bidirectional Transformer encoder is trained on large unlabeled text using masked-language-modeling (predicting randomly masked tokens from both left and right context) and next-sentence prediction. The resulting contextual embeddings are then fine-tuned with a task-specific head for downstream NLP tasks such as classification.

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Transformer Models and BERT: Encoder-Decoder Architecture and Bidirectional Language Modeling

The Transformer architecture operates on the principle that context-dependent word representations require bidirectional contextual awareness to resolve semantic ambiguity in natural language process…