Bidirectional Transformers for Language Understanding
Understand how BERT revolutionized NLP using bidirectional self-attention and innovative pretraining tasks to capture deep contextual language meaning.
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In the last lesson, we saw how transformers let every word attend to every other, but they still struggled to capture context from both directions. Early advances tried to fix this: ELMo (2018) used LSTMs for context-aware embeddings, while GPT applied unidirectional transformers for fluent text generation.
The real breakthrough came with BERT (Bidirectional Encoder Representations from Transformers) in 2018. Unlike models that read only left-to-right or right-to-left, BERT processes text in both directions at once. For example, in the sentence “The bat flew out of the cave,” BERT considers both “flew” and “cave” to decide that “bat” means an animal, not a baseball bat. This bidirectional view allows it to grasp nuanced meaning with remarkable accuracy, making BERT one of the first true large language models.
What is BERT?
So, what exactly is BERT? At its core, BERT is an architecture built on the same transformer encoder we’ve discussed—but with one crucial twist: bidirectional. Like some early language models, traditional models would process text in one direction (imagine reading a sentence word by word from left to right). BERT, however, takes in the entire sentence at once, considering both the words that come before and after any given word. This approach allows it to capture subtleties and context that unidirectional models might miss.
In simple terms, BERT is like a super attentive reader. When it sees ...