Bidirectional Transformers for Language Understanding
Explore the role of bidirectional transformers in language understanding by studying BERT, its architecture, and how it processes text for nuanced context. Understand BERT’s pretraining tasks and applications like question answering and sentiment analysis to see why it transformed natural language processing.
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In the last lesson, we learned how transformers enable every word to attend to every other, but they still struggle 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? ...