🤯 Did You Know (click to read)
BERT set state-of-the-art performance on SQuAD v1.1 by accurately identifying answer spans from context.
BERT’s bidirectional transformers allow it to understand context across entire passages, identifying relevant spans for answering questions. Fine-tuning on datasets like SQuAD enables precise extractive question answering, outperforming previous NLP models. The self-attention mechanism helps it consider distant words, ensuring correct interpretation even in complex sentences.
💥 Impact (click to read)
Machine reading comprehension enhances search engines, chatbots, and AI assistants, providing users with accurate information retrieval from large text documents.
For users, BERT seems to understand text deeply. The irony is that it statistically predicts answer spans rather than truly comprehending the material.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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