🤯 Did You Know (click to read)
BERT can identify the exact span of text that answers a user’s question in paragraphs from large documents.
BERT uses bidirectional context and transformer self-attention to locate relevant spans in a document that answer a given question. Fine-tuning on datasets like SQuAD enables the model to understand question intent and passage context, producing extractive answers efficiently. This capability supports search engines, virtual assistants, and educational tools by providing accurate and context-aware answers from large text sources.
💥 Impact (click to read)
Document-based question answering accelerates information retrieval, helping users obtain answers without manually reading entire documents. It enhances productivity in research, education, and customer support.
For users, BERT provides concise answers from text passages. The irony is that answer extraction is statistical rather than cognitive, yet appears contextually intelligent.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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