Contextual Text Generation

via “question-answering via text-to-text generation with context encoding”

translation model by undefined. 22,70,077 downloads.

Unique: Treats QA as text-to-text generation enabling abstractive answers; uses joint encoding of question and context through multi-head attention rather than separate question-context encoders, creating tighter question-context alignment

vs others: Simpler to deploy than BERT-based extractive QA systems; enables abstractive answers unlike span-extraction models, though with lower factuality guarantees