Process / pipeline

Question Answering (QA)

Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.

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Sources

  1. Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI: 10.18653/v1/D16-1264
  2. Yang, Z. et al. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. NeurIPS. link

Related methods

Referenced by

ScholarGateQuestion Answering (Question Answering (QA)). Retrieved 2026-06-04 from https://scholargate.app/en/text-mining/question-answering