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SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar et al., 2016, SQuAD (Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP))

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2 cards in “2 Existing Datasets” · whole deck

2 Existing Datasets

  1. How do SQuAD questions differ from cloze-style queries (CNN/Daily Mail, CBT)?
    Cloze answers are single words or entities merely suggested by the passage, and the data are generated automatically. SQuAD answers are often non-entity, longer phrases entailed by the passage, written by humans as questions.
    • Rajpurkar et al., 2016, SQuAD (Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)), §2, pp. 2384–2385
  2. How does SQuAD differ from open-domain QA answer extraction?
    Answer extraction usually exploits redundancy, since an answer appears in many documents; in SQuAD a system has only a single reading passage.
    • Rajpurkar et al., 2016, SQuAD (Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)), §2, pp. 2384–2385

Source

  • Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev and Percy Liang. 2016. SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2383–2392. License: CC BY 4.0. Flashcards adapted from this work. doi:10.18653/v1/D16-1264 https://aclanthology.org/D16-1264/