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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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5.2 Logistic Regression

  1. What features did the SQuAD logistic regression model use?
    Matching word and bigram TF-IDF sums (left of, right of and inside the span, and whole sentence), dependency root matches, lengths, span word frequencies, constituent label and span POS tags (optionally with the wh-word), lexicalized question–sentence lemma pairs, and dependency tree paths: about 180 million features.
    • Rajpurkar et al., 2016, SQuAD (Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)), §5.2, pp. 2388–2389

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/