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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7 Conclusion
- How quickly did neural models start closing the SQuAD gap?Within about four months, Wang and Jiang’s Match-LSTM with an answer pointer reached 70.3% F1, more than halving the gap between the 51.0% logistic regression baseline and 86.8% human performance.
- Rajpurkar et al., 2016, SQuAD (Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)), §7, p. 2391
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/