Natural Questions: A Benchmark for Question Answering Research

@article{Kwiatkowski2019NaturalQA,
  title={Natural Questions: A Benchmark for Question Answering Research},
  author={T. Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur P. Parikh and Chris Alberti and D. Epstein and Illia Polosukhin and J. Devlin and Kenton Lee and Kristina Toutanova and Llion Jones and Matthew Kelcey and Ming-Wei Chang and Andrew M. Dai and Jakob Uszkoreit and Quoc V. Le and Slav Petrov},
  journal={Transactions of the Association for Computational Linguistics},
  year={2019},
  volume={7},
  pages={453-466}
}
We present the Natural Questions corpus, a question answering data set. [...] Key Method We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.Expand
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References

SHOWING 1-10 OF 34 REFERENCES
QuAC : Question Answering in Context
WikiQA: A Challenge Dataset for Open-Domain Question Answering
CoQA: A Conversational Question Answering Challenge
SQuAD: 100, 000+ Questions for Machine Comprehension of Text
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Know What You Don't Know: Unanswerable Questions for SQuAD
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Reading Wikipedia to Answer Open-Domain Questions
...
1
2
3
4
...