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Chris Alberti; Kenton Lee; Michael Collins

Abstract
This technical note describes a new baseline for the Natural Questions. Our model is based on BERT and reduces the gap between the model F1 scores reported in the original dataset paper and the human upper bound by 30% and 50% relative for the long and short answer tasks respectively. This baseline has been submitted to the official NQ leaderboard at ai.google.com/research/NaturalQuestions. Code, preprocessed data and pretrained model are available at https://github.com/google-research/language/tree/master/language/question_answering/bert_joint.
Code Repositories
gooofy/zbrain
tf
Mentioned in GitHub
see--/natural-question-answering
pytorch
Mentioned in GitHub
google-research/language
Official
tf
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| question-answering-on-natural-questions-long | BERTjoint | F1: 64.7 |
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