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2019

A BERT Baseline for the Natural Questions

Alberti, Chris, Lee, Kenton, Collins, Michael

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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.

A BERT Baseline for the Natural Questions · Around

Built on

Adam: A method for stochastic optimization
Original
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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A decomposable attention model for natural language inference
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Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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