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.


翻译:本技术说明描述了自然问题的新基线,我们的模型以BERT为基础,将原始数据集文件中报告的F1模型分数与长期和短期答复任务中人类上限的相对值分别缩小30%和50%。该基准已提交Ai.google.com/research/NaturalQuesestion的官方NQ领导板。代码、预处理数据和预培训模型可在https://github.com/google-research/language/tree/master/langues/ageny_ question/bert_joint查阅。

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