BERT model has been successfully applied to open-domain QA tasks. However,
previous work trains BERT by viewing passages corresponding to the same
question as independent training instances, which may cause incomparable scores
for answers from different passages. To tackle this issue, we propose a
multi-passage BERT model to globally normalize answer scores across all
passages of the same question, and this change enables our QA model find better
answers by utilizing more passages. In addition, we find that splitting
articles into passages with the length of 100 words by sliding window improves
performance by 4%. By leveraging a passage ranker to select high-quality
passages, multi-passage BERT gains additional 2%. Experiments on four standard
benchmarks showed that our multi-passage BERT outperforms all state-of-the-art
models on all benchmarks. In particular, on the OpenSQuAD dataset, our model
gains 21.4% EM and 21.5% $F_1$ over all non-BERT models, and 5.8% EM and 6.5%
$F_1$ over BERT-based models.

本文提出了一种多样化的 BERT 模型，通过使用多个片段并全局规范化答案分数，并得到统一的分数，性能提高了 4％。通过使用段落评分器选择高质量的段落，多通道 BERT 获得了额外的 2％。实验结果表明，我们的方案在所有基准测试上均优于现有模型，特别是在 OpenSQuAD 数据集上，我们的模型比所有非 BERT 模型和基于 BERT 的模型都表现出色。