  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.
