Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
Table 4: Results on the SQuAD-document dev set.
['Model', 'EM', 'F1']
We find our approach adapts well: the best model achieves 87.2 F1. Note that the BERT\textscLARGE model has obtained 90.9 F1 on the original SQuAD dataset (single-paragraph setting), which is only 3.7% ahead of us.