Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering
Table 2: Comparison with state-of-the-art models, where the first group are models without using BERT, the second group are BERT-based models, and the last group are our multi-passage BERT models.
['Datasets Models', 'Quasar-T EM', 'Quasar-T  [ITALIC] F1', 'SearchQA EM', 'SearchQA  [ITALIC] F1', 'TriviaQA EM', 'TriviaQA  [ITALIC] F1', 'OpenSQuAD EM', 'OpenSQuAD  [ITALIC] F1']
We evaluate BERT-RC and Multi-passage BERT on four standard benchmarks, where passage scores are leveraged for both models. We build another multi-passage BERT for each dataset by initializing it with the pre-trained BERT-Large model. In particular, on the OpenSQuAD dataset, our model improves by 21.4% EM and 21.5% F1 over all non-BERT models, and 5.8% EM and 6.5% Leveraging BERT-Large model makes multi-passage BERT even better on TriviaQA and OpenSQuAD datasets.