A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
Table 1: The performance of MTMSN and other competing approaches on DROP dev and test set.
['Model', 'Dev EM', 'Dev F1', 'Test EM', 'Test F1']
MTMSN outperforms all existing approaches by a large margin, and creates new state-of-the-art results by achieving an EM score of 75.85 and a F1 score of 79.88 on the test set. Since our best model utilizes BERT\textscLARGE as encoder, we therefore compare MTMSN\textscLARGE with the NABERT\textscLARGE baseline. As we can see, our model obtains 12.07/13.19 absolute gain of EM/F1 over the baseline, demonstrating the effectiveness of our approach. However, as the human achieves 95.98 F1 on the test set, our results suggest that there is still room for improvement.