Read + Verify: Machine Reading Comprehension with Unanswerable Questions
Table 1: Comparison of different approaches on the SQuAD 2.0 test set, extracted on Aug 28, 2018: Levy et al. Levy171, Clark et al. Clark182, Liu et al. liu2017stochastic3, Huang et al. Huang17b4 and Wang et al. wang2018multi5. † indicates unpublished works.
['Model', 'Dev EM', 'Dev F1', 'Test EM', 'Test F1']
We use Model-III as the default answer verifier, and only report the best result. As we can see, our system obtains state-of-the-art results by achieving an EM score of 71.7 and a F1 score of 74.2 on the test set. Notice that SLQA+ has reached a comparable result compared to our approach.