Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension
Table 3: Test set accuracy on MedHop dataset. The results marked with ⋆ are reported in Welbl et al. (2017).
['[EMPTY]', 'Test (Masked)', 'Test']
To eliminate this bias and to test our system’s ability to conduct multi-hop reasoning using the context, we additionally evaluate our system on the masked version of MedHop, where every candidate expression is replaced randomly using 100 unique placeholder tokens so that models can only rely on the context to comprehend every candidate. Our model achieves 41.6% accuracy in this “masked” setting, outperforming all previously published works by a large margin.