Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Table 4: Performance comparison in terms of joint EM and F1 scores under different reasoning types.
['[EMPTY]', 'Bridge (5918 samples) joint EM', 'Bridge (5918 samples) joint  [ITALIC] F1', 'Comparison (1487 samples) joint EM', 'Comparison (1487 samples) joint  [ITALIC] F1']
Our proposed SAE system delivers better performance under both reasoning types, and improvement over the DFGN model on the “bridge” type of reasoning is bigger than that of the ”comparison” type. This proves our SAE system is better at dealing with bridging type of reasoning. Another interesting finding is that for all models the performance under “comparison” type is higher than “bridge” type. For our model, we conjecture that this is due to the bridge entity may be far from the question entities in context, thus is hard to find it. However, attributes of entities are usually close and are less possible to be ignored for “comparison” type of reasoning.