Commonsense Evidence Generation and Injection in Reading Comprehension
Table 5: Accuracy (%) of different models on Cosmos development set
['Model', 'Acc', '[EMPTY]']
To evaluate the contributions of individual components of our proposed framework, we use an ablation study. In the evidence row, we keep using the capsule network but with or without knowledge injection. In the capsule row, we keep the textual and factual evidences and test on using and not using a capsule network. From the result, we can see that injecting generated explainable evidence can help the model achieve a better performance in terms of accuracy. Using generated textual evidence and factual evidence together can benefit more. Using capsule network significantly improves the reasoning performance, we doubt that is due to the hierarchical structure information from both token-level and phrase-level are extracted by Capsule network.