Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
Table 5: Percentage of five categories for both long answer and short answer.
['[EMPTY]', 'Long Answer Case1', 'Long Answer Case2', 'Long Answer Case3', 'Long Answer Case4', 'Long Answer Case5', 'Short Answer', 'Short Answer Case1', 'Short Answer Case2', 'Short Answer Case3', 'Short Answer Case4', 'Case5']
For BERT-base+Model-III, we can see it outperforms other BERT-base models in the first four cases on the long answer and gets comparable results on Case 5. For the short answer, the improvement of our proposed model mainly comes from Case 1 and Case 4, which suggests that our approach helps the model do well in cases that have a short answer. Comparing Model- I and Model-III , we can see the significant improvement of our model lies in the long answer on Case 1 (From 38.2%, 40.0% to 41.8%, 43.0%, respectively).