Variational Question-Answer Pair Generation for Machine Reading Comprehension
Table 3: Results for answer extraction on the test set. For all the metrics, higher is better.
['[EMPTY]', 'Relevance Precision', 'Relevance Precision', 'Relevance Recall', 'Relevance Recall', 'Diversity Dist']
Our model with the condition {\rm C=5} performed the best in terms of the recall scores, while surpassing NER in terms of diversity. From the viewpoint of diversity, {\rm C=20} is the best setting. However, high Dist scores do not occur together with high recall scores. This observation shows the trade-off between diversity and quality. In this task, we show that our model with {\rm C=5} can cover most of the human-created answers and also extract more diverse answers than baselines. However, when {\rm C=0}, the Dist score is fairly low. This implies the posterior collapse issue, though the precision scores are the best.