 We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
<fluency>  We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.
 We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models.