Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
Table 7: Performances of generated questions for QA.
['Data Type', 'Accuracy']
Previous experiments demonstrate that our model can deliver more precise questions. To further prove the effectiveness of our model, we will see how useful the generated questions are for training a question answering system over knowledge bases. Specifically, we combine human-labeled data with the same amount of model-generated data to a typical QA system (Mohammed et al., 2018). We can observe that adding generative questions may weaken the performance of QA Our generated questions achieve the best performance on the QA system. It indicates that our model generates more precise question and has improved QA performances greatly.