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). The accuracy of QA is shown in Table \[tab: Question Answering\]. We can observe that adding generative questions may weaken the performance of QA (drop from 68.97 to 68.53 in Table \[tab: Question Answering\]). 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.

### Speed {#subsec:Speed}

![Performance on valid data through epochs, where “base" is the method in Elsahar et al. (2018).[]{data-label="fig:epoch"}](epoch.pdf){width="6.6cm"}

In order to further explore the convergence speed, we plot the performances on valid data through epochs in Figure \[fig:epoch\]. Our model has much more information to learn, and it may have a bad impact on the convergence speed. Nevertheless, our model can copy KB elements and textual context simultaneously, which may accelerate the convergence speed. As demonstrated in Figure \[fig:epoch\], our model achieves 