Effectiveness of Generated Questions for Enhancing QA over KBs {#subsec:Question Answering}
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[p[4.1cm]{}&lt;p[1.5cm]{}&lt;]{} Data Type & Accuracy human-labeled data & 68.97

+ gen\_data [@P16-1056] & 68.53 + gen\_data [@N18-1020] & 69.13 + gen\_data (Our Model$_{\rm ans\_loss}$) & **69.57**

Previous experiments demonstrate that our model is able to 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 (15180) of model-generated data to a typical QA system [@N18-2047]. 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 QA system. It indicates that our model generates more precise question and has improved QA performances greatly.

Ablation Study {#subsec:Ablation Study}
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[p[3.0cm]{}&lt;p[1.15cm]{}&lt;p[1.15cm]{}&lt;p[1.2cm]{}&lt;]{} Model $\quad \ \ $ & BLEU4 & ROUGE$\rm _{L}$ & METEOR Our Model$_{\rm ans\_loss}$ & **41.72** & **69.31** & **48.13** w/o context copy & 41.27 & 68.36 & 47.54 w/o KB copy & 41.04 & 68.66 & 47.72 