contexts simultaneously. For the purpose of further making generated questions correspond to definitive answers, we propose the **answer-aware loss** by optimizing the cross-entropy between the generated question and answer type words, which is beneficial to generate precise questions. We conduct experiments on an open public dataset. Experimental results demonstrate that the proposed model using diversified textual contexts outperforms strong baselines (+4.5 BLEU4 score). Besides, it can further increase the BLEU score (+5.16 BLEU4 score) and produce questions associated with more definitive answers by incorporating answer-aware loss. Human evaluations complement that our model can express the given predicate more precisely. In brief, our main contributions are as follows:

\(1) We leverage diversified contexts and multi-level copy mechanism to alleviate the issue of incorrect predicate expression in traditional methods.

\(2) We propose an answer-aware loss to tackle the issue that conventional methods can not generate questions with definitive answers.

\(3) Experiments demonstrate that our model achieves state-of-the-art performance. Meanwhile, such generated question can express the given predicate and refer to a definitive answer.

![image](overall_structure.pdf){width="446pt"}

Task Description
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We leverage textual contexts concerned with the triplet fact to generate questions over KBs. The task of KBQG can be formalized as follows:$$\begin{split}
P(Y|F)=\prod \nolimits_{t=1}^{|Y|}P(y_{t}|y_{<t},F, 