We treat object type words as the answer type words because the object is the answer. $H_{a_n,y_t}$ denotes the cross entropy between the answer type word $a_n$ and the generated question word $y_t$. Finally, the minimum cross entropy is regarded as the answer-aware loss $\mathcal{L}_{ans\_loss}$. Optimizing $\mathcal{L}_{ans\_loss}$ means that the model aims at generating an answer type word in the generated question sequence. For example, the model tends to generate Q3 rather than Q2 in Figure \[fig:example\], because Q3 contains an answer type word—“city". Similarly, $\mathcal{L}_{ans\_loss}$ could be optimized in an end-to-end manner, and it can integrate $\mathcal{L}_{ques\_loss}$ by a weight coefficient $\lambda$ to the total loss as follows: $$\begin{split}
\mathcal{L}_{total\_loss}=\mathcal{L}_{ques\_loss} + \lambda \mathcal{L}_{ans\_loss}
\end{split}
\label{eq:toal_loss}$$

Experiment {#Setion: Experiment}
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Experimental Settings {#subsec:exp_setting}
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### Experimental Data Details

We conduct experiments on the SimpleQuestion dataset [@DBLP:journals/corr/BordesUCW15], and there are 75910/10845/21687 question answering pairs (QA-pairs) for training/validation/test. In order to obtain **diversified contexts**, we additionally employ domain, range and topic of the predicate to improve the coverage of predicate contexts. In this way, 100% predicates (rather than 44%^\[predicate\ context\ percentage\]^ of those in @N18-1020) have contexts. For the subject and object context, we combine the most frequently mentioned entity type [@N18-1020] 