the best performances on almost epochs. After about 6 epochs, performances on our model become stable and convergent.

### Case Study {#subsec:case_study}

![Examples of questions by different models.[]{data-label="fig:case"}](case.pdf){width="7cm"}

Figure \[fig:case\] lists referenced question and generated questions by different models. It can be seen that our generated questions can better express the target predicate such as ID 1 (marked as ). In ID 2, although all questions express the target predicate correctly, only our question refers to a definitive answer since it contains an answer type word “city" (marked as **bold**). It should be emphasized that the questions, generated by our method with answer-aware loss, do not always contain answer type words (ID 1 and 3).

[|p[2.0cm]{}&lt;|p[2.0cm]{}&lt;|p[2.0cm]{}&lt;|]{} Eval Problem & Label Problem & Error Predicate\
57 & 16 & 14\

Related Work
============

Our work is inspired by a large number of successful applications using neural encoder-decoder frameworks on NLP tasks such as machine translation [@conf/ssst/ChoMBB14] and dialog generation [@journals/corr/VinyalsL15]. Our work is also inspired by the recent work for KBQG based on encoder-decoder frameworks. first proposed a neural network for mapping KB facts into natural language questions. To improve the generalization, introduced extra contexts for the input fact, which achieved 