answer coverage, which may be because our diversified contexts contain rich answer type words. (2) To demonstrate the effectiveness of answer-aware loss, we set the weight of answer-aware loss ($\lambda$) to 0.05/0.2/0.5/1.0 (the last four lines in Table \[tab: answer-aware loss\]). It can be seen that our model, incorporating answer-aware loss, has a significant improvement on answer coverage while there is no performance degradation on BLEU4 compared with $\lambda=0$, which indicates that answer-aware loss contributes to generating better questions. Especially, the generated questions are more precise because they refer to more definitive answers with high Ans$_{\rm cov}$.

\(3) It tends to correspond to alternative answers (object in the triplet fact) for some predicates such as `fb:location/containedby`, while other predicates (e.g. `fb:person/gender`) may refer to a definitive answer. To investigate our model, by incorporating answer-aware loss, does not generate an answer type word in a mandatory way, we found 20.5% predicate corresponds to the generated questions without answer type words when our model obtains the highest Ans$_{\rm cov}$ ($\lambda$=0.5), and it is very close to 21.7% for the one in human-annotated questions. This demonstrates that the answer-aware loss does not force all predicates to generate questions with answer type words.

The 