**75.5**

To evaluate the ability of our model on predicate identification, we sample 100 generated questions from each model, and then two annotators are employed to judge whether the generated question expresses the given predicate. The Kappa for inter-annotator statistics is 0.611, and p-value for all scores is less than 0.005. As shown in Table \[tab: pred\_identification\], we can see that our model has a significant improvement in the predicate identification.

Performances on Answer Coverage — The Effectiveness of Answer-Aware Loss {#subsec:Answer Coverage}
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[p[2.6cm]{}&lt;p[1.0cm]{}&lt;p[1.2cm]{}&lt;p[1.3cm]{}&lt;]{} Model & $\lambda$ & BLEU4 & ${\rm Ans_{cov}}$ & 0 & 36.56 & 59.49 Our Model& 0 & 41.09 & 61.65 Our Model$_{\rm ans\_loss}$ & 0.05 & 41.55 & 62.27 Our Model$_{\rm ans\_loss}$ & 0.2 & **41.72** & 64.23 Our Model$_{\rm ans\_loss}$ & 0.5 & 41.57 & **65.50** Our Model$_{\rm ans\_loss}$ & 1.0 & 41.34 & 65.25

Table \[tab: answer-aware loss\] reports performances on BLUE4 and answer coverage (Ans$_{\rm cov}$). We can obtain that: (1) When answer-aware loss is not leveraged ($\lambda=0$), advantages of performance are obvious in our model. Note that the answer coverage is 55.23 on the human-labeled questions. Although our model does not explicitly capture answer information, it still obtains a high 