pre-trained by TransE [@NIPS2013_5071]. The word embeddings are initialized by the pre-trained Glove word vectors[^4] with 200 dimensions. In the transformer, we set the hidden units $d$ to 200, and we employ 4 paralleled attention head and a stack of 5 identical layers. We set the weight ($\lambda$) of the answer-aware loss to 0.2.

Overall Comparisons {#subsec:Overall Comparisons}
-------------------

[p[2.6cm]{}&lt;p[1.2cm]{}&lt;p[1.3cm]{}&lt;p[1.3cm]{}&lt;]{} Model $\quad \ \ $ & BLEU4 & ROUGE$\rm _{L}$ & METEOR Template $\quad \ \ $ & 31.36 & \* & 33.12 & 33.32 & \* & 35.38 & 36.56 & 58.09 & 34.41 Our Model $\quad \ \ \,$ & 41.09 & 68.68 & 47.75 $\ \ \,$ Our Model$_{\rm ans\_loss}$ & **41.72** & **69.31** & **48.13**

In Table \[tab: overall performance\], we compare our model with the typical baselines on word-overlap based metrics. It is evident that our model is remarkably better than baselines on all metrics, where the BLEU4 score increases 4.53 compared with the strongest baseline [@N18-1020]. Especially, incorporating answer-aware loss (the last line in Table \[tab: overall performance\]) further improves the performance (+5.16 BLEU4).

Performances on Predicate Identification {#subsec:Predicate Identification}
----------------------------------------

[p[2.8cm]{}&lt;p[2.5cm]{}&lt;]{} Model & Pred. Identification & 53.5 & 71.5 Our Model$_{\rm ans\_loss}$ & 