BLEU4 & ROUGE$\rm _{L}$ & METEOR & True & 36.56 & 58.09 & 34.41 & False & 33.67 & 55.57 & 33.20 Our Model$_{\rm ans\_loss}$ & True & 41.72 & 69.31 & 48.13 Our Model$_{\rm ans\_loss}$ & False & 41.55 & 68.59 & 47.52

Pre-trained KB embeddings may provide rich structured relational information among entities. However, it heavily relies on large-scale triplets, which is time and resource-intensive. To investigate the effectiveness of pre-trained KB embedding for KBQG, we report the performance of KBQG whether using pre-trained KB embeddings by simply applying TransE. Table \[tab: pre-train\] shows that the performance of KBQG is degraded without TransE embeddings. In comparison, obtain obvious degradation on all metrics while there is only a slight decline in our model. We believe that it may owe to the context-augmented fact encoder since our model drops to 40.87 on the BLEU4 score without context-augmented fact encoder and transE embeddings.

### The Effectiveness of Generated Questions for Enhancing Question Answering over Knowledge Bases {#sec:Question Answering}

[p[4.1cm]{}&lt;p[1.5cm]{}&lt;]{} Data Type & Accuracy human-labeled data & 68.97

+ gen\_data (Serban et al., 2016) & 68.53 + gen\_data (Elsahar et al., 2018) & 69.13 + gen\_data (Our Model$_{\rm ans\_loss}$) & **69.57**

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