Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
Table 6: Performances of whether using the pre-trained KB embedding by transE.
['Model', 'TransE', 'BLEU4', 'ROUGE [ITALIC] L', 'METEOR']
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 In comparison, \newciteN18-1020 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.