Improving Question Answering over Incomplete KBswith Knowledge-Aware Reader
Table 1: Comparisons with Key-Value Memory Networks and GRAFT-Nets under different KB settings.
['[BOLD] Model', '[BOLD] 10% KB Hit@1', '[BOLD] 10% KB F1', '[BOLD] 30% KB Hit@1', '[BOLD] 30% KB F1', '[BOLD] 50% KB Hit@1', '[BOLD] 50% KB F1', '[BOLD] 100% KB Hit@1', '[BOLD] 100% KB F1']
For reference, we also show the results under full KB settings (i.e., 100%, all of the required evidence is covered by KB). The row of SGReader shows the results of our model using only KB evidence. Compared to the previous KBQA methods (KV-KB and GN-KB), SGReader achieves better results in incomplete KB settings and competitive performance with the full KB. Here we do not compare with existing methods that utilize semantic parsing annotations Yih et al. It is worth noting that SGReader only needs one hop of graph propagation while the compared methods typically require multiple hops.