CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
Table 3: System performance with different subject network structures.
['[BOLD] Relation Network', '[BOLD] Subject Network Embed-AVG', '[BOLD] Subject Network BiGRU']
Although it is a natural choice, it remains unclear whether the final performance is sensitive to this design. Motivated by this question, we substitute the BiGRU with an Embed-AVG model, and evalute the system performance. For this experiment, we always use focused pruning and type vector, but vary the structure of the relation scoring network to allow high-order interaction across models. Insepcting the table horizontally, when BiGRU is employed as the subject network, the accuracy is consistently higher regardless of relation network structures. However, the margin is quite narrow, especially compared to the effect of varying the relation network structure the same way. We suspect this difference reflects the fact that modeling p(s|r,q) is intrinsically more challenging than modeling p(r|q). It also suggests that learning smooth entity representations with good discriminative power remains an open problem.