CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
Table 1: Accuracy on SimpleQuestions testing set. ∗ indicates using ensembles. N-Gram+ uses additional heuristics. The proposed CFO (focused pruning + BiGRU + type vector) achieves the top accuracy.
['[BOLD] Pruning Method', '[BOLD] Relation Network', '[BOLD] Entity Representation Random', '[BOLD] Entity Representation Pretrain', '[BOLD] Entity Representation Type Vec']
Trained on 75,910 questions, our proposed model and baseline methods are evaluated on the testing set with 21,687 questions. CFO (focused pruning + BiGRU + type vector) achieves the best performance, outperforming all other methods by substantial margins. This evidence suggests the superiority of RNN in capturing semantics of question utterances. Surprisingly, it turns out that Embed-AVG achieves better performance than the more complex LTG-CNN. It suggests that under sparse supervision, training high-quality distributed knowledge representations remains a challenging problem. That said, pretraining entity embeddings with TransE indeed gives better performance compared to random initialization, indicating the future potential of unsupervised methods in improving continuous knowledge representation.