Attentive Pooling Networks
Table 5: Performance of different systems for WikiQA
['[BOLD] System', '[BOLD] MAP', '[BOLD] MRR']
Like in the other two datasets, AP-CNN outperforms QA-CNN, and AP-biLSTM outperforms the QA-biLSTM. The difference of performance between AP-CNN and QA-CNN is smaller than the one for the InsuranceQA dataset. We believe it is because the average size of the answers in WikiQA (25) is much smaller than in InsuranceQA (95). It is expected that attentive pooling bring more impact to the datasets that have larger answer/question lengths. Yang et al. ’s result that use word embeddings AP-CNN outperforms these two systems in both metrics.