Attentive Pooling Networks
Table 4: Performance of different systems for TREC-QA
['[BOLD] System', '[BOLD] MAP', '[BOLD] MRR']
The results are in terms of mean average precision (MAP) and mean reciprocal rank (MRR), which are the metric normally used in previous work with the same dataset. We use the official trec_eval scorer to compute MAP and MRR. AP-biLSTM outperforms the QA-biLSTM, but its performance is not as good as the of AP-CNN. Their best result consists of a combination of LSTM and the BM25 algorithm. AP-CNN outperforms the state-of-the-art systems in both metrics, MAP and MRR.