Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
Table 5: Experimental results on the QatarLiving dataset. Best result is in boldface and second best is underlined. CTRN outperforms the complex AI-CNN model.
['[BOLD] Model', '[BOLD] P@1', '[BOLD] MAP']
The performance of the CTRN model also outperforms the baseline QRNN by 3% on P@1. Similar to the Yahoo QA dataset , we also found that the baseline QRNN performed surprisingly well, i.e., outperforming ConvKN and other CNN based models such as ARC- I and ARC-II. Overall, our proposed approach achieves very competitive results on this dataset.