Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
Table 4: Experimental results on YahooQA. Models are ranked by P@1. Models marked with ϕ are reported directly from [Tay et al.2017] while † denotes our own implementation. Best result is in boldface and second best is underlined.
['[BOLD] Model', '[BOLD] P@1', '[BOLD] MRR']
Firstly, we observe that our proposed CTRN achieves state-of-the-art performance on this dataset. CTRN also outperforms attention based models such as AP-BiLSTM and AP-CNN At this junction, we make several observations about our proposed CTRN model. Firstly, this shows that our LTC mechanism is more effective than soft-attention matching on this dataset. Secondly, the merits of this mechanism can be further observed by the performance difference in the QRNN and CTRN. Our proposed CTRN comfortably outperforms QRNN by 2%−3% in terms of P@1 and MRR.