Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
Table 3. Experimental results on YahooCQA.
['Model', 'P@1', 'MRR', '# Params', 'Time']
First, we observe that HyperQA outperforms AP-BiLSTM and AP-CNN significantly. Specifically, we outperform AP-BiLSTM, the runner-up model by 6% in terms of MRR and 10% in terms of MAP. Notably, HyperQA is 32 times faster than AP-BiLSTM and has 20 times less parameters. Our approach shows that complicated attentive pooling mechanisms are not necessary for good performance.