Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
Table 2. Experimental results on WikiQA.
['Model', 'MAP', 'MRR', '#Params', 'Time']
4.4.1. Firstly, we observe that HyperQA outperforms a myriad of complex neural architectures. Notably, we obtain a clear performance gain of 2%−3% in terms of MAP/MRR against models such as AP-CNN or AP-BiLSTM. Our model also outperforms MP-CNN which is severely equipped with parameterized word matching mechanisms. We achieve competitive results relative to the Rank MP-CNN. Finally, HyperQA is extremely efficient and fast, clocking 2s per epoch compared to 33s per epoch for Rank MP-CNN. The parameter cost is also 90K vs 10 million which is a significant improvement.