Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
Table 4. Experimental results on SemEvalCQA.
['Model', 'P@1', 'MAP', '#Params', 'Time']
Our proposed approach achieves highly competitive performance on this dataset. Specifically, we have obtained the best P@1 performance overall, outperforming the state-of-the-art AI-CNN model by 3% in terms of P@1. The performance of our model on MAP is marginally short from the best performing model. Notably, AI-CNN has benefited from external handcrafted features. As such, comparing AI-CNN (w/o features) with HyperQA shows that our proposed model is a superior neural ranking model. Next, we draw the readers attention to the time cost of AI-CNN. The training time per epoch is ≈3250s per epoch which is about 300 times longer than our model. AI-CNN is extremely cost prohibitive, i.e., attentive pooling is already very expensive and yet AI-CNN performs 3D attentive pooling. Evidently, its performance can be easily superseded in a much smaller training time and parameter cost. This raises questions about the effectiveness of the 3D attentive pooling mechanism.