A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
Table 3: Ablation tests of different architecture choices using MTMSN\textscLARGE.
['Model', 'EM', 'F1']
S4SS3SSS0Px2 Architecture ablation First, we investigate the effects of some “global vectors” used in our model. Specifically, we find that removing the question and passage vectors from all involved computation leads to 1.3 % drop on F1. Ablating the representation of [CLS] token leads to even worse results. We also try to use the last hidden representation (denoted as M3) to calculate question and passage vectors, but find that does not work. Next, we remove the gating mechanism used during span prediction, and observe a nearly 0.8% decline on both metrics. Finally, we share parameters between the arithmetic expression component and the negation component, and find the performance drops by 1.1% on F1.