A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
Table 2: Ablation tests of base and large models on the DROP dev set.
['Model', 'BASE EM', 'BASE F1', 'LARGE EM', 'LARGE F1']
S4SS3SSS0Px1 Component ablation To analyze the effect of the proposed components, we conduct ablation studies on the development set. Predicting count numbers is also an important component that contributes nearly 5% gain on both metrics. Moreover, enhancing the model with the negation type significantly increases the F1 by roughly 9 percent on both models. In brief, the above results show that multi-type answer prediction is vitally important for handling different forms of answers, especially in cases where discrete reasoning abilities are required.