A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
Table 5: Performance breakdown of NABERT\textscLARGE and MTMSN\textscLARGE by predicted answer types.
['Type', 'NABERT (%)', 'NABERT EM', 'NABERT F1', 'MTMSN (%)', 'MTMSN EM', 'MTMSN F1']
As shown in the Table, the main improvements are due to the addition/subtraction and negation types. We conjecture that there are two reasons for these improvements. First, our proposed expression reranking mechanism helps validate candidate expressions. Second, a new inductive bias that enables the model to perform logical negation has been introduced. The impressive performance on the negation type confirms our judgement, and suggests that the model is able to find most of negation operations. In addition, we also observe promising gains brought by the span and count types. We think the gains are mainly due to the multi-span extraction method as well as architecture designs.