Numerical reasoning based on transformer

Numerical reasoning is a complex subtask in machine reading comprehension, which requires the model to complete discrete operations such as statistics and calculation in the process of reasoning answers. As far as we know, the best solution for numerical reasoning adopts GNN structure. However, the graph structure is often generated by hand-designed rules and it will cost extra computation resource. To tackle these problems, we propose a Transformer-based numerical reasoning solution which transforms the hand-designed graph structure into learnable parameters. Extensive experimental results show that compared to those GNN-based methods, our method can carry out numerical reasoning more efficiently only at the cost of a little decrease on answer quality.