Numerical reasoning based on transformer

Xu Wei

doi:10.1117/12.2661770

28 December 2022 Numerical reasoning based on transformer

Xu Wei

Author Affiliations +

Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 1250620 (2022) https://doi.org/10.1117/12.2661770
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China

Abstract

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.

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Xu Wei "Numerical reasoning based on transformer", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 1250620 (28 December 2022); https://doi.org/10.1117/12.2661770

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KEYWORDS

Computer programming

Transformers

Data modeling

Neural networks

Statistical modeling

Convolution

Dielectrophoresis

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