A Robustly Optimized Long Text to Math Models for Numerical Reasoning On FinQA
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Abstract (arXiv)
Numerical reasoning is required when solving most problems in our life, but it has been neglected in previous artificial intelligence researches. FinQA challenge has been organized to strengthen the study on numerical reasoning where the participants are asked to predict the numerical reasoning program to solve financial question. The result of FinQA will be evaluated by both execution accuracy and program accuracy. In this paper, we present our approach to tackle the task objective by developing models with different specialized capabilities and fusing their strength. Overall, our approach achieves the 1st place in FinQA challenge, with 71.93% execution accuracy and 67.03% program accuracy.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 29 Jun 2022.
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8 of 13 references.
References · 8
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- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
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- 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 903
- 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 562
- 2018The Web as a Knowledge-base for Answering Complex Questionscited by 410
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.
2 papers.