2022/06/29 by Renhui Zhang, Zhang, Renhui, Youwei Zhang +3 · 1 citation
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Mathematics, Computing, and Information Processing #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2207.06490
openalex publication_date 2022/06/29 · openalex created_date 2022/07/16 · openalex updated_date 2026/07/28
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.