论文标题
finqa上数字推理的数学推理的强大优化的长文本
A Robustly Optimized Long Text to Math Models for Numerical Reasoning On FinQA
论文作者
论文摘要
在解决我们生活中的大多数问题时，需要数值推理，但是在以前的人工智能研究中已被忽略。 Finqa挑战是为了加强有关数值推理的研究，要求参与者预测数值推理计划以解决财务问题。 FinQA的结果将通过执行精度和程序准确性评估。在本文中，我们介绍了通过开发具有不同专业功能并融合力量的模型来解决任务目标的方法。总体而言，我们的方法在Finqa挑战赛中获得了第一名，执行精度为71.93％，计划准确性为67.03％。
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.
