As a rising star in the field of natural language processing, question
answering systems (Q&A Systems) are widely used in all walks of life. Compared
with other scenarios, the applicationin financial scenario has strong
requirements in the traceability and interpretability of the Q&A systems. In
addition, since the demand for artificial intelligence technology has gradually
shifted from the initial computational intelligence to cognitive intelligence,
this research mainly focuses on the financial numerical reasoning dataset -
FinQA. In the shared task, the objective is to generate the reasoning program
and the final answer according to the given financial report containing text
and tables. We use the method based on DeBERTa pre-trained language model, with
additional optimization methods including multi-model fusion, training set
combination on this basis. We finally obtain an execution accuracy of 68.99 and
a program accuracy of 64.53, ranking No. 4 in the 2022 FinQA Challenge.

该文介绍了基于 DeBERTa 预训练语言模型的金融问答系统在 FinQA 挑战赛中的应用，通过多模型融合和训练集组合等优化方法，输出准确度达到 68.99，程序准确度达到 64.53，排名第四。

一种基于 DeBERTa 的新型金融问答模型

A Novel DeBERTa-based Model for Financial Question Answering Task

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.

本文提出了一种使用不同专业能力的模型进行融合的方法，以解决财务问题的数字推理任务，并在 FinQA 挑战赛中获得了第一名，执行准确率为 71.93％，程序准确率为 67.03％。