  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.
