Improving Financial QA via Enhancing Argument Understanding with an Argument Predictor

Jinsu Lim, Yechan Hwang, Young-Jun Lee, Ho-Jin Choi

Abstract: This paper studies enhancing mathematical reasoning in Question Answering by improving the comprehension of arguments. In mathematical QA based on real-world documents, generating a reasoning program with correct arguments is challenging due to the many noisy inputs, unlike in general mathematical QA. In this study, we explore the potential for improving the performance of mathematical QA by enhancing the understanding and extraction of proper arguments to the Question. To this end, we propose an argument predictor to enhance the model's ability to comprehend and extract proper arguments for generating solution programs. Experiments conducted on the FinQA dataset demonstrate the improvement in the model's ability to identify relevant arguments, particularly in scenarios demanding multiple reasoning. The result shows improved program accuracy and execution accuracy, confirming the efficacy of our argument predictor.