  Long-form numerical reasoning in financial analysis aims to generate a
reasoning program to calculate the correct answer for a given question.
Previous work followed a retriever-generator framework, where the retriever
selects key facts from a long-form document, and the generator generates a
reasoning program based on retrieved facts. However, they treated all facts
equally without considering the different contributions of facts with and
without numbers. Meanwhile, the program consistency were ignored under
supervised training, resulting in lower training accuracy and diversity. To
solve these problems, we proposed APOLLO to improve the long-form numerical
reasoning framework. For the retriever, we adopt a number-aware negative
sampling strategy to enable the retriever to be more discriminative on key
numerical facts. For the generator, we design consistency-based reinforcement
learning and target program augmentation strategy based on the consistency of
program execution results. Experimental results on the FinQA and ConvFinQA
leaderboard verify the effectiveness of our proposed method, achieving the new
state-of-the-art.
