  With the recent advance in large pre-trained language models, researchers
have achieved record performances in NLP tasks that mostly focus on language
pattern matching. The community is experiencing the shift of the challenge from
how to model language to the imitation of complex reasoning abilities like
human beings. In this work, we investigate the application domain of finance
that involves real-world, complex numerical reasoning. We propose a new
large-scale dataset, ConvFinQA, aiming to study the chain of numerical
reasoning in conversational question answering. Our dataset poses great
challenge in modeling long-range, complex numerical reasoning paths in
real-world conversations. We conduct comprehensive experiments and analyses
with both the neural symbolic methods and the prompting-based methods, to
provide insights into the reasoning mechanisms of these two divisions. We
believe our new dataset should serve as a valuable resource to push forward the
exploration of real-world, complex reasoning tasks as the next research focus.
Our dataset and code is publicly available at
https://github.com/czyssrs/ConvFinQA.
