Mathematical reasoning is a fundamental aspect of human intelligence and is
applicable in various fields, including science, engineering, finance, and
everyday life. The development of artificial intelligence (AI) systems capable
of solving math problems and proving theorems has garnered significant interest
in the fields of machine learning and natural language processing. For example,
mathematics serves as a testbed for aspects of reasoning that are challenging
for powerful deep learning models, driving new algorithmic and modeling
advances. On the other hand, recent advances in large-scale neural language
models have opened up new benchmarks and opportunities to use deep learning for
mathematical reasoning. In this survey paper, we review the key tasks,
datasets, and methods at the intersection of mathematical reasoning and deep
learning over the past decade. We also evaluate existing benchmarks and
methods, and discuss future research directions in this domain.