  This paper outlines the use of Transformer networks trained to translate math
word problems to equivalent arithmetic expressions in infix, prefix, and
postfix notations. We compare results produced by many neural configurations
and find that most configurations outperform previously reported approaches on
three of four datasets with significant increases in accuracy of over 20
percentage points. The best neural approaches boost accuracy by 30% when
compared to the previous state-of-the-art on some datasets.
