  Constructing accurate and automatic solvers of math word problems has proven
to be quite challenging. Prior attempts using machine learning have been
trained on corpora specific to math word problems to produce arithmetic
expressions in infix notation before answer computation. We find that
custom-built neural networks have struggled to generalize well. This paper
outlines the use of Transformer networks trained to translate math word
problems to equivalent arithmetic expressions in infix, prefix, and postfix
notations. In addition to training directly on domain-specific corpora, we use
an approach that pre-trains on a general text corpus to provide foundational
language abilities to explore if it improves performance. We compare results
produced by a large number of 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 almost 10% on average when
compared to the previous state of the art.
