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.

本文提出了使用 Transformer 网络将数学问题转化为等价的前缀、中缀和后缀表达式，并使用预训练的通用文本语料库进行训练以提高性能，与以往最先进的方法相比，最好的神经网络方法可将准确率平均提高近 10%。