In this paper, we revisit the solving bias when evaluating models on current
Math Word Problem (MWP) benchmarks. However, current solvers exist solving bias
which consists of data bias and learning bias due to biased dataset and
improper training strategy. Our experiments verify MWP solvers are easy to be
biased by the biased training datasets which do not cover diverse questions for
each problem narrative of all MWPs, thus a solver can only learn shallow
heuristics rather than deep semantics for understanding problems. Besides, an
MWP can be naturally solved by multiple equivalent equations while current
datasets take only one of the equivalent equations as ground truth, forcing the
model to match the labeled ground truth and ignoring other equivalent
equations. Here, we first introduce a novel MWP dataset named UnbiasedMWP which
is constructed by varying the grounded expressions in our collected data and
annotating them with corresponding multiple new questions manually. Then, to
further mitigate learning bias, we propose a Dynamic Target Selection (DTS)
Strategy to dynamically select more suitable target expressions according to
the longest prefix match between the current model output and candidate
equivalent equations which are obtained by applying commutative law during
training. The results show that our UnbiasedMWP has significantly fewer biases
than its original data and other datasets, posing a promising benchmark for
fairly evaluating the solvers' reasoning skills rather than matching nearest
neighbors. And the solvers trained with our DTS achieve higher accuracies on
multiple MWP benchmarks. The source code is available at
this https URL.

本篇论文提出解决当前数学应用问题（Math Word Problem，MWP）数据偏见问题的方法：构建了一个去除偏见的新数据集 UnbiasedMWP，同时我们提出了一种名为 Dynamic Target Selection 的动态目标选择策略来缓解学习偏差问题，两种方法都取得了良好的实验效果并成为解决 MWP 的有趣基准。