Automated math word problem solvers based on neural networks have
successfully managed to obtain 70-80\% accuracy in solving arithmetic word
problems. However, it has been shown that these solvers may rely on superficial
patterns to obtain their equations. In order to determine what information math
word problem solvers use to generate solutions, we remove parts of the input
and measure the model's performance on the perturbed dataset. Our results show
that the model is not sensitive to the removal of many words from the input and
can still manage to find a correct answer when given a nonsense question. This
indicates that automatic solvers do not follow the semantic logic of math word
problems, and may be overfitting to the presence of specific words.

基于神经网络的自动数学问题求解器在解决算术问题方面成功地达到了 70-80％的准确率，然而研究表明这些求解器可能依赖表面模式得到方程。为了确定数学问题求解器使用哪些信息生成解答，我们移除输入的一部分并测量模型对扰动数据集的表现。结果表明，当给出无意义问题时，即使从输入中删除许多单词，模型也不敏感并仍能找到正确答案。这表明自动求解器并不遵循数学问题的语义逻辑，可能过拟合于特定单词的存在。