Arabshahi, Singh, and Anandkumar (2018) propose a method for creating a dataset of symbolic mathematical equations for the tasks of symbolic equation verification and equation completion. Unfortunately, a dataset constructed using the method they propose will suffer from two serious flaws. First, the class of true equations that the procedure can generate will be very limited. Second, because true and false equations are generated in completely different ways, there are likely to be artifactual features that allow easy discrimination. Moreover, over the class of equations they consider, there is an extremely simple probabilistic procedure that solves the problem of equation verification with extremely high reliability. The usefulness of this problem in general as a testbed for AI systems is therefore doubtful.

翻译：Arabshahi、Singh和Anandkumar(2018年)提出了为象征性等式核查和完成等式的任务创建象征性数学方程式数据集的方法。 不幸的是,使用他们建议的方法构建的数据集将存在两个严重缺陷。 首先,该程序能够产生的真实方程式类别将非常有限。 其次,由于真实和假方程式是以完全不同的方式生成的,因此可能存在容易歧视的原生特征。 此外,在他们考虑的方程式类别中,有一个极其简单的概率化程序,可以非常可靠地解决等式核查问题。因此,这个问题作为AI系统的测试台,一般来说是否有用是值得怀疑的。