  Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to
automatic math word problem solving. Despite its simplicity, a drawback still
remains: a math word problem can be correctly solved by more than one
equations. This non-deterministic transduction harms the performance of maximum
likelihood estimation. In this paper, by considering the uniqueness of
expression tree, we propose an equation normalization method to normalize the
duplicated equations. Moreover, we analyze the performance of three popular
SEQ2SEQ models on the math word problem solving. We find that each model has
its own specialty in solving problems, consequently an ensemble model is then
proposed to combine their advantages. Experiments on dataset Math23K show that
the ensemble model with equation normalization significantly outperforms the
previous state-of-the-art methods.
