  To solve Math Word Problems, human students leverage diverse reasoning logic
that reaches different possible equation solutions. However, the mainstream
sequence-to-sequence approach of automatic solvers aims to decode a fixed
solution equation supervised by human annotation. In this paper, we propose a
controlled equation generation solver by leveraging a set of control codes to
guide the model to consider certain reasoning logic and decode the
corresponding equations expressions transformed from the human reference. The
empirical results suggest that our method universally improves the performance
on single-unknown (Math23K) and multiple-unknown (DRAW1K, HMWP) benchmarks,
with substantial improvements up to 13.2% accuracy on the challenging
multiple-unknown datasets.
