  With the recent advancements in deep learning, neural solvers have gained
promising results in solving math word problems. However, these SOTA solvers
only generate binary expression trees that contain basic arithmetic operators
and do not explicitly use the math formulas. As a result, the expression trees
they produce are lengthy and uninterpretable because they need to use multiple
operators and constants to represent one single formula. In this paper, we
propose sequence-to-general tree (S2G) that learns to generate interpretable
and executable operation trees where the nodes can be formulas with an
arbitrary number of arguments. With nodes now allowed to be formulas, S2G can
learn to incorporate mathematical domain knowledge into problem-solving, making
the results more interpretable. Experiments show that S2G can achieve a better
performance against strong baselines on problems that require domain knowledge.
