  As one of the challenging NLP tasks, designing math word problem (MWP)
solvers has attracted increasing research attention for the past few years. In
previous work, models designed by taking into account the properties of the
binary tree structure of mathematical expressions at the output side have
achieved better performance. However, the expressions corresponding to a MWP
are often diverse (e.g., $n_1+n_2 \times n_3-n_4$, $n_3\times n_2-n_4+n_1$,
etc.), and so are the corresponding binary trees, which creates difficulties in
model learning due to the non-deterministic output space. In this paper, we
propose the Structure-Unified M-Tree Coding Solver (SUMC-Solver), which applies
a tree with any M branches (M-tree) to unify the output structures. To learn
the M-tree, we use a mapping to convert the M-tree into the M-tree codes, where
codes store the information of the paths from tree root to leaf nodes and the
information of leaf nodes themselves, and then devise a Sequence-to-Code
(seq2code) model to generate the codes. Experimental results on the widely used
MAWPS and Math23K datasets have demonstrated that SUMC-Solver not only
outperforms several state-of-the-art models under similar experimental settings
but also performs much better under low-resource conditions.
