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.

本文提出了一种基于 M-tree 的求解数学问题的方法 SUMC-Solver，相比现有方法在同等设置下表现更好，在低资源条件下表现更佳。

面向数学题的结构统一 M 树编码求解器

Structure-Unified M-Tree Coding Solver for MathWord Problem

Grounding natural language in images, such as localizing "the black dog on
the left of the tree", is one of the core problems in artificial intelligence,
as it needs to comprehend the fine-grained and compositional language space.
However, existing solutions rely on the association between the holistic
language features and visual features, while neglect the nature of
compositional reasoning implied in the language. In this paper, we propose a
natural language grounding model that can automatically compose a binary tree
structure for parsing the language and then perform visual reasoning along the
tree in a bottom-up fashion. We call our model RVG-TREE: Recursive Grounding
Tree, which is inspired by the intuition that any language expression can be
recursively decomposed into two constituent parts, and the grounding confidence
score can be recursively accumulated by calculating their grounding scores
returned by sub-trees. RVG-TREE can be trained end-to-end by using the
Straight-Through Gumbel-Softmax estimator that allows the gradients from the
continuous score functions passing through the discrete tree construction.
Experiments on several benchmarks show that our model achieves the
state-of-the-art performance with more explainable reasoning.

本文提出了一种基于 RVG-TREE 的自然语言基础模型，其可以自动组成二叉树结构进行语言解析，并沿着树进行自底向上的视觉推理，实现更具解释性的推理效果。