From the latter half of the last decade, there has been a growing interest in
developing algorithms for automatically solving mathematical word problems
(MWP). It is a challenging and unique task that demands blending surface level
text pattern recognition with mathematical reasoning. In spite of extensive
research, we are still miles away from building robust representations of
elementary math word problems and effective solutions for the general task. In
this paper, we critically examine the various models that have been developed
for solving word problems, their pros and cons and the challenges ahead. In the
last two years, a lot of deep learning models have recorded competing results
on benchmark datasets, making a critical and conceptual analysis of literature
highly useful at this juncture. We take a step back and analyse why, in spite
of this abundance in scholarly interest, the predominantly used experiment and
dataset designs continue to be a stumbling block. From the vantage point of
having analyzed the literature closely, we also endeavour to provide a road-map
for future math word problem research.

本研究论文从近十年开始，对自动解决数学单词问题的算法进行了批判性评估和未来研究规划，主要关键词为数学单词问题、算法、深度学习模型、数据集设计和研究。