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

Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers