Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers
This survey addresses the challenge of building effective AI for elementary math problem-solving, which is incremental as it synthesizes existing research to identify limitations and guide future work.
The paper critically surveys deep learning models for solving mathematical word problems, highlighting that despite competing results on benchmarks, current experiment and dataset designs remain a major obstacle to robust solutions.
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.