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Automatic math problem solving has recently attracted increasing attention asa long-standing AI benchmark. In this paper, we focus on solving geometricproblems, which requires a comprehensive understanding of textual descriptions,visual diagrams, and theorem knowledge. However, the existing methods werehighly dependent on handcraft rules and were merely evaluated on small-scaledatasets. Therefore, we propose a Geometric Question Answering dataset GeoQA,containing 4,998 geometric problems with corresponding annotated programs,which illustrate the solving process of the given problems. Compared withanother publicly available dataset GeoS, GeoQA is 25 times larger, in which theprogram annotations can provide a practical testbed for future research onexplicit and explainable numerical reasoning. Moreover, we introduce a NeuralGeometric Solver (NGS) to address geometric problems by comprehensively parsingmultimodal information and generating interpretable programs. We further addmultiple self-supervised auxiliary tasks on NGS to enhance cross-modal semanticrepresentation. Extensive experiments on GeoQA validate the effectiveness ofour proposed NGS and auxiliary tasks. However, the results are stillsignificantly lower than human performance, which leaves large room for futureresearch. Our benchmark and code are released athttps://github.com/chen-judge/GeoQA .

GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning