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Geometric Question Answering Towards Multimodal Numerical Reasoning
Jiaqi Chen · Jianheng Tang · Jinghui Qin · Xiaodan Liang · Lingbo Liu · Eric Xing · Liang Lin

Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. However, the existing methods were highly dependent on handcraft rules and were merely evaluated on small-scale datasets. Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 5,010 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems. Compared with another publicly available dataset GeoS, GeoQA is 25 times larger, in which the program annotations can provide a practical testbed for future research on explicit and explainable numerical reasoning. Moreover, we introduce a Neural Geometric Solver (NGS) to address geometric problems by comprehensively parsing multimodal information and generating interpretable programs. We further add multiple self-supervised auxiliary tasks on NGS to enhance cross-modal semantic representation. Extensive experiments on GeoQA validate the effectiveness of our proposed NGS and auxiliary tasks. However, the results are still significantly lower than human performance, which leaves large room for future research.

Author Information

Jianheng Tang (Sun Yat-sen University)
Jinghui Qin (Sun Yat-sen University)
Xiaodan Liang (Sun Yat-sen University)
Lingbo Liu (Sun Yat-sen University)
Eric Xing (Petuum Inc.)
Liang Lin (Sun Yat-Sen University)

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