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GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
  • Jiaqi Chen, Sun Yat-Sen University
  • 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 P. Xing, Mohamed Bin Zayed University of Artificial Intelligence
  • Liang Lin, Sun Yat-Sen University
Document Type
Conference Proceeding

Automatic math problem solving has recently attracted increasing attention as a longstanding 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. Our benchmark and code are released at

Publication Date
  • Annotated program; Geometric problems; Hand-craft rules; Math problem solving; Multi-modal; Numerical reasoning; Program annotation; Question Answering; Small scale; Textual description

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OA version available on ACL Anthology

Citation Information
J. Chen et al., “GeoQA: A geometric question answering benchmark towards multimodal numerical reasoning,” Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , pp. 513–523, 2021, doi: 10.18653/V1/2021.FINDINGS-ACL.46.