GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning

Chen Jiaqi, Sun Yat-Sen University, China
Jianheng Tang, Shenzhen Campus of Sun Yat-Sen University, China
Jinghui Qin, Sun Yat-Sen University, China
Xiaodan Liang, Shenzhen Campus of Sun Yat-Sen University, China
Lingbo Liu, Sun Yat-Sen University, China
Lingbo Liu, Sun Yat-Sen University, China
Eric P. Xing, Mohamed bin Zayed University of Artificial Intelligence
Liang Lin, Sun Yat-Sen University, China
Liang Lin, Dark Matter AI Inc., China

Preprint: arXiv


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 4,998 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 Copyright © 2021, The Authors. All rights reserved.