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        <title>LANS: A Layout-Aware Neural Solver for Plane Geometry Problem</title>
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    <abstract>Geometry problem solving (GPS) is a challenging mathematical reasoning task requiring multi-modal understanding, fusion, and reasoning. Existing neural solvers take GPS as a vision-language task but are short in the representation of geometry diagrams that carry rich and complex layout information. In this paper, we propose a layout-aware neural solver named LANS, integrated with two new modules: multimodal layout-aware pre-trained language module (MLA-PLM) and layout-aware fusion attention (LA-FA). MLA-PLM adopts structural-semantic pre-training (SSP) to implement global relationship modeling, and point-match pre-training (PMP) to achieve alignment between visual points and textual points. LA-FA employs a layout-aware attention mask to realize point-guided cross-modal fusion for further boosting layout awareness of LANS. Extensive experiments on datasets Geometry3K and PGPS9K validate the effectiveness of the layout-aware modules and superior problem-solving performance of our LANS solver, over existing symbolic and neural solvers. We have made our code and data publicly available.</abstract>
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%0 Conference Proceedings
%T LANS: A Layout-Aware Neural Solver for Plane Geometry Problem
%A Li, Zhong-Zhi
%A Zhang, Ming-Liang
%A Yin, Fei
%A Liu, Cheng-Lin
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F li-etal-2024-lans
%X Geometry problem solving (GPS) is a challenging mathematical reasoning task requiring multi-modal understanding, fusion, and reasoning. Existing neural solvers take GPS as a vision-language task but are short in the representation of geometry diagrams that carry rich and complex layout information. In this paper, we propose a layout-aware neural solver named LANS, integrated with two new modules: multimodal layout-aware pre-trained language module (MLA-PLM) and layout-aware fusion attention (LA-FA). MLA-PLM adopts structural-semantic pre-training (SSP) to implement global relationship modeling, and point-match pre-training (PMP) to achieve alignment between visual points and textual points. LA-FA employs a layout-aware attention mask to realize point-guided cross-modal fusion for further boosting layout awareness of LANS. Extensive experiments on datasets Geometry3K and PGPS9K validate the effectiveness of the layout-aware modules and superior problem-solving performance of our LANS solver, over existing symbolic and neural solvers. We have made our code and data publicly available.
%U https://aclanthology.org/2024.findings-acl.153
%P 2596-2608

Markdown (Informal)

[LANS: A Layout-Aware Neural Solver for Plane Geometry Problem](https://aclanthology.org/2024.findings-acl.153) (Li et al., Findings 2024)

• LANS: A Layout-Aware Neural Solver for Plane Geometry Problem (Li et al., Findings 2024)

ACL

• Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, and Cheng-Lin Liu. 2024. LANS: A Layout-Aware Neural Solver for Plane Geometry Problem. In Findings of the Association for Computational Linguistics ACL 2024, pages 2596–2608, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.