LANS: A Layout-Aware Neural Solver for Plane Geometry Problem

Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, Cheng-Lin Liu

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.
Anthology ID:
2024.findings-acl.153
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2596–2608
Language:
URL:
https://aclanthology.org/2024.findings-acl.153
DOI:
10.18653/v1/2024.findings-acl.153
Bibkey:
Cite (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. Association for Computational Linguistics.
Cite (Informal):
LANS: A Layout-Aware Neural Solver for Plane Geometry Problem (Li et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/autopr/2024.findings-acl.153.pdf
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