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

• URL: http://arxiv.org/abs/2311.16476v2
• Date: Tue, 20 Feb 2024 03:35:46 GMT
• Title: LANS: A Layout-Aware Neural Solver for Plane Geometry Problem
• Authors: Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, Cheng-Lin Liu
• Abstract summary: We propose a layout-aware neural solver named LANS, integrated with two new modules: multimodal layout-aware pre-trained language module and layout-aware fusion attention (LA-FA) 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.
• Score: 43.192629815250285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The code will be made public available soon.

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