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

arXiv:2311.16476v2 Announce Type: replace
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 …

abstract arxiv cs.ai cs.cv diagrams fusion geometry gps information language mathematical reasoning modal multi-modal paper plane reasoning representation solver type understanding vision