zzli2022/lans

Code and Data for paper: "LANS: A Layout-Aware Neural Solver for Plane Geometry Problem"

LANS

Code and Data for ACL2024 Findings paper: "LANS: A Layout-Aware Neural Solver for Plane Geometry Problem". 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.

Figure 1. Overview of PGPSNet solver.

Figure 1. Overview of PGPSNet solver.

PGPS9K Dataset

You could download the dataset from Dataset Homepage. In default, unzip the dataset file to the fold ./datasets.

Multimodal Layout-Aware Pre-training & LANS Finetuning

code comming soon