A Framework of Cross-Modal Learning for Solving Geometry Problems
Fucheng Guo, Pengpeng Jian, Yanli Wang, Qingjiang Wang · 2021
This paper presents a framework of deep cross-modal learning networks for solving geometry problems. Existing geometry solvers either focus on single-modal problems or multi-modal problems, and they cannot fit each other. In this paper, we propose a cross-modal learning architecture for solving geometry problems, which can effectively solve both text and image-text geometry problems. To attack the representation of cross-modal features is a key challenge in understanding geometry problems that this paper adopts a shared encoder, in which the text and (or) image features are masked by using self-attention units and a multi-layer transformer is used to realize the interaction between cross-modal features. Further, we adopt a shared decoder to decode the single-modal features or the series sequence of multi-modal features according to the input of the encoder is single-modal or multi-modal problems. The representation of the decoder is transferred to task-specific heads for geometry relations extraction, theorems reasoning and geometry problem solving. The proposed algorithm can produce the solutions of geometry problems in a readable way. The experimental results shown that the proposed algorithm has good property and performance in solving geometry problems.