@Article{cmes.2023.023243,
AUTHOR = {Pengpeng Jian, Fucheng Guo, Yanli Wang, Yang Li},
TITLE = {Solving Geometry Problems via Feature Learning and Contrastive Learning of Multimodal Data},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {136},
YEAR = {2023},
NUMBER = {2},
PAGES = {1707--1728},
URL = {http://www.techscience.com/CMES/v136n2/51552},
ISSN = {1526-1506},
ABSTRACT = {This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data. A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems. Existing methods either focus on single-modal or multimodal problems, and they cannot fit each other. A general geometry problem solver should obviously be able to process various modal problems at the same time. In this paper, a shared feature-learning model of multimodal data is adopted to learn the unified feature representation of text and image, which can solve the heterogeneity issue between multimodal geometry problems. A contrastive learning model of multimodal data enhances the semantic relevance between multimodal features and maps them into a unified semantic space, which can effectively adapt to both single-modal and multimodal downstream tasks. Based on the feature extraction and fusion of multimodal data, a proposed geometry problem solver uses relation extraction, theorem reasoning, and problem solving to present solutions in a readable way. Experimental results show the effectiveness of the method.},
DOI = {10.32604/cmes.2023.023243}
}