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A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram
Zhang Ming-Liang1,2; Yin Fei1,2; Liu Cheng-Lin1,2
Conference NameProceedings of the 32nd International Joint Conference on Artificial Intelligence
Conference Date2023-7-19
Conference Place中国 澳门

Geometry problem solving (GPS) is a high-level mathematical reasoning requiring the capacities of multi-modal fusion and geometric knowledge application. Recently, neural solvers have shown great potential in GPS but still be short in diagram presentation and modal fusion. In this work, we convert diagrams into basic textual clauses to describe diagram features effectively, and propose a new neural solver called PGPSNet to fuse multimodal information efficiently. Combining structural and semantic pre-training, data augmentation and self-limited decoding, PGPSNet is endowed with rich knowledge of geometry theorems and geometric representation, and therefore promotes geometric understanding and reasoning. In addition, to facilitate the research of GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K, labeled with both fine-grained diagram annotation and interpretable solution program. Experiments on PGPS9K and an existing dataset Geometry3K validate the superiority of our method over the state-of-the-art neural solvers.

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IS Representative Paper
Sub direction classification知识表示与推理
planning direction of the national heavy laboratory认知决策知识体系
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Document Type会议论文
Corresponding AuthorLiu Cheng-Lin
Affiliation1.MAIS, Institute of Automation of Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang Ming-Liang,Yin Fei,Liu Cheng-Lin. A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram[C],2023:3374-3382.
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