English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Geometry problem solving (GPS) is a high-level mathematical reasoningrequiring the capacities of multi-modal fusion and geometric knowledgeapplication. Recently, neural solvers have shown great potential in GPS butstill be short in diagram presentation and modal fusion. In this work, weconvert diagrams into basic textual clauses to describe diagram featureseffectively, and propose a new neural solver called PGPSNet to fuse multi-modalinformation efficiently. Combining structural and semantic pre-training, dataaugmentation and self-limited decoding, PGPSNet is endowed with rich knowledgeof geometry theorems and geometric representation, and therefore promotesgeometric understanding and reasoning. In addition, to facilitate the researchof GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K,labeled with both fine-grained diagram annotation and interpretable solutionprogram. Experiments on PGPS9K and an existing dataset Geometry3K validate thesuperiority of our method over the state-of-the-art neural solvers. Our code,dataset and appendix material are available at\url{https://github.com/mingliangzhang2018/PGPS}.

A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram