Modeling chinese character evolution using manifold learning and neural ODEs
Tracing the Evolution of Oracle Bone Characters Across Three Millennia
Computation and Language
Summary
Many ancient Chinese characters from oracle bones are hard to understand because their forms changed a lot over thousands of years. The authors created a new method that represents each character’s shape in different historical eras as a point in a special space, tracking how those points change continuously over time. They use a kind of neural network that can model smooth transformations to learn how characters evolved from one era to the next. This helps link ancient characters with their later versions, improving understanding of their development.
What this means in practice
- •For digital humanities teams: Improve linking and decipherment of ancient Chinese characters by modeling their continuous evolution across historical eras.
- •For historical linguistics software developers: Develop tools that accurately predict character changes between dynasties using a learned continuous model of script evolution.
Authors
Tianhao Fu, Xinxin Xu, Spike Wang, Cunyi Kang, Jian Cao, Xixin Cao
Abstract
Of the approximately 4,500 Oracle Bone Inscription (OBI) characters discovered from the Shang dynasty, only about 1,600 have been deciphered. Many computational approaches compare OBI with glyphs from one historical period at a time. However, during the evolution of Chinese characters, significant structural or semantic changes often occur in uncertain dynasties. A single-period reference may be insufficient when relevant forms change substantially between observed eras. Therefore, we propose the \textbf{Manifold-based Script Evolution Framework (MSEF)}, a framework that models the evolution series (OBI, Bronze, Seal, Clerical, Regular) of Chinese characters as the continual evolution of a manifold space. MSEF represents each character as an era-specific manifold point and learns continuous inter-era transition rules via Neural Ordinary Differential Equations. Both manifold space and transition dynamics can be trained end-to-end through character evolution pairs across any two eras.