Deciphering Oracle Bone Language with Diffusion Models

TL;DR

This paper presents a novel AI-based approach using diffusion models to decipher ancient Oracle Bone Script, overcoming traditional NLP limitations and demonstrating promising results in understanding this ancient language.

Contribution

Introduces Oracle Bone Script Decipher (OBSD), a diffusion-based method for deciphering ancient scripts, marking a new application of AI in historical linguistics.

Findings

Effective in generating decipherment clues

Outperforms traditional NLP methods

Validated on Oracle Bone Script dataset

Abstract

Originating from China's Shang Dynasty approximately 3,000 years ago, the Oracle Bone Script (OBS) is a cornerstone in the annals of linguistic history, predating many established writing systems. Despite the discovery of thousands of inscriptions, a vast expanse of OBS remains undeciphered, casting a veil of mystery over this ancient language. The emergence of modern AI technologies presents a novel frontier for OBS decipherment, challenging traditional NLP methods that rely heavily on large textual corpora, a luxury not afforded by historical languages. This paper introduces a novel approach by adopting image generation techniques, specifically through the development of Oracle Bone Script Decipher (OBSD). Utilizing a conditional diffusion-based strategy, OBSD generates vital clues for decipherment, charting a new course for AI-assisted analysis of ancient languages. To validate its efficacy, extensive experiments were conducted on an oracle bone script dataset, with quantitative results demonstrating the effectiveness of OBSD. Code and decipherment results will be made available at https://github.com/guanhaisu/OBSD.