Shengwei Han
2024
Deciphering Oracle Bone Language with Diffusion Models
Haisu Guan
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Huanxin Yang
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Xinyu Wang
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Shengwei Han
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Yongge Liu
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Lianwen Jin
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Xiang Bai
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Yuliang Liu
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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.
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Co-authors
- Haisu Guan 1
- Huanxin Yang 1
- Xinyu Wang 1
- Yongge Liu 1
- Lianwen Jin 1
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