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Proceedings of the AAAI Conference on Artificial Intelligence

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InteChar: A Unified Oracle Bone Character List for Ancien...
Xiaolei Diao · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Xiaolei Diao College of Computer Science and Technology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University Department of information engineering and computer science, University of Trento
  • Zhihan Zhou College of Computer Science and Technology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Lida Shi School of Artificial Intelligence, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Ting Wang School of Software Engineering, Tongji University
  • Ruihua Qi School of Archaeology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Daqian Shi Queen Mary, University of London Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Hao Xu College of Computer Science and Technology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University

DOI:

https://doi.org/10.1609/aaai.v40i1.36981

Abstract

Constructing historical language models (LMs) plays a crucial role in aiding archaeological provenance studies and understanding ancient cultures. However, existing resources present major challenges for training effective LMs on historical texts. First, the scarcity of historical language samples renders unsupervised learning approaches based on large text corpora highly inefficient, hindering effective pre-training. Moreover, due to the considerable temporal gap and complex evolution of ancient scripts, the absence of comprehensive character encoding schemes limits the digitization and computational processing of ancient texts, particularly in early Chinese writing. To address these challenges, we introduce InteChar, a unified and extensible character list that integrates unencoded oracle bone characters with traditional and modern Chinese. InteChar enables consistent digitization and representation of historical texts, providing a foundation for robust modeling of ancient scripts. To evaluate the effectiveness of InteChar, we construct the Oracle Corpus Set (OracleCS), an ancient Chinese corpus that combines expert-annotated samples with LLM-assisted data augmentation, centered on Chinese oracle bone inscriptions. Extensive experiments show that models trained with InteChar on OracleCS achieve substantial improvements across various historical language understanding tasks, confirming the effectiveness of our approach and establishing a solid foundation for future research in ancient Chinese NLP.

How to Cite

Diao, X., Zhou, Z., Shi, L., Wang, T., Qi, R., Shi, D., & Xu, H. (2026). InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 211–219. https://doi.org/10.1609/aaai.v40i1.36981

Issue

Section

AAAI Technical Track on Application Domains I

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