mlsanityData to 7 Oct 2026

Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks

arXiv 2003.01912 ↗First posted 4 Mar 2020cs.CL cs.LG stat.MLcited by 11

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Abstract (arXiv)

The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Despite being an invaluable resource, many tablets are fragmented leading to missing information. Currently these missing parts are manually completed by experts. In this work we investigate the possibility of assisting scholars and even automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia by modelling the language using recurrent neural networks.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 3; 2025: 1; 2026: 2.

11 papers.

  1. 2023SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanitiescited by 9
  2. 2021Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approachcited by 8
  3. 2022Functional Ensemble Distillationcited by 3
  4. 2023Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAIcited by 3
  5. 2025Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restorationcited by 3
  6. 2024Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millenniacited by 2
  7. 2023Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphycited by 1
  8. 2024Restoring Ancient Ideograph: A Multimodal Multitask Neural Network Approachcited by 1
  9. 2024Review of Computational Epigraphycited by 1
  10. 2026DocRevive: A Unified Pipeline for Document Text Restorationcited by 0
  11. 2026Text Restoration of Ancient Documents with Language Modelscited by 0