# Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2003.01912 · HTML: https://mlsanity.com/paper/2003.01912/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities](https://mlsanity.com/paper/2304.07778/) (2023)
- [Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach](https://mlsanity.com/paper/2109.04513/) (2021)
- [Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration](https://mlsanity.com/paper/2507.05108/) (2025)
- [Functional Ensemble Distillation](https://mlsanity.com/paper/2206.02183/) (2022)
- [Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI](https://mlsanity.com/paper/2310.09091/) (2023)
- [Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy](https://mlsanity.com/paper/2310.07310/) (2023)
- [Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia](https://mlsanity.com/paper/2406.04039/) (2024)
- [Restoring Ancient Ideograph: A Multimodal Multitask Neural Network Approach](https://mlsanity.com/paper/2403.06682/) (2024)
- [Review of Computational Epigraphy](https://mlsanity.com/paper/2406.06570/) (2024)
- [DocRevive: A Unified Pipeline for Document Text Restoration](https://mlsanity.com/paper/2604.10077/) (2026)
- [Text Restoration of Ancient Documents with Language Models](https://mlsanity.com/paper/2608.28170/) (2026)

## 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.