Restoration of fragmentary Babylonian texts using recurrent neural networks

Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


The main sources of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Many of these tablets are damaged, leading to missing information. Currently, the missing text is manually reconstructed by experts. We investigate the possibility of assisting scholars, by modeling the language using recurrent neural networks and automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia.

Original languageEnglish
Pages (from-to)22743-22751
Number of pages9
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number37
StatePublished - 15 Sep 2020


  • Babylonian heritage
  • cuneiform script
  • Late Babylonian dialect
  • Achaemenid empire
  • neural networks


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