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arXiv:2003.01912cs.CL

Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks

Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin

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Abstract

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.

arXiv comments
Journal reference
DOI
doi:10.1073/pnas.2003794117
Subjects
cs.CLComputation and Language cs.LGMachine Learning stat.MLMachine Learning (Statistics)

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