Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks
This addresses the challenge of incomplete historical records for scholars of ancient Mesopotamian culture, though it appears incremental as it applies an existing method to a new domain.
The paper tackled the problem of missing information in fragmentary Babylonian cuneiform tablets by using recurrent neural networks to model the Akkadian language, aiming to assist or automate the restoration of breaks in texts from the Achaemenid period.
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.