Abstract
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 language | English |
|---|---|
| Pages (from-to) | 22743-22751 |
| Number of pages | 9 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 117 |
| Issue number | 37 |
| DOIs | |
| State | Published - 15 Sep 2020 |
Bibliographical note
Publisher Copyright:© 2020 National Academy of Sciences. All rights reserved.
Funding
This research was supported by the Ministry of Science & Technology, Israel, Grant 89540 for the project “Human-Computer Collaboration for Studying Life and Environment in Babylonian Exile” of Sh.G. and Amos Azaria, as part of Sh.G.’s Babylonian Engine initiative. We thank Eugene McGarry for language editing, Avital Romach for her assistance with the final proofs of this paper, Klaus Wagensonner for tablet photographs, and Moshe Shtekel for designing the web-tool for Atrahasis. We especially thank the anonymous reviewers for their detailed remarks and corrections.
| Funders | Funder number |
|---|---|
| Amos Azaria | |
| Ministry of science and technology, Israel | 89540 |