Work

Restoration of fragmentary Babylonian texts using recurrent neural networks

Year: 2020

Type: article

Abstract: Significance The documentary sources for the political, economic, and social history of ancient Mesopotamia constitute hundreds of thousands of clay tablets inscribed in the cuneiform script. Most tablets are damaged, leaving gaps in the texts written on them, and the missing portions must be restored by experts. This paper uses available digitized texts for training advanced machine-learning algorithms to restore daily economic and administrative documents from the Persian empire (sixth to fourth centuries BCE). As the amount of digitized texts grows, the model can be trained to restore damaged texts belonging to other genres, such as scientific or literary texts. Therefore, this is a first step for a large-scale reconstruction of a lost ancient heritage.

Source: Proceedings of the National Academy of Sciences

DOI: 10.1073/pnas.2003794117

Authors: Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin

Institutions: Bar-Ilan University, Weizmann Institute of Science, Ariel University

Language: English

Topic: Ancient Near East History

Subfield: Archeology

Field: Arts and Humanities

Domain: Social Sciences

Open Access status: green

Funder: Ministry of Science and Technology, Israel

Award: G7499811677