Neural Machine Translation of Old Assyrian Cuneiform Business Records into English

Abstract This research develops a neural machine translation system for converting ancient Old Assyrian cuneiform business records into modern English, addressing a long standing challenge in digital humanities and historical linguistics. Using a large corpus of annotated cuneiform texts, the study applies Transformer based sequence to sequence models to learn linguistic patterns in ancient commercial documentation. The system is evaluated using standard translation quality metrics and qualitative linguistic analysis. Results show that modern deep learning approaches can significantly improve the accessibility and interpretation of ancient texts, enabling historians, linguists, and archaeologists to analyze early economic systems more efficiently and at scale.