From Clay to Code: Transforming Hittite Texts for Machine Learning

Emma Yavasan, Shai Gordin


Abstract
This paper presents a comprehensive method-ology for transforming XML-encoded Hittite cuneiform texts into computationally accessi-ble formats for machine learning applications. Drawing from a corpus of 8,898 texts (558,349 tokens in total) encompassing 145 cataloged genres and compositions, we develop a struc-tured approach to preserve both linguistic and philological annotations while enabling compu-tational analysis. Our methodology addresses key challenges in ancient language processing, including the handling of fragmentary texts, multiple language layers, and complex anno-tation systems. We demonstrate the applica-tion of our corpus through experiments with T5 models, achieving significant improvements in Hittite-to-German translation (ROUGE-1: 0.895) while identifying limitations in morpho-logical glossing tasks. This work establishes a standardized, machine-readable dataset in Hit-tite cuneiform, which also maintains a balance with philological accuracy and current state-of-the-art.
Anthology ID:
2025.alp-1.10
Volume:
Proceedings of the Second Workshop on Ancient Language Processing
Month:
May
Year:
2025
Address:
The Albuquerque Convention Center, Laguna
Editors:
Adam Anderson, Shai Gordin, Bin Li, Yudong Liu, Marco C. Passarotti, Rachele Sprugnoli
Venues:
ALP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
77–86
Language:
URL:
https://preview.aclanthology.org/fix-sig-urls/2025.alp-1.10/
DOI:
Bibkey:
Cite (ACL):
Emma Yavasan and Shai Gordin. 2025. From Clay to Code: Transforming Hittite Texts for Machine Learning. In Proceedings of the Second Workshop on Ancient Language Processing, pages 77–86, The Albuquerque Convention Center, Laguna. Association for Computational Linguistics.
Cite (Informal):
From Clay to Code: Transforming Hittite Texts for Machine Learning (Yavasan & Gordin, ALP 2025)
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PDF:
https://preview.aclanthology.org/fix-sig-urls/2025.alp-1.10.pdf