Machine Learning for Ancient Languages: A Survey

Abstract

Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in Artificial Intelligence and Machine Learning have enabled analyses on a scale and in a detail that are reshaping the field of Humanities, similarly to how microscopes and telescopes have contributed to the realm of Science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script and medium, spanning over three and a half millennia of civilisations around the ancient world. To analyse the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitisation, restoration, attribution, linguistic analysis, textual criticism, translation and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the Humanities and Machine Learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, flagging promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the Humanities and Machine Learning.

Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy

“…Some efforts have expanded resources for specific ancient languages. The PROIEL offers 187,000 syntactic annotations for Ancient Greek [2], while the Index Thomisticus Treebank contains over 60 million words from Medieval Latin [2]. However, diversity issues persist, and most ancient languages remain under-resourced.…”

“…Deciphering ancient scripts through the study of inscriptions, known as epigraphy, provides valuable insights into historical languages and cultures. However, the automated recognition and interpretation of ancient writing systems pose considerable challenges for ML techniques [1,2]. In particular, Old Aramaic scripts present a highly complex task for algorithmic analysis.…”

Use of Open Access AI in teaching classical antiquity. A methodological proposal

“…Another area is the study of calligraphic ductus , in which computerised palaeography enables a stylometric analysis to be carried out using algorithms. This is very useful when attempting to identify handwriting in fields such as palaeography, epigraphy and diplomacy (Azmi et al ., 2011; Cuéllar, 2023; Sommerschield et al ., 2023; Wolf et al ., 2011). It can even transcribe documents, examining and comparing them to recognise their authorship (Kang, 2021; Meza-Lovn, 2012; Tuzzi and Cortelazzo, 2018).…”

Explainability and transparency in the realm of digital humanities: toward a historian XAI

“…While numerous approaches for OCR/HTR and text reconstruction tackle different languages (e.g., Akkadian (Lazar et al, 2021;Fetaya et al, 2020), hieroglyphs (Barucci et al, 2021), etc. ), the analysis of historical texts is heavily biased towards Ancient Greek and Latin (Sommerschield et al, 2023).…”