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Machine Learning for Ancient Languages: A Survey

Summary

This survey maps the published research applying machine learning to texts in ancient languages. It covers any script or writing medium from the first Mesopotamian writing (about 3400 BCE) to the end of the first millennium CE, and it leaves out pre-2000 studies and work that does not use ML. The authors organize the literature with a taxonomy that mirrors the real workflow scholars follow with ancient sources, covering the conversion of texts into digital form, the restoration of damaged passages, attribution, the study of their language, textual criticism, translating, and deciphering. Within each area they review methods ranging from feature-engineered classifiers to CNN, RNN and Transformer models. Appendices count publications by task, input modality (text or image), model family and language. The survey finds rapid growth in the field over roughly the last five years. It also finds that coverage across languages is uneven, and that the lack of shared benchmarks and the use of differing datasets make it hard to compare results. The authors argue that progress depends on close collaboration between humanists and ML researchers, on interpretable outputs rather than chasing metrics, and on rigorous evaluation.

Key points

  • Taxonomy: The survey defines seven task areas, with subtasks such as fragment reassembly, chronological and geographical attribution, POS tagging and parsing, stemmatology, and machine translation.
  • Languages covered: The most studied are Ancient Greek (63 papers), Latin (59), cuneiform (30, covering Akkadian, Sumerian and Babylonian) and Old Chinese (21). The authors warn that this imbalance risks "digital colonialism."
  • Model trends: RNNs (37 works), CNNs (35) and Transformers (33) dominate overall. Transformers appear almost entirely in 2020–2023 (30 works).
  • Tasks by modality: POS tagging and parsing is the largest text-based category (45 works). Recognition relies mostly on visual input (17 works) or on both text and images (12).
  • Main recommendations: The authors call for standardized benchmarks and metrics, proper train/validation/test splits or cross-validation, and interdisciplinary teamwork.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Natural language processing

Cited by

47 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 40 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00481

@article{sommerschield2023machine,
  title = {{Machine Learning for Ancient Languages: A Survey}},
  author = {Sommerschield, Thea and Assael, Yannis and Pavlopoulos, John and Stefanak, Vanessa and Senior, Andrew and Dyer, Chris and Bodel, John and Prag, Jonathan and Androutsopoulos, Ion and Freitas, Nando de},
  journal = {Computational Linguistics},
  year = {2023},
  doi = {10.1162/coli\_a\_00481},
  url = {https://doi.org/10.1162/coli_a_00481}
}

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