From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages
Paper · 2026 · Matthias Schöffel and Esteban Garces Arias
Summary
Medieval Romance texts are difficult to tag for parts of speech. Their spelling is unstable, their morphology is rich, and little annotated data exists, so tools built for modern standard languages often fail on them. The authors test this task on three varieties: Medieval Occitan (NAF6195, a 14th-century manuscript of the Vida de Sant Honorat), Medieval Catalan (the 13th-century chronicle Llibre dels Fets) and Medieval French (the anatomy section of Gui de Chauliac's 15th-century Grande Chirurgie). All tagsets were mapped to Universal Dependencies conventions. The off-the-shelf taggers UDPipe and COLaF are compared with two open LLMs, Gemma3-12B and Phi4-14B. The LLMs are used through zero-shot and few-shot prompting with several decoding strategies, monolingual fine-tuning, and bilingual or trilingual cross-lingual training. LLM-based setups outperformed the traditional tools in every configuration. Fine-tuning and multilingual training gave the largest gains, and a carefully chosen language pair sometimes beat training on all three languages. The study matters because it offers evidence-based guidance for digital humanities annotation pipelines. It shows that language relatedness and corpus properties both shape how well transfer works. Code, models and processed data are publicly released.
Key points
- Datasets differ widely in size and genre:
- NAF: Occitan, literary, 45,457 tokens.
- CAT: Catalan, chronicle, 59,359 tokens.
- Chauliac: French, medical, 2,443 tokens.
- For the transfer experiments, each corpus is split 80/20 into training and test data.
- Five settings were evaluated:
- traditional taggers;
- prompting with a fixed few-shot block mixing all three varieties, tested under beam search and temperature, top-k and nucleus sampling;
- monolingual fine-tuning;
- bilingual cross-lingual transfer (CLTF);
- trilingual CLTF.
- Per-class F1 on NAF: the best LLM method improves most over UDPipe on PROPN (25.85 → 92.47, +66.62) and NUM (+62.97). SCONJ rises from 52.94 to 94.62 under Gemma3 CLTF.
- Transfer depends on the language pair: Catalan data helps Occitan, and CAT+FR training benefits the small Chauliac corpus. Close genealogical relationship is not enough on its own to make transfer work; the amount of data, the type of text and how consistently it was annotated play a role as well.
- Limitations:
- The fine-tuned 12–14B models, trained with LoRA on a single H100, are far costlier than UDPipe or COLaF.
- Smaller LLMs, XLM-R-style encoders and learning-curve experiments were not tested.
- The code is available at github.com/msch38/medieval-romance-pos.
Institutions
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksLiterature and Literary Theory · 519 works
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksPart-of-speech tagging · 126 works
Cite
Cite as Matthias Schöffel and Esteban Garces Arias. 2026. From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages. Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities.
@inproceedings{schoffel2026traditional,
title = {{From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages}},
author = {Matthias Schöffel and Esteban Garces Arias},
booktitle = {Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities},
year = {2026},
doi = {10.18653/v1/2026.nlp4dh-1.27},
url = {https://doi.org/10.18653/v1/2026.nlp4dh-1.27}
}References
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