---
title: "From Traditional Taggers to LLMs: A Comparative Study of POS"
description: "The study matters because it offers evidence-based guidance for digital humanities annotation pipelines. 29 references."
authors: ["Matthias Schöffel", "Esteban Garces Arias"]
year: 2026
venue: "Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities"
source: "https://aclanthology.org/2026.nlp4dh-1.27/"
doi: "10.18653/v1/2026.nlp4dh-1.27"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/schoffel-2026-traditional-taggers-llms-comparative-study/"
---
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# From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages

**Authors:** Matthias Schöffel and [Esteban Garces Arias](https://md.research.elamur.ai/free/en/authors/esteban-garces-arias/) · **Year:** 2026 · **Venue:** [Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities](https://md.research.elamur.ai/free/en/venues/nlp4dh/) ([International Conference on Natural Language Processing for the Digital Humanities 2026](https://md.research.elamur.ai/free/en/venues/nlp4dh/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.nlp4dh-1.27/) · [PDF](https://aclanthology.org/2026.nlp4dh-1.27.pdf) · [DOI 10.18653/v1/2026.nlp4dh-1.27](https://doi.org/10.18653/v1/2026.nlp4dh-1.27) · [OpenAlex](https://openalex.org/W7166823196)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## 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.

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

## Institutions

- [Ludwig-Maximilians-Universität München](https://md.research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/) — University, Munich, Germany · 113 works · 56,817 citations
- [Munich Center for Machine Learning](https://md.research.elamur.ai/free/en/universities/munich-center-for-machine-learning/) — University, Munich, Germany · 62 works · 1,167 citations
- [Bavarian Academy of Sciences and Humanities](https://md.research.elamur.ai/free/en/universities/bavarian-academy-of-sciences-and-humanities/) — University, Munich, Germany · 2 works · 1 citation

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## 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.

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

```bibtex
@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}
}
```

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*Updated 1 November 2026*