# Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource

arXiv: https://arxiv.org/abs/1806.03757 · HTML: https://mlsanity.com/paper/1806.03757/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 10 of 10)

- [CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation](https://mlsanity.com/paper/2305.17267/) (2023)
- [GRDD: A Dataset for Greek Dialectal NLP](https://mlsanity.com/paper/2308.00802/) (2023)
- [GRDD+: An Extended Greek Dialectal Dataset with Cross-Architecture Fine-tuning Evaluation](https://mlsanity.com/paper/2511.03772/) (2025)
- [Modern Models, Medieval Texts: A POS Tagging Study of Old Occitan](https://mlsanity.com/paper/2503.07827/) (2025)
- [Language Varieties of Italy: Technology Challenges and Opportunities](https://mlsanity.com/paper/2209.09757/) (2022)
- [Noisy Parallel Data Alignment](https://mlsanity.com/paper/2301.09685/) (2023)
- [Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages](https://mlsanity.com/paper/2502.09778/) (2025)
- [Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages](https://mlsanity.com/paper/2506.17715/) (2025)
- [Surfing the modeling of PoS taggers in low-resource scenarios](https://mlsanity.com/paper/2402.02449/) (2024)
- [Transfer Learning for an Endangered Slavic Variety: Dependency Parsing in Pomak Across Contact-Shaped Dialects](https://mlsanity.com/paper/2603.28033/) (2026)

## Abstract (arXiv)

Most work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual endangered language, Griko. We present a resource that contains 114 narratives in Griko, along with sentence-level translations in Italian, and provides gold annotations for the test set. Based on a previously collected small corpus, we investigate several traditional methods, as well as methods that take advantage of monolingual data or project cross-lingual POS tags. We show that the combination of a semi-supervised method with cross-lingual transfer is more appropriate for this extremely challenging setting, with the best tagger achieving an accuracy of 72.9%. With an applied active learning scheme, which we use to collect sentence-level annotations over the test set, we achieve improvements of more than 21 percentage points.