@inproceedings{afanasev-2026-evaluation,
    title = "Evaluation Framework for Transfer Learning between Closely Related Lects: A Case Study of Lemko",
    author = "Afanasev, Ilia",
    booktitle = "Proceedings of the 13th Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.vardial-1.25/",
    pages = "304--316",
    abstract = "The creation of a robust evaluation methodology is one of the pivotal issues for transfer learning between closely related lects. The current study proposes to resolve this issue by concisely implementing a group of evaluation methods that enable a more systematic qualitative analysis of errata (for instance, string similarity measures to assess lemmatisation more effectively). The paper introduces a robustness score, a metric that aims to assess the stabilityof model performance across different datasets. The case study is a morphosyntactic tagging of a small historical (beginning of the twentieth century) corpus of Lemko (Slavic clade, Transcarpathian area). It presents a diversity of cross-dependent tasks, made rather complex by the rich Lemko morphology, highly influenced by areal convergence processes. The tagger is a pre-trained Stanza. The study uses modern standard Ukrainian as the source language, as it is the closest to the Lemko high-resource lect. The analysis reveals that linguistically-aware metrics improve the speed and accuracy of analysis of the errata, especially those caused by the differences between source and target lects. The key data contribution is the open- source dataset of Lemko, obtained during the tagging tasks. Future research directions include a larger-scale test that applies more models to a more extensive material."
}