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        <title>Evaluation Framework for Transfer Learning between Closely Related Lects: A Case Study of Lemko</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Evaluation Framework for Transfer Learning between Closely Related Lects: A Case Study of Lemko
%A Afanasev, Ilia
%S Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F afanasev-2026-evaluation
%X 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.
%U https://aclanthology.org/2026.vardial-1.25/
%P 304-316

Markdown (Informal)

[Evaluation Framework for Transfer Learning between Closely Related Lects: A Case Study of Lemko](https://aclanthology.org/2026.vardial-1.25/) (Afanasev, VarDial 2026)

• Evaluation Framework for Transfer Learning between Closely Related Lects: A Case Study of Lemko (Afanasev, VarDial 2026)

ACL

• Ilia Afanasev. 2026. Evaluation Framework for Transfer Learning between Closely Related Lects: A Case Study of Lemko. In Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 304–316, Rabat, Morocco. Association for Computational Linguistics.