Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding

Arturs Znotins

doi:10.63317/5khpzsaiqrzw

Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding

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Abstract

We present a systematic evaluation of Latvian morphosyntactic parsing with pretrained transformer encoders in a unified joint architecture for tagging, lemmatization, and dependency parsing. We benchmark multilingual and Latvian-specific models and show that language-specific adaptation, even with modest in-language data, substantially improves performance. We further demonstrate that factored morphological modeling improves robustness and that integrating a Latvian morphological analyzer through constrained decoding yields consistent gains in XPOS tagging and lemmatization. The best system achieves new state-of-the-art results, reaching 95.22% XPOS accuracy, 98.72% lemma accuracy, and 93.19% LAS.

Anthology ID:: 2026.lrec-1.918
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 11724–11734
Language:
External URL:: https://lrec.elra.info/lrec2026-main-918
DOI:: 10.63317/5khpzsaiqrzw
Bibkey:
Cite (ACL):: Arturs Znotins. 2026. Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 11724–11734, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding (Znotins, LREC 2026)
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@inproceedings{znotins-2026-improving,
    title = "Improving {L}atvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding",
    author = "Znotins, Arturs",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.918/",
    doi = "10.63317/5khpzsaiqrzw",
    pages = "11724--11734",
    abstract = "We present a systematic evaluation of Latvian morphosyntactic parsing with pretrained transformer encoders in a unified joint architecture for tagging, lemmatization, and dependency parsing. We benchmark multilingual and Latvian-specific models and show that language-specific adaptation, even with modest in-language data, substantially improves performance. We further demonstrate that factored morphological modeling improves robustness and that integrating a Latvian morphological analyzer through constrained decoding yields consistent gains in XPOS tagging and lemmatization. The best system achieves new state-of-the-art results, reaching 95.22{\%} XPOS accuracy, 98.72{\%} lemma accuracy, and 93.19{\%} LAS."
}

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%0 Conference Proceedings
%T Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding
%A Znotins, Arturs
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F znotins-2026-improving
%X We present a systematic evaluation of Latvian morphosyntactic parsing with pretrained transformer encoders in a unified joint architecture for tagging, lemmatization, and dependency parsing. We benchmark multilingual and Latvian-specific models and show that language-specific adaptation, even with modest in-language data, substantially improves performance. We further demonstrate that factored morphological modeling improves robustness and that integrating a Latvian morphological analyzer through constrained decoding yields consistent gains in XPOS tagging and lemmatization. The best system achieves new state-of-the-art results, reaching 95.22% XPOS accuracy, 98.72% lemma accuracy, and 93.19% LAS.
%R 10.63317/5khpzsaiqrzw
%U https://aclanthology.org/2026.lrec-1.918/
%U https://doi.org/10.63317/5khpzsaiqrzw
%P 11724-11734

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Markdown (Informal)

[Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding](https://aclanthology.org/2026.lrec-1.918/) (Znotins, LREC 2026)

Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding (Znotins, LREC 2026)

ACL

Arturs Znotins. 2026. Improving Latvian Morphosyntactic Parsing with Pretrained Encoders and Analyzer-Constrained Decoding. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 11724–11734, Palma de Mallorca, Spain. ELRA Language Resource Association.