@article{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",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.918/",
    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."
}