@inproceedings{laurmaa-2026-automatic,
    title = "Automatic Grammatical Case Prediction for Template Filling in Case-Marking Languages: Implementation and Evaluation for {F}innish",
    author = "Laurmaa, Johannes",
    editor = "Vylomova, Ekaterina  and
      Shcherbakov, Andrei  and
      Rani, Priya",
    booktitle = "Proceedings of the 8th Workshop on Research in Computational Linguistic Typology and Multilingual {NLP}",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ccl/2026.sigtyp-main.1/",
    pages = "1--11",
    ISBN = "979-8-89176-374-6",
    abstract = "Automatically generating grammatically correct sentences in case-marking languages is hard because nominal case inflection depends on context. In template-based generation, placeholders must be inflected to the right case before insertion, otherwise the result is ungrammatical. We formalise this case selection problem for template slots and present a practical, data-driven solution designed for morphologically rich, case-marking languages, and apply it to Finnish. We automatically derive training instances from raw text via morphological analysis, and fine-tune transformer encoders to predict a distribution over 14 grammatical cases, with and without lemma conditioning. The predicted case is then realized by a morphological generator at deployment. On a held-out test set in the lemma-conditioned setting, our model attains 89.1{\%} precision, 81.1{\%} recall, and 84.2{\%} F1, with recall@3 of 93.3{\%} (macro averages). The probability outputs support abstention and top-$k$- suggestion User Interfaces, enabling robust, lightweight template filling for production use in multiple domains, such as customer messaging. The pipeline assumes only access to raw text plus a morphological analyzer and generator, and can be applied to other languages with productive case systems."
}