@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Automatic Grammatical Case Prediction for Template Filling in Case-Marking Languages: Implementation and Evaluation for Finnish
%A Laurmaa, Johannes
%Y Vylomova, Ekaterina
%Y Shcherbakov, Andrei
%Y Rani, Priya
%S Proceedings of the 8th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-374-6
%F laurmaa-2026-automatic
%X 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.
%U https://aclanthology.org/2026.sigtyp-main.1/
%P 1-11
Markdown (Informal)
[Automatic Grammatical Case Prediction for Template Filling in Case-Marking Languages: Implementation and Evaluation for Finnish](https://aclanthology.org/2026.sigtyp-main.1/) (Laurmaa, SIGTYP 2026)
ACL