Abstract
This paper introduces Vorm, an unsupervised morphological segmentation system, leveraging translation data to infer highly accurate morphological transformations, including less-frequently modeled processes such as infixation and reduplication. The system is evaluated on standard benchmark data and a novel, typologically diverse, dataset of 37 languages. Model performance is competitive and sometimes superior on canonical segmentation, but more limited on surface segmentation.- Anthology ID:
- 2025.conll-1.39
- Volume:
- Proceedings of the 29th Conference on Computational Natural Language Learning
- Month:
- July
- Year:
- 2025
- Address:
- Vienna, Austria
- Editors:
- Gemma Boleda, Michael Roth
- Venues:
- CoNLL | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 602–626
- Language:
- URL:
- https://preview.aclanthology.org/tt-tag/2025.conll-1.39/
- DOI:
- 10.18653/v1/2025.conll-1.39
- Cite (ACL):
- Barend Beekhuizen. 2025. Vorm: Translations and a constrained hypothesis space support unsupervised morphological segmentation across languages. In Proceedings of the 29th Conference on Computational Natural Language Learning, pages 602–626, Vienna, Austria. Association for Computational Linguistics.
- Cite (Informal):
- Vorm: Translations and a constrained hypothesis space support unsupervised morphological segmentation across languages (Beekhuizen, CoNLL 2025)
- PDF:
- https://preview.aclanthology.org/tt-tag/2025.conll-1.39.pdf