Abstract
We apply convolutional neural networks to the task of shallow morpheme segmentation using low-resource datasets for 5 different languages. We show that both in fully supervised and semi-supervised settings our model beats previous state-of-the-art approaches. We argue that convolutional neural networks reflect local nature of morpheme segmentation better than other semi-supervised approaches.- Anthology ID:
- W19-4218
- Volume:
- Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Garrett Nicolai, Ryan Cotterell
- Venue:
- ACL
- SIG:
- SIGMORPHON
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 154–159
- Language:
- URL:
- https://aclanthology.org/W19-4218
- DOI:
- 10.18653/v1/W19-4218
- Cite (ACL):
- Alexey Sorokin. 2019. Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?. In Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 154–159, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art? (Sorokin, ACL 2019)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W19-4218.pdf