Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?

Alexey Sorokin
2019 Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology   unpublished
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 neural approaches.
doi:10.18653/v1/w19-4218 fatcat:kw6zey7ahvbwpebs5xh35xwrk4