Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?
Alexey Sorokin · 2019
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.