Morphological reinflection with convolutional neural networks

Abstract

We present a system for morphological reinflection based on an encoder-decoder neural network model with extra convolutional layers. In spite of its simplicity, the method performs reasonably well on all the languages of the SIGMORPHON 2016 shared task, particularly for the most challenging problem of limited-resources reinflection (track 2, task 3). We also find that using only convolution achieves surprisingly good results in this task, surpassing the accuracy of our encoder-decoder model for several languages.