Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages

Katharina Kann, Manuel Mager, Ivan Meza, Hinrich Schütze

Abstract

Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approaches -one with, one without need for external unlabeled resources-, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75%. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research.

Resources

• Paper [arXiv, local]
• Data set [language_data.tar.bz2]
• Sorce Code [med.zip]
• Presentation Video [external link]

Papers that use this dataset

• Eskander, R., Klavans, J. & Muresan S. (2019) Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages. In Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology (pp. 189–195).
• Sorokin, A. (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 (pp. 154–159).1

Cite

You are welcome to use the code and datasets, however please acknowledge its use with a citation: