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

Katharina Kann, Manuel Mager, Ivan Meza, Hinrich Schütze


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.



You are welcome to use the code and datasets, however please acknowledge its use with a citation:
    title = "Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages",
    author = {Kann, Katharina  and
    Mager Hois, Jesus Manuel  and
    Meza Ruiz, Ivan Vladimir  and
    Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "",
    doi = "10.18653/v1/N18-1005",
    pages = "47--57",