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

Katharina Kann, Jesus Manuel Mager Hois, Ivan Vladimir Meza-Ruiz, 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.
Anthology ID:
N18-1005
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
47–57
Language:
URL:
https://aclanthology.org/N18-1005
DOI:
10.18653/v1/N18-1005
Bibkey:
Cite (ACL):
Katharina Kann, Jesus Manuel Mager Hois, Ivan Vladimir Meza-Ruiz, and Hinrich Schütze. 2018. Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 47–57, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages (Kann et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1005.pdf
Video:
 https://aclanthology.org/N18-1005.mp4