A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages

Clara Vania, Yova Kementchedjhieva, Anders Søgaard, Adam Lopez
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
2019). A systematic comparison of methods for lowresource dependency parsing on genuinely low-resource languages. In Abstract Parsers are available for only a handful of the world's languages, since they require lots of training data. How far can we get with just a small amount of training data? We systematically compare a set of simple strategies for improving low-resource parsers: data augmentation, which has not been tested before; cross-lingual training; and transliteration. Experimenting
more » ... three typologically diverse low-resource languages-North Sámi, Galician, and Kazah-We find that (1) when only the low-resource treebank is available, data augmentation is very helpful; (2) when a related high-resource treebank is available, cross-lingual training is helpful and complements data augmentation; and (3) when the high-resource treebank uses a different writing system, transliteration into a shared orthographic spaces is also very helpful.
doi:10.18653/v1/d19-1102 dblp:conf/emnlp/VaniaKSL19 fatcat:w6tt4bygofefxbtgdepnoktfku