The paper presents a method for parsing low-resource languages with very small training corpora using multilingual word embeddings and annotated corpora of larger languages. The study demonstrates that specific language combinations enable improved dependency parsing when compared to previous work, allowing for wider reuse of pre-existing resources when parsing low-resource languages. The study also explores the question of whether contemporary contact languages or genetically related languages would be the most fruitful starting point for multilingual parsing scenarios.
@InProceedings{LIM18.600, author = {KyungTae Lim and Niko Partanen and Thierry Poibeau}, title = "{Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper