Abstract
The University of Helsinki participated in the AmericasNLP shared task for all ten language pairs. Our multilingual NMT models reached the first rank on all language pairs in track 1, and first rank on nine out of ten language pairs in track 2. We focused our efforts on three aspects: (1) the collection of additional data from various sources such as Bibles and political constitutions, (2) the cleaning and filtering of training data with the OpusFilter toolkit, and (3) different multilingual training techniques enabled by the latest version of the OpenNMT-py toolkit to make the most efficient use of the scarce data. This paper describes our efforts in detail.
Cite
CITATION STYLE
Vázquez, R., Scherrer, Y., Virpioja, S., & Tiedemann, J. (2021). The Helsinki submission to the AmericasNLP shared task. In Proceedings of the 1st Workshop on Natural Language Processing for Indigenous Languages of the Americas, AmericasNLP 2021 (pp. 255–264). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.americasnlp-1.29
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