Open machine translation for low resource South American languages (AmericasNLP 2021 shared task contribution)

dc.contributor.authorSharma, Yashvardhan
dc.date.accessioned2024-11-14T10:56:28Z
dc.date.available2024-11-14T10:56:28Z
dc.date.issued2021
dc.description.abstractThis paper describes the team ("Tamalli")’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs “Spanish-Bribri", “Spanish-Asháninka", and “Spanish-Rarámuri" in the category “Development set not used for training". Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.en_US
dc.identifier.urihttps://biblio.ugent.be/publication/8709864/file/8719511.pdf
dc.identifier.urihttps://dspace.bits-pilani.ac.in/handle/123456789/16384
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguistics (ACL)en_US
dc.subjectComputer Scienceen_US
dc.subjectMachine Translation (MT)en_US
dc.subjectNeural Machine Translation (NMT)en_US
dc.titleOpen machine translation for low resource South American languages (AmericasNLP 2021 shared task contribution)en_US
dc.typeArticleen_US

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