DSpace logo

Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16384
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSharma, Yashvardhan-
dc.date.accessioned2024-11-14T10:56:28Z-
dc.date.available2024-11-14T10:56:28Z-
dc.date.issued2021-
dc.identifier.urihttps://biblio.ugent.be/publication/8709864/file/8719511.pdf-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16384-
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.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
Appears in Collections:Department of Computer Science and Information Systems

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.