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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sharma, Yashvardhan | - |
dc.date.accessioned | 2024-11-14T10:56:28Z | - |
dc.date.available | 2024-11-14T10:56:28Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | https://biblio.ugent.be/publication/8709864/file/8719511.pdf | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16384 | - |
dc.description.abstract | This 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.iso | en | en_US |
dc.publisher | Association for Computational Linguistics (ACL) | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Machine Translation (MT) | en_US |
dc.subject | Neural Machine Translation (NMT) | en_US |
dc.title | Open machine translation for low resource South American languages (AmericasNLP 2021 shared task contribution) | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Computer Science and Information Systems |
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