Abstract
We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios. Unsuccessfully. In the end, we submitted two runs: (i) a standard phrase-based model, and (ii) a random babbling baseline using character trigrams. We found that it was surprisingly hard to beat (i), in spite of this model being, in theory, a bad fit for polysynthetic languages; and more interestingly, that (ii) was better than several of the submitted systems, highlighting how difficult low-resource machine translation for polysynthetic languages is.
Originalsprog | Engelsk |
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Titel | Proceedings of the 1st Workshop on Natural Language Processing for Indigenous Languages of the Americas, AmericasNLP 2021 |
Redaktører | Manuel Mager, Arturo Oncevay, Annette Rios, Ivan Vladimir Meza Ruiz, Alexis Palmer, Graham Neubig, Katharina Kann |
Forlag | Association for Computational Linguistics |
Publikationsdato | 2021 |
Sider | 248-254 |
ISBN (Elektronisk) | 9781954085442 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 1st Workshop on Natural Language Processing for Indigenous Languages of the Americas, AmericasNLP 2021 - Virtual, Online Varighed: 11 jun. 2021 → … |
Konference
Konference | 1st Workshop on Natural Language Processing for Indigenous Languages of the Americas, AmericasNLP 2021 |
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By | Virtual, Online |
Periode | 11/06/2021 → … |
Bibliografisk note
Publisher Copyright:© 2021 Association for Computational Linguistics