Sicilian Translator: A Recipe for Low-Resource NMT
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Abstract (arXiv)
With 17,000 pairs of Sicilian-English translated sentences, Arba Sicula developed the first neural machine translator for the Sicilian language. Using small subword vocabularies, we trained small Transformer models with high dropout parameters and achieved BLEU scores in the upper 20s. Then we supplemented our dataset with backtranslation and multilingual translation and pushed our scores into the mid 30s. We also attribute our success to incorporating theoretical information in our dataset. Prior to training, we biased the subword vocabulary towards the desinences one finds in a textbook. And we included textbook exercises in our dataset.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 5 Oct 2021.
Builds on
11 of 15 references.
References · 11
- 2017Attention Is All You Needcited by 60,361
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
- 2020Beyond English-Centric Multilingual Machine Translationcited by 561
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- 2020Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translationcited by 277
- 2019Massively Multilingual Neural Machine Translation in the Wild: Findings and Challengescited by 257
- 2019Investigating Multilingual NMT Representations at Scalecited by 68
- 2019Revisiting Low-Resource Neural Machine Translation: A Case Studycited by 61
- 2017Sockeye: A Toolkit for Neural Machine Translationcited by 15
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Citing papers per year: 2023: 1.
1 paper.