Tackling neural machine translation in low-resource settings: a Portuguese case study

  • Arthur T. Estrella UFRJ
  • João B. O. Souza Filho UFRJ


Neural machine translation (NMT) nowadays requires an increasing amount of data and computational power, so succeeding in this task with limited data and using a single GPU might be challenging. Strategies such as the use of pre-trained word embeddings, subword embeddings, and data augmentation solutions can potentially address some issues faced in low-resource experimental settings, but their impact on the quality of translations is unclear. This work evaluates some of these strategies on two low-resource experiments beyond just reporting BLEU: errors are categorized on the Portuguese-English pair with the help of a translator, considering semantic and syntactic aspects. The BPE subword approach has shown to be the most effective solution, allowing a BLEU increase of 59% p.p. compared to the standard Transformer.


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ESTRELLA, Arthur T.; SOUZA FILHO, João B. O.. Tackling neural machine translation in low-resource settings: a Portuguese case study. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 275-282. DOI: https://doi.org/10.5753/stil.2021.17807.