Corpus ID: 207847397

Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation

@article{Bogoychev2019DomainTA,
  title={Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation},
  author={Nikolay Bogoychev and Rico Sennrich},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.03362}
}
  • Nikolay Bogoychev, Rico Sennrich
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • The quality of neural machine translation can be improved by leveraging additional monolingual resources to create synthetic training data. Source-side monolingual data can be (forward-)translated into the target language for self-training; target-side monolingual data can be back-translated. It has been widely reported that back-translation delivers superior results, but could this be due to artefacts in the test sets? We perform a case study using French-English news translation task and… CONTINUE READING

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