Google Research

Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection

  • Wei Wang
  • Taro Watanabe
  • Macduff Hughes
  • Tetsuji Nakagawa
  • Ciprian Chelba
Third Conference on Machine Translation (WMT18) (2018)


Measuring domain relevance of data and identifying or selecting well-fit domain data for machine translation (MT) is a well-studied topic, but denoising is not yet. Denoising is concerned with a different type of data quality and tries to reduce the negative impact of data noise on MT training, in particular, neural MT (NMT) training. This paper generalizes methods for measuring and selecting data for domain MT and applies them to denoising NMT training. The proposed approach uses trusted data and a denoising curriculum realized by online data selection. Intrinsic and extrinsic evaluations of the approach show its significant effectiveness for NMT to train on data with severe noise.

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work