Instance Weighting for Neural Machine Translation Domain Adaptation

Instance weighting has been widely applied to phrase-based machine translation domain adaptation.However, it is challenging to be applied to Neural Machine Translation (NMT) directly, because NMT is not a linear model.In this paper, two instance weighting technologies, i.e., sentence weighting and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation.Empirical results on the IWSLT English-German/French tasks show that the proposed methods can substantially improve NMT performance by up to 2.7-6.7 BLEU points, outperforming the existing baselines by up to 1.6-3.6BLEU points.