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        <title>Instance Weighting for Neural Machine Translation Domain Adaptation</title>
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    <abstract>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 1.6-3.6 BLEU points.</abstract>
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%0 Conference Proceedings
%T Instance Weighting for Neural Machine Translation Domain Adaptation
%A Wang, Rui
%A Utiyama, Masao
%A Liu, Lemao
%A Chen, Kehai
%A Sumita, Eiichiro
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-instance
%X 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 1.6-3.6 BLEU points.
%R 10.18653/v1/D17-1155
%U https://aclanthology.org/D17-1155
%U https://doi.org/10.18653/v1/D17-1155
%P 1482-1488

Markdown (Informal)

[Instance Weighting for Neural Machine Translation Domain Adaptation](https://aclanthology.org/D17-1155) (Wang et al., EMNLP 2017)

• Instance Weighting for Neural Machine Translation Domain Adaptation (Wang et al., EMNLP 2017)

ACL

• Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, and Eiichiro Sumita. 2017. Instance Weighting for Neural Machine Translation Domain Adaptation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1482–1488, Copenhagen, Denmark. Association for Computational Linguistics.