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        <title>Regularization techniques for fine-tuning in neural machine translation</title>
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    <name type="personal">
        <namePart type="given">Antonio</namePart>
        <namePart type="given">Valerio</namePart>
        <namePart type="family">Miceli Barone</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>We investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset. In this scenario, overfitting is a major challenge. We investigate a number of techniques to reduce overfitting and improve transfer learning, including regularization techniques such as dropout and L2-regularization towards an out-of-domain prior. In addition, we introduce tuneout, a novel regularization technique inspired by dropout. We apply these techniques, alone and in combination, to neural machine translation, obtaining improvements on IWSLT datasets for English→German and English→Russian. We also investigate the amounts of in-domain training data needed for domain adaptation in NMT, and find a logarithmic relationship between the amount of training data and gain in BLEU score.</abstract>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T Regularization techniques for fine-tuning in neural machine translation
%A Miceli Barone, Antonio Valerio
%A Haddow, Barry
%A Germann, Ulrich
%A Sennrich, Rico
%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 miceli-barone-etal-2017-regularization
%X We investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset. In this scenario, overfitting is a major challenge. We investigate a number of techniques to reduce overfitting and improve transfer learning, including regularization techniques such as dropout and L2-regularization towards an out-of-domain prior. In addition, we introduce tuneout, a novel regularization technique inspired by dropout. We apply these techniques, alone and in combination, to neural machine translation, obtaining improvements on IWSLT datasets for English→German and English→Russian. We also investigate the amounts of in-domain training data needed for domain adaptation in NMT, and find a logarithmic relationship between the amount of training data and gain in BLEU score.
%R 10.18653/v1/D17-1156
%U https://aclanthology.org/D17-1156
%U https://doi.org/10.18653/v1/D17-1156
%P 1489-1494

Markdown (Informal)

[Regularization techniques for fine-tuning in neural machine translation](https://aclanthology.org/D17-1156) (Miceli Barone et al., EMNLP 2017)

• Regularization techniques for fine-tuning in neural machine translation (Miceli Barone et al., EMNLP 2017)

ACL

• Antonio Valerio Miceli Barone, Barry Haddow, Ulrich Germann, and Rico Sennrich. 2017. Regularization techniques for fine-tuning in neural machine translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1489–1494, Copenhagen, Denmark. Association for Computational Linguistics.