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        <title>Compact Personalized Models for Neural Machine Translation</title>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture–combining a state-of-the-art self-attentive model with compact domain adaptation–provides high quality personalized machine translation that is both space and time efficient.</abstract>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Compact Personalized Models for Neural Machine Translation
%A Wuebker, Joern
%A Simianer, Patrick
%A DeNero, John
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wuebker-etal-2018-compact
%X We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture–combining a state-of-the-art self-attentive model with compact domain adaptation–provides high quality personalized machine translation that is both space and time efficient.
%R 10.18653/v1/D18-1104
%U https://aclanthology.org/D18-1104
%U https://doi.org/10.18653/v1/D18-1104
%P 881-886

Markdown (Informal)

[Compact Personalized Models for Neural Machine Translation](https://aclanthology.org/D18-1104) (Wuebker et al., EMNLP 2018)

• Compact Personalized Models for Neural Machine Translation (Wuebker et al., EMNLP 2018)

ACL

• Joern Wuebker, Patrick Simianer, and John DeNero. 2018. Compact Personalized Models for Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 881–886, Brussels, Belgium. Association for Computational Linguistics.