@inproceedings{wuebker-etal-2018-compact,
    title = "Compact Personalized Models for Neural Machine Translation",
    author = "Wuebker, Joern  and
      Simianer, Patrick  and
      DeNero, John",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D18-1104/",
    doi = "10.18653/v1/D18-1104",
    pages = "881--886",
    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."
}