@inproceedings{qin-etal-2017-evolution,
    title = "Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems",
    author = "Qin, Hao  and
      Shinozaki, Takahiro  and
      Duh, Kevin",
    editor = "Sakti, Sakriani  and
      Utiyama, Masao",
    booktitle = "Proceedings of the 14th International Conference on Spoken Language Translation",
    month = dec # " 14-15",
    year = "2017",
    address = "Tokyo, Japan",
    publisher = "International Workshop on Spoken Language Translation",
    url = "https://preview.aclanthology.org/fix-sig-urls/2017.iwslt-1.17/",
    pages = "120--128",
    abstract = "Neural machine translation (NMT) systems have demonstrated promising results in recent years. However, non-trivial amounts of manual effort are required for tuning network architectures, training configurations, and pre-processing settings such as byte pair encoding (BPE). In this study, we propose an evolution strategy based automatic tuning method for NMT. In particular, we apply the covariance matrix adaptation-evolution strategy (CMA-ES), and investigate a Pareto-based multi-objective CMA-ES to optimize the translation performance and computational time jointly. Experimental results show that the proposed method automatically finds NMT systems that outperform the initial manual setting."
}