@inproceedings{wang-etal-2019-dynamically,
    title = "Dynamically Composing Domain-Data Selection with Clean-Data Selection by {\textquotedblleft}Co-Curricular Learning{\textquotedblright} for Neural Machine Translation",
    author = "Wang, Wei  and
      Caswell, Isaac  and
      Chelba, Ciprian",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P19-1123/",
    doi = "10.18653/v1/P19-1123",
    pages = "1282--1292",
    abstract = "Noise and domain are important aspects of data quality for neural machine translation. Existing research focus separately on domain-data selection, clean-data selection, or their static combination, leaving the dynamic interaction across them not explicitly examined. This paper introduces a {\textquotedblleft}co-curricular learning{\textquotedblright} method to compose dynamic domain-data selection with dynamic clean-data selection, for transfer learning across both capabilities. We apply an EM-style optimization procedure to further refine the {\textquotedblleft}co-curriculum{\textquotedblright}. Experiment results and analysis with two domains demonstrate the effectiveness of the method and the properties of data scheduled by the co-curriculum."
}