  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
"co-curricular learning" 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
"co-curriculum". Experiment results and analysis with two domains demonstrate
the effectiveness of the method and the properties of data scheduled by the
co-curriculum.
