Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation

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 viability of the method and the properties of data scheduled by the co-curriculum.

• Machine Intelligence

• Machine Translation