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Noise and domain are important aspects of data quality for neural machinetranslation. Existing research focus separately on domain-data selection,clean-data selection, or their static combination, leaving the dynamicinteraction across them not explicitly examined. This paper introduces a"co-curricular learning" method to compose dynamic domain-data selection withdynamic 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 demonstratethe effectiveness of the method and the properties of data scheduled by theco-curriculum.

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