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