Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation
Wei Wang, Isaac Rayburn Caswell, Ciprian I. Chelba · 2019
Noise and domain are important aspects of data quality for neural machine translation.Existing research focus separately on domaindata 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 cocurriculum.