Dynamically Composing Domain-Data Selection with Clean-Data
Selection by “Co-Curricular Learning” for Neural Machine Translation
Abstract
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.