Learning a Multi-Domain Curriculum for Neural Machine Translation
Wei Wang, Ye Tian, Jiquan Ngiam, Yinfei Yang, Isaac Rayburn Caswell, Zarana Parekh · 2020
Most data selection research in machine translation focuses on improving a single domain.We perform data selection for multiple domains at once.This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches.Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-ofdomain.In large-scale experiments, the multidomain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.