We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.
REF use CL to adapt generic NMT models to a specific domain.
Curriculum Learning for Domain Adaptation in Neural Machine Translation
Proceedings of the 2019 Conference of the North
Proceedings of the 2019 Conference of the North
Computer Science