In NMT fine-tuning, a model is first trained on a task A, typically translating a large generaldomain corpus (Luong and Manning, 2015).
eWC outperforms no-reg and L2 on News, not only reducing forgetting but giving 0.5 BLeU improvement over the baseline News model.