An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation

Understand

In this paper, we propose a novel domain adaptation method named "mixed fine tuning" for neural machine translation (NMT).

• We combine two existing approaches namely fine tuning and multi domain NMT.
• We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora.
• All corpora are augmented with artificial tags to indicate specific domains.