The groundwork approach in this aspect is Neural Machine Translation (NMT), the corresponding research of which suggests that fine-tuning of the NMT model trained on out-of-domain data using a small in-domain parallel corpus boosts performance. However, NMT - as opposed to phrase-based MT - rarely incorporates translation memory, and so it is inherently harder for NMT models to adapt using active or interactive learning. Thus, CL for NMT is essential.