  The development of deep learning techniques has allowed Neural Machine
Translation (NMT) models to become extremely powerful, given sufficient
training data and training time. However, systems struggle when translating
text from a new domain with a distinct style or vocabulary. Tuning on a
representative training corpus allows good in-domain translation, but such
data-centric approaches can cause over-fitting to new data and `catastrophic
forgetting' of previously learned behaviour.
  We concentrate on more robust approaches to domain adaptation for NMT,
particularly the case where a system may need to translate sentences from
multiple domains. We divide techniques into those relating to data selection,
model architecture, parameter adaptation procedure, and inference procedure. We
finally highlight the benefits of domain adaptation and multi-domain adaptation
techniques to other lines of NMT research.
