  Machine translation models struggle when translating out-of-domain text,
which makes domain adaptation a topic of critical importance. However, most
domain adaptation methods focus on fine-tuning or training the entire or part
of the model on every new domain, which can be costly. On the other hand,
semi-parametric models have been shown to successfully perform domain
adaptation by retrieving examples from an in-domain datastore (Khandelwal et
al., 2021). A drawback of these retrieval-augmented models, however, is that
they tend to be substantially slower. In this paper, we explore several
approaches to speed up nearest neighbor machine translation. We adapt the
methods recently proposed by He et al. (2021) for language modeling, and
introduce a simple but effective caching strategy that avoids performing
retrieval when similar contexts have been seen before. Translation quality and
runtimes for several domains show the effectiveness of the proposed solutions.
