  Machine translation (MT) models used in industries with constantly changing
topics, such as translation or news agencies, need to adapt to new data to
maintain their performance over time. Our aim is to teach a pre-trained MT
model to translate previously unseen words accurately, based on very few
examples. We propose (i) an experimental setup allowing us to simulate novel
vocabulary appearing in human-submitted translations, and (ii) corresponding
evaluation metrics to compare our approaches. We extend a data augmentation
approach using a pre-trained language model to create training examples with
similar contexts for novel words. We compare different fine-tuning and data
augmentation approaches and show that adaptation on the scale of one to five
examples is possible. Combining data augmentation with randomly selected
training sentences leads to the highest BLEU score and accuracy improvements.
Impressively, with only 1 to 5 examples, our model reports better accuracy
scores than a reference system trained with on average 313 parallel examples.
