  Retrieval-augmented machine translation leverages examples from a translation
memory by retrieving similar instances. These examples are used to condition
the predictions of a neural decoder. We aim to improve the upstream retrieval
step and consider a fixed downstream edit-based model: the multi-Levenshtein
Transformer. The task consists of finding a set of examples that maximizes the
overall coverage of the source sentence. To this end, we rely on the theory of
submodular functions and explore new algorithms to optimize this coverage. We
evaluate the resulting performance gains for the machine translation task.
