  Idioms are common in everyday language, but often pose a challenge to
translators because their meanings do not follow from the meanings of their
parts. Despite significant advances, machine translation systems still struggle
to translate idiomatic expressions. We provide a simple characterization of
idiomatic translation and related issues. This allows us to conduct a synthetic
experiment revealing a tipping point at which transformer-based machine
translation models correctly default to idiomatic translations. To expand
multilingual resources, we compile a dataset of ~4k natural sentences
containing idiomatic expressions in French, Finnish, and Japanese. To improve
translation of natural idioms, we introduce two straightforward yet effective
techniques: the strategic upweighting of training loss on potentially idiomatic
sentences, and using retrieval-augmented models. This not only improves the
accuracy of a strong pretrained MT model on idiomatic sentences by up to 13% in
absolute accuracy, but also holds potential benefits for non-idiomatic
sentences.
