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.

机器翻译的一个挑战是正确翻译习语表达，本研究提供了对习语翻译及相关问题的简单描述，并通过实验找到了适合使用习语翻译的临界点。为了丰富多语种资源，我们编制了一个包含法语、芬兰语和日语中约 4 千个自然句子的数据集，同时引入了两种简单而有效的技术来改进自然习语的翻译，这不仅在习语句子的准确性上提高了强大预训练机器翻译模型达到了 13% 的绝对准确度，而且也对非习语句子存在潜在的好处。