Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting

• URL: http://arxiv.org/abs/2310.07081v2
• Date: Fri, 20 Oct 2023 19:24:14 GMT
• Title: Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting
• Authors: Emmy Liu, Aditi Chaudhary, Graham Neubig
• Abstract summary: We provide a simple characterization of idiomatic translation and related issues. We conduct a synthetic experiment revealing a tipping point at which transformer-based machine translation models correctly default to idiomatic translations. To improve translation of natural idioms, we introduce two straightforward yet effective techniques.
• Score: 66.02718577386426
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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