mlsanityData to 7 Oct 2026

The Effect of Alignment Objectives on Code-Switching Translation

arXiv 2309.05044 ↗First posted 10 Sep 2023cs.CLcited by 1

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Abstract (arXiv)

One of the things that need to change when it comes to machine translation is the models' ability to translate code-switching content, especially with the rise of social media and user-generated content. In this paper, we are proposing a way of training a single machine translation model that is able to translate monolingual sentences from one language to another, along with translating code-switched sentences to either language. This model can be considered a bilingual model in the human sense. For better use of parallel data, we generated synthetic code-switched (CSW) data along with an alignment loss on the encoder to align representations across languages. Using the WMT14 English-French (En-Fr) dataset, the trained model strongly outperforms bidirectional baselines on code-switched translation while maintaining quality for non-code-switched (monolingual) data.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 60 references that are in the map. First posted on arXiv 10 Sep 2023.

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36 of 60 references.

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  1. 2025Low-resource Machine Translation for Code-switched Kazakh-Russian Language Paircited by 3