Checks and Strategies for Enabling Code-Switched Machine Translation
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Abstract (arXiv)
Code-switching is a common phenomenon among multilingual speakers, where alternation between two or more languages occurs within the context of a single conversation. While multilingual humans can seamlessly switch back and forth between languages, multilingual neural machine translation (NMT) models are not robust to such sudden changes in input. This work explores multilingual NMT models' ability to handle code-switched text. First, we propose checks to measure switching capability. Second, we investigate simple and effective data augmentation methods that can enhance an NMT model's ability to support code-switching. Finally, by using a glass-box analysis of attention modules, we demonstrate the effectiveness of these methods in improving robustness.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 30 of its 47 references that are in the map. First posted on arXiv 11 Oct 2022.
Builds on
30 of 47 references.
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18 more
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3 papers.