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Checks and Strategies for Enabling Code-Switched Machine Translation

2022-10-11 02:25:21
Thamme Gowda, Mozhdeh Gheini, Jonathan May
arXiv_CL
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.05096

PDF

https://arxiv.org/pdf/2210.05096.pdf

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