  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.
