  Code-switching is about dealing with alternative languages in speech or text.
It is partially speaker-depend and domain-related, so completely explaining the
phenomenon by linguistic rules is challenging. Compared to most monolingual
tasks, insufficient data is an issue for code-switching. To mitigate the issue
without expensive human annotation, we proposed an unsupervised method for
code-switching data augmentation. By utilizing a generative adversarial
network, we can generate intra-sentential code-switching sentences from
monolingual sentences. We applied proposed method on two corpora, and the
result shows that the generated code-switching sentences improve the
performance of code-switching language models.
