Code-Switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation

Code-switching is about dealing with alternative languages in speech or text.It is partially speaker-dependent and domainrelated, 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 the proposed method on two corpora, and the result shows that the generated code-switching sentences improve the performance of code-switching language models.