  Generating code-switched text is a problem of growing interest, especially
given the scarcity of corpora containing large volumes of real code-switched
text. In this work, we adapt a state-of-the-art neural machine translation
model to generate Hindi-English code-switched sentences starting from
monolingual Hindi sentences. We outline a carefully designed curriculum of
pretraining steps, including the use of synthetic code-switched text, that
enable the model to generate high-quality code-switched text. Using text
generated from our model as data augmentation, we show significant reductions
in perplexity on a language modeling task, compared to using text from other
generative models of CS text. We also show improvements using our text for a
downstream code-switched natural language inference task. Our generated text is
further subjected to a rigorous evaluation using a human evaluation study and a
range of objective metrics, where we show performance comparable (and sometimes
even superior) to code-switched text obtained via crowd workers who are native
Hindi speakers.
