  Extending semantic parsers to code-switched input has been a challenging
problem, primarily due to a lack of supervised training data. In this work, we
introduce CST5, a new data augmentation technique that finetunes a T5 model
using a small seed set ($\approx$100 utterances) to generate code-switched
utterances from English utterances. We show that CST5 generates high quality
code-switched data, both intrinsically (per human evaluation) and extrinsically
by comparing baseline models which are trained without data augmentation to
models which are trained with augmented data. Empirically we observe that using
CST5, one can achieve the same semantic parsing performance by using up to 20x
less labeled data. To aid further research in this area, we are also releasing
(a) Hinglish-TOP, the largest human annotated code-switched semantic parsing
dataset to date, containing 10k human annotated Hindi-English (Hinglish)
code-switched utterances, and (b) Over 170K CST5 generated code-switched
utterances from the TOPv2 dataset. Human evaluation shows that both the human
annotated data as well as the CST5 generated data is of good quality.
