  Codeswitching has become one of the most common occurrences across
multilingual speakers of the world, especially in countries like India which
encompasses around 23 official languages with the number of bilingual speakers
being around 300 million. The scarcity of Codeswitched data becomes a
bottleneck in the exploration of this domain with respect to various Natural
Language Processing (NLP) tasks. We thus present a novel algorithm which
harnesses the syntactic structure of English grammar to develop grammatically
sensible Codeswitched versions of English-Hindi, English-Marathi and
English-Kannada data. Apart from maintaining the grammatical sanity to a great
extent, our methodology also guarantees abundant generation of data from a
minuscule snapshot of given data. We use multiple datasets to showcase the
capabilities of our algorithm while at the same time we assess the quality of
generated Codeswitched data using some qualitative metrics along with providing
baseline results for couple of NLP tasks.
