  Text generation is a highly active area of research in the computational
linguistic community. The evaluation of the generated text is a challenging
task and multiple theories and metrics have been proposed over the years.
Unfortunately, text generation and evaluation are relatively understudied due
to the scarcity of high-quality resources in code-mixed languages where the
words and phrases from multiple languages are mixed in a single utterance of
text and speech. To address this challenge, we present a corpus (HinGE) for a
widely popular code-mixed language Hinglish (code-mixing of Hindi and English
languages). HinGE has Hinglish sentences generated by humans as well as two
rule-based algorithms corresponding to the parallel Hindi-English sentences. In
addition, we demonstrate the inefficacy of widely-used evaluation metrics on
the code-mixed data. The HinGE dataset will facilitate the progress of natural
language generation research in code-mixed languages.
