  Code-mixing is a phenomenon of mixing words and phrases from two or more
languages in a single utterance of speech and text. Due to the high linguistic
diversity, code-mixing presents several challenges in evaluating standard
natural language generation (NLG) tasks. Various widely popular metrics perform
poorly with the code-mixed NLG tasks. To address this challenge, we present a
metric independent evaluation pipeline MIPE that significantly improves the
correlation between evaluation metrics and human judgments on the generated
code-mixed text. As a use case, we demonstrate the performance of MIPE on the
machine-generated Hinglish (code-mixing of Hindi and English languages)
sentences from the HinGE corpus. We can extend the proposed evaluation strategy
to other code-mixed language pairs, NLG tasks, and evaluation metrics with
minimal to no effort.
