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Code-mixing is a phenomenon of mixing words and phrases from two or morelanguages in a single utterance of speech and text. Due to the high linguisticdiversity, code-mixing presents several challenges in evaluating standardnatural language generation (NLG) tasks. Various widely popular metrics performpoorly with the code-mixed NLG tasks. To address this challenge, we present ametric independent evaluation pipeline MIPE that significantly improves thecorrelation between evaluation metrics and human judgments on the generatedcode-mixed text. As a use case, we demonstrate the performance of MIPE on themachine-generated Hinglish (code-mixing of Hindi and English languages)sentences from the HinGE corpus. We can extend the proposed evaluation strategyto other code-mixed language pairs, NLG tasks, and evaluation metrics withminimal to no effort.

MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation