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

2021-07-24 05:24:26
Ayush Garg, Sammed S Kagi, Vivek Srivastava, Mayank Singh
arXiv_CL
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2107.11534

PDF

https://arxiv.org/pdf/2107.11534.pdf

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