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The rapid growth of machine translation (MT) systems has necessitatedcomprehensive studies to meta-evaluate evaluation metrics being used, whichenables a better selection of metrics that best reflect MT quality.Unfortunately, most of the research focuses on high-resource languages, mainlyEnglish, the observations for which may not always apply to other languages.Indian languages, having over a billion speakers, are linguistically differentfrom English, and to date, there has not been a systematic study of evaluatingMT systems from English into Indian languages. In this paper, we fill this gapby creating an MQM dataset consisting of 7000 fine-grained annotations,spanning 5 Indian languages and 7 MT systems, and use it to establishcorrelations between annotator scores and scores obtained using existingautomatic metrics. Our results show that pre-trained metrics, such as COMET,have the highest correlations with annotator scores. Additionally, we find thatthe metrics do not adequately capture fluency-based errors in Indian languages,and there is a need to develop metrics focused on Indian languages. We hopethat our dataset and analysis will help promote further research in this area.

IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation metrics for Indian Languages