Correlation of RankC Metric with Zero-Shot QA Accuracy in Low-Resource Languages versus High-Resource Baselines in XLM-R

Abstract

Abstract: While machine translation evaluation has been studied primarily for high-resource languages, there has been a recent interest in evaluation for low-resource languages due to the increasing availability of data and models. In this paper, we focus on a zero-shot evaluation setting focusing on low-resource Indian languages, namely Assamese, Kannada, Maithili, and Punjabi. We collect sufficient Multi-Dimensional Quality Metrics (MQM) and Direct Assessment (DA) annotations to create test sets and meta-evaluate a plethora of automatic evaluation metrics. We observe that even for learned metrics, whi

Research Question

How does the RankC metric correlate with zero-shot question answering accuracy for low-resource languages in XLM-R compared to high-resource baselines?

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Quality Tier

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

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