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As machine translation (MT) metrics improve their correlation with humanjudgement every year, it is crucial to understand the limitations of suchmetrics at the segment level. Specifically, it is important to investigatemetric behaviour when facing accuracy errors in MT because these can havedangerous consequences in certain contexts (e.g., legal, medical). We curateACES, a translation accuracy challenge set, consisting of 68 phenomena rangingfrom simple perturbations at the word/character level to more complex errorsbased on discourse and real-world knowledge. We use ACES to evaluate a widerange of MT metrics including the submissions to the WMT 2022 metrics sharedtask and perform several analyses leading to general recommendations for metricdevelopers. We recommend: a) combining metrics with different strengths, b)developing metrics that give more weight to the source and less tosurface-level overlap with the reference and c) explicitly modelling additionallanguage-specific information beyond what is available via multilingualembeddings.

ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics