XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics
For researchers evaluating multilingual translation systems, this work addresses the overlooked problem of cross-lingual scoring bias, offering a benchmark and a practical solution to improve metric fairness.
The paper introduces XQ-MEval, a dataset for benchmarking translation metrics across nine language pairs, and provides the first empirical evidence of cross-lingual scoring bias in automatic metrics. A normalization strategy is proposed that improves fairness and reliability, reducing bias by aligning score distributions across languages.
Automatic evaluation metrics are essential for building multilingual translation systems. The common practice of evaluating these systems is averaging metric scores across languages, yet this is suspicious since metrics may suffer from cross-lingual scoring bias, where translations of equal quality receive different scores across languages. This problem has not been systematically studied because no benchmark exists that provides parallel-quality instances across languages, and expert annotation is not realistic. In this work, we propose XQ-MEval, a semi-automatically built dataset covering nine translation directions, to benchmark translation metrics. Specifically, we inject MQM-defined errors into gold translations automatically, filter them by native speakers for reliability, and merge errors to generate pseudo translations with controllable quality. These pseudo translations are then paired with corresponding sources and references to form triplets used in assessing the qualities of translation metrics. Using XQ-MEval, our experiments on nine representative metrics reveal the inconsistency between averaging and human judgment and provide the first empirical evidence of cross-lingual scoring bias. Finally, we propose a normalization strategy derived from XQ-MEval that aligns score distributions across languages, improving the fairness and reliability of multilingual metric evaluation.