基于模型的大规模多语言平行数据质量评估
Model-Based Quality Assessment for Massively Multilingual Parallel Data
AI总结：
针对大规模多语言平行数据中存在的非平行句对和低质量翻译问题，提出将模型评估分解为平行性评估（使用多语言嵌入）和无参考质量估计两个独立组件，并通过实验发现没有模型在所有翻译方向上普遍可靠，建议采用方向感知的路由和校准方法。
摘要：
大规模多语言平行文本通常包含两个不同的问题：非平行句对和低质量翻译。我们将此类数据的基于模型评估分解为两个独立组件：使用多语言嵌入的平行性评估和无参考质量估计（QE）。对于平行性，我们在FLORES-200和BOUQuET检索任务上对四个嵌入模型进行了基准测试，覆盖了目标语言对清单中的6,654个源-目标方向。对于QE，我们在专业FLORES-200翻译上评估了九个无参考评估器，涵盖41,412个有序源-目标方向。结果表明，没有模型在所有翻译方向上普遍可靠。简单的QE集成会稀释强模型信号，而有文档记录的目标语言覆盖范围与更高的QE分数密切相关。总体而言，这些发现表明，多语言平行数据评估最好被视为一个方向感知的路由和校准问题，其中没有单一的通用指标预计在所有语言上都足够。
英文摘要：
Large-scale multilingual bitext often contains two distinct problems: non-parallel sentence pairs and low-quality translations. We decompose model-based assessment for such data into two independent components: parallelism assessment with multilingual embeddings and reference-free quality estimation (QE). For parallelism, we benchmark four embedding models on FLORES-200 and BOUQuET retrieval tasks, covering 6,654 source--target directions in our target language-pair inventory. For QE, we evaluate nine reference-free evaluators on professional FLORES-200 translations across 41,412 ordered source--target directions. Results show that no model is universally reliable across translation directions. Naive QE ensembles dilute strong model signals, while documented target-language coverage is strongly associated with higher QE scores. Overall, these findings suggest that multilingual parallel-data assessment is best approached as a direction-aware routing and calibration problem, where no single universal metric is expected to suffice across all languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
