# 大规模多语言平行数据的模型化质量评估

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-29 08:00
- AIHOT 分数：60
- AIHOT 链接：https://aihot.virxact.com/items/cmpwt12lv00fqsl795z6xkaxe
- 原文链接：https://arxiv.org/abs/2606.00285

## AI 摘要

针对大规模多语言平行数据中存在的非平行句对与低质量翻译问题，该研究将模型化评估分解为两个部分。一是基于多语言嵌入向量的平行性评估，在FLORES-200和BOUQuET任务上对四个嵌入模型进行了基准测试，覆盖6,654个源-目标方向。二是无参考质量评估，在FLORES-200的专业翻译上评估了九个无参考评估器，覆盖41,412个有序方向。结果显示，没有模型在所有翻译方向上都可靠，简单的质量评估集成会稀释强模型的信号，而文档化的目标语言覆盖率与更高的质量评估分数密切相关。这些发现表明，该问题最好被视为一个方向感知的路由和校准问题。

## 正文

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.