Model-Based Quality Assessment for Massively Multilingual Parallel Data

Abdelaziz M. A. Ibrahim, Zihao Li, Jörg Tiedemann, Shaoxiong

Model-Based Quality Assessment for Massively Multilingual Parallel Data: 1 upvotes on Hugging Face Daily Papers, #58 of 72 papers on 2026-06-02. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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