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Model-Based Quality Assessment for Massively Multilingual Parallel Data

arXiv 2606.00285 ↗First posted 29 May 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 29 May 2026.

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References · 21

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  10. 2023Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Taskcited by 88
  11. 2023Batch Prompting: Efficient Inference with Large Language Model APIscited by 84
  12. 2018Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddingscited by 83
9 more
  1. 2020MLQE-PE: A Multilingual Quality Estimation and Post-Editing Datasetcited by 47
  2. 2025M-Prometheus: A Suite of Open Multilingual LLM Judgescited by 37
  3. 2018Filtering and Mining Parallel Data in a Joint Multilingual Spacecited by 32
  4. 2025BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translationcited by 21
  5. 2023There's no Data Like Better Data: Using QE Metrics for MT Data Filteringcited by 19
  6. 2020Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddingscited by 16
  7. 2024From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimationcited by 10
  8. 2025Remedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modelingcited by 9
  9. 2025TASER: Translation Assessment via Systematic Evaluation and Reasoningcited by 3

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