AI RESEARCH
Model-Based Quality Assessment for Massively Multilingual Parallel Data
arXiv CS.CL
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ArXi:2606.00285v1 Announce Type: new 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.