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This study evaluates multilingual sentence embeddings for distinguishing correct English–Greek translations from erroneous ones, finding that embeddings provide useful semantic signals but are better integrated into broader translation evaluation frameworks.
This study uses multilingual encoders to measure framing differences across Wikipedia's language editions, finding that religious and political concepts diverge more than scientific ones.
This paper introduces Task-Conditional Flow Matching (TCFM), a framework for adapting multilingual text embedding models that selectively uses flow matching for translation tasks and other objectives for retrieval/classification. It achieves state-of-the-art results on the Indic Massive Text Embedding Benchmark.