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Idiomatic expressions, slang, and cultural references introduce hard-to-avoid translation artifacts. Yet automated moral classification depends on language-specific annotated corpora that exist almost exclusively in English. We investigate whether LLM-based translation can bridge this gap, taking Polish as a test case.\n  Using $\\sim$50k morally annotated social media posts from a diverse range of topics, we apply a principled four-method validation pipeline: LaBSE cross-lingual embedding similarity, Centred Kernel Alignment (CKA), LLM-as-judge evaluation, and deep learning classifier parity tests. We show that despite shortcomings in handling slang, vulgarity, and culturally loaded expressions, direct translation preserves subtle moral cues well enough to be harvested by cross-lingual machine learning --- with a mean ","title":"Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora","url":"https://arxiv.org/abs/2605.22660","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.22660v2 Announce Type: replace-cross \nAbstract: Moral language is subtle and culturally variable, making it difficult to translate faithfully across languages. Idiomatic expressions, slang, and cultural references introduce hard-to-avoid translation artifacts. Yet automated moral classification depends on language-specific annotated corpora that exist almost exclusively in English. 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