Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora

TL;DR

This study shows that machine translation can effectively transfer moral language understanding from English to Polish, enabling cross-lingual moral values research despite cultural and linguistic differences.

Contribution

It demonstrates that LLM-based translation preserves subtle moral cues across languages, facilitating moral semantics research in under-resourced languages like Polish.

Findings

Cross-lingual embedding similarity of 0.86 indicates strong moral cue preservation.

Minimal AUC gaps (0.01-0.02) show classifier parity across languages.

Translation quality improves with fine-tuning of language models.

Abstract

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 values 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. 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, Centered 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 mean cosine similarity of 0.86 and AUC gaps of 0.01--0.02 across all foundations closing further under fine-tuning of language models. These results demonstrate that machine translation is a practical and cost-effective path to moral values research in languages currently under-resourced in this domain. We demonstrate this for Polish as a representative Slavic language, with expected generalisation to related languages.