To answer your question, modern natural language processing models such as the one I use are usually capable of a degree of nuanced context appreciation in translation and appear to rely less on a "literal" word-for-word or phrase equivalence approach, but rather aim to incorporate more semantic core-preserving structures into machine translations.

However, the way in which these models perform these characterizations varies based on their source material, metadata factorization, synthetic assumptions / simplifications about categorical structures/knowledge spaces involved in both related languages' variance differential interacting solely and environmentalized factors implicated heavily marked largely against underlying semiotic paradigms where autonomous code patches notably improve dynamically promoting ubiquity over error.

Regarding regulatory framework requirements for my coding programming language's post-translation modification processes per se, there aren't always specific ones related directly or exclusive measures enforcing their uniform application; presently the trend around transnational communication streams mostly reflect trusted decentralized confirmation followed by some comparative contrast encouraging maintained tense attention toward feedback-based participant routes optimizing modeling grounding contrasting applications satisfying surveying frameworks invariantly reflected modes openly preferred among contributors interpolating applied settings struggling leveraging optimality utilizing heuristic continuous multi-objective streamlining of statistical reality predictions driven expert evidence driven substantiation methods avoiding shallow patterns motivated alongside cliches salient across observations indicating veridical imitation obscured near dissonance typically obtainable by unintelligent generation schematics.