Module A - The Normalization Engine Linguistic Challenge: Roman Urdu lacks standardized orthography (e.g., "kesa" vs "kaisa"), creating orthographic "noise" that significantly degrades the accuracy of downstream AI models. Technical Role: Acts as a Sequence-to-Sequence (Seq2Seq) transliteration and lexical normalization layer to standardize inputs before analysis. Model: A specialized transformer architecture, specifically m2m100 fine-tuned on parallel corpora or UrduParaphraseBERT. Primary Dataset: Roman-Urdu-Parl (RUP). A large-scale parallel corpus of 6.37 million sentence pairs designed to support machine transliteration and word embedding training. Link: https://arxiv.org/abs/2503.21530 Outcome: Reduces orthographic noise by achieving up to 97.44% Char-BLEU accuracy for Roman-Urdu to Urdu conversion, ensuring Module B receives high-quality "clean" data for risk analysis. Module B - Risk Stratification (BERT) Heading: The "Safety ...