Romanization is a specific type of transliteration where text is converted into the Roman (Latin) script — often used in NLP to standardize inputs from diverse scripts (Cyrillic, Arabic, Devanagari, CJK) into a common character set to facilitate transfer or vocabulary sharing.

Usage in NLP

• Vocabulary Reduction: Instead of a vocabulary of 200,000 (learning Chinese chars, Hindi chars, etc.), Romanization allows a smaller shared vocabulary.
• Alignment: It's easier for a model to see that "Bank" (En) and "Bank" (De) are similar if they share the script. Not so for "Bank" vs "банк".
• u-PMLM: Universal Phonetic/Romanized models convert all training data to IPA or Latin script to focus on phonetic/semantic similarities.

Why It Matters

• Low-Resource: Helps languages with rare scripts benefit from high-resource Latin-script transfer.
• Lossy: Romanization typically loses information (tonal marks, distinct spellings mapping to same phoneme) — inevitable trade-off.

Romanization is standardizing to Latin — converting all world scripts into A-Z characters to maximize overlap and sharing in multilingual models.