Regardless of language, the standard character set for text messages (SMS) and many other social media platforms is the Roman alphabet. There are romanization conventions for some character sets, but they are used inconsistently in informal text, such as SMS. In this work, we convert informal, romanized Urdu messages into the native Arabic script and normalize non-standard SMS language. Doing so prepares the messages for existing downstream processing tools, such as machine translation, which are typically trained on well-formed, native script text. Our model combines information at the word and character levels, allowing it to handle out-of-vocabulary items. Compared with a baseline deterministic approach, our system reduces both word and character error rate by over 50%.
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