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The wide accessibility of social media has provided linguisticallyunder-represented communities with an extraordinary opportunity to createcontent in their native languages. This, however, comes with certain challengesin script normalization, particularly where the speakers of a language in abilingual community rely on another script or orthography to write their nativelanguage. This paper addresses the problem of script normalization for severalsuch languages that are mainly written in a Perso-Arabic script. Usingsynthetic data with various levels of noise and a transformer-based model, wedemonstrate that the problem can be effectively remediated. We conduct asmall-scale evaluation of real data as well. Our experiments indicate thatscript normalization is also beneficial to improve the performance ofdownstream tasks such as machine translation and language identification.

Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities