The wide accessibility of social media has provided linguistically
under-represented communities with an extraordinary opportunity to create
content in their native languages. This, however, comes with certain challenges
in script normalization, particularly where the speakers of a language in a
bilingual community rely on another script or orthography to write their native
language. This paper addresses the problem of script normalization for several
such languages that are mainly written in a Perso-Arabic script. Using
synthetic data with various levels of noise and a transformer-based model, we
demonstrate that the problem can be effectively remediated. We conduct a
small-scale evaluation of real data as well. Our experiments indicate that
script normalization is also beneficial to improve the performance of
downstream tasks such as machine translation and language identification.

本文研究在社交媒体上，通过使用建模技术，对不同语言，特别是那些主要使用波斯 - 阿拉伯语脚本书写的语言的脚本归一化的问题进行了处理，而这有助于提高机器翻译和语言识别等下游任务的性能。