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Lexical normalization, the translation of non-canonical data to standardlanguage, has shown to improve the performance of manynatural languageprocessing tasks on social media. Yet, using multiple languages in oneutterance, also called code-switching (CS), is frequently overlooked by thesenormalization systems, despite its common use in social media. In this paper,we propose three normalization models specifically designed to handlecode-switched data which we evaluate for two language pairs: Indonesian-English(Id-En) and Turkish-German (Tr-De). For the latter, we introduce novelnormalization layers and their corresponding language ID and POS tags for thedataset, and evaluate the downstream effect of normalization on POS tagging.Results show that our CS-tailored normalization models outperform Id-En stateof the art and Tr-De monolingual models, and lead to 5.4% relative performanceincrease for POS tagging as compared to unnormalized input.

Lexical Normalization for Code-switched Data and its Effect on POS-tagging