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