Before revision:  In this paper, we focus on normalization in the context of code-switching. We introduce a variety of models to perform normalization on CS data , and analyse the impact of word-level language identification on normalization . We show that the performance of the proposed normalization models is generally high, but language labels are only slightly informative. We also carry out POS taggingas extrinsic evaluation and show that automatic normalization of the input leads to 3.2\% absolute performance increase , whereas gold normalization leads to an increase of 6.8\% .
After revision:  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 .