  Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual
speakers alternate between two or more languages/dialects within a single
conversation. Processing CS data is especially challenging in intra-sentential
data given state-of-the-art monolingual NLP technologies since such
technologies are geared toward the processing of one language at a time. In
this paper, we address the problem of Part-of-Speech tagging (POS) in the
context of linguistic code switching (CS). We explore leveraging multiple
neural network architectures to measure the impact of different pre-trained
embeddings methods on POS tagging CS data. We investigate the landscape in four
CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic-
Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic
dialect (MSA-LEV). Our results show that multilingual embedding (e.g., MSA-EGY
and MSA-LEV) helps closely related languages (EGY/LEV) but adds noise to the
languages that are distant (SPA/HIN). Finally, we show that our proposed models
outperform state-of-the-art CS taggers for MSA-EGY language pair.
