# Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data

## TL;DR

This paper investigates how pre-trained multilingual embeddings can improve part-of-speech tagging in code-switched data across four language pairs, demonstrating benefits for related languages and outperforming existing models.

## Contribution

It introduces neural network models leveraging pre-trained embeddings for POS tagging in code-switching, showing improved performance over state-of-the-art methods.

## Key findings

- Multilingual embeddings benefit closely related languages in CS POS tagging.
- Distant language pairs experience noise from multilingual embeddings.
- Proposed models outperform existing CS taggers for MSA-EGY.

## Abstract

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.