# Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English

arXiv: https://arxiv.org/abs/1909.10892 · HTML: https://mlsanity.com/paper/1909.10892/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech](https://mlsanity.com/paper/2108.12881/) (2021)
- [Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation](https://mlsanity.com/paper/2205.12649/) (2022)
- [IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages](https://mlsanity.com/paper/2311.12405/) (2023)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Predicting User Code-Switching Level from Sociological and Psychological Profiles](https://mlsanity.com/paper/2112.06462/) (2021)
- [The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic-English Code-Switching Levels based on Character Profiles](https://mlsanity.com/paper/2208.00433/) (2022)

## Abstract (arXiv)

Code-switching (CS) is a widespread phenomenon among bilingual and multilingual societies. The lack of CS resources hinders the performance of many NLP tasks. In this work, we explore the potential use of bilingual word embeddings for code-switching (CS) language modeling (LM) in the low resource Egyptian Arabic-English language. We evaluate different state-of-the-art bilingual word embeddings approaches that require cross-lingual resources at different levels and propose an innovative but simple approach that jointly learns bilingual word representations without the use of any parallel data, relying only on monolingual and a small amount of CS data. While all representations improve CS LM, ours performs the best and improves perplexity 33.5% relative over the baseline.