# Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation

arXiv: https://arxiv.org/abs/2205.12649 · HTML: https://mlsanity.com/paper/2205.12649/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 73 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English](https://mlsanity.com/paper/1902.01382/) (2019)
- [Speech Recognition Challenge in the Wild: Arabic MGB-3](https://mlsanity.com/paper/1709.07276/) (2017)
- [Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences](https://mlsanity.com/paper/1909.08582/) (2019)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text](https://mlsanity.com/paper/2107.06483/) (2021)
- [Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation](https://mlsanity.com/paper/1811.02356/) (2018)
- [Can You Traducir This? Machine Translation for Code-Switched Input](https://mlsanity.com/paper/2105.04846/) (2021)
- [Challenges of Computational Processing of Code-Switching](https://mlsanity.com/paper/1610.02213/) (2016)
- [Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech](https://mlsanity.com/paper/2108.12881/) (2021)
- [LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual](https://mlsanity.com/paper/1309.5652/) (2013)
- [Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2201.02550/) (2022)
- [Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling](https://mlsanity.com/paper/1810.10254/) (2018)
- [Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks](https://mlsanity.com/paper/2112.06327/) (2021)
- [The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation](https://mlsanity.com/paper/1906.11751/) (2019)
- [Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English](https://mlsanity.com/paper/1909.10892/) (2019)
- [Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages](https://mlsanity.com/paper/2103.13272/) (2021)
- [IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task](https://mlsanity.com/paper/2106.16055/) (2021)

## Built on by (most cited first, 9 of 9)

- [ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English](https://mlsanity.com/paper/2211.12000/) (2022)
- [CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset](https://mlsanity.com/paper/2509.14161/) (2025)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [Adapting Language Balance in Code-Switching Speech](https://mlsanity.com/paper/2510.18724/) (2025)
- [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)
- [The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR](https://mlsanity.com/paper/2503.23576/) (2025)
- [Bayesian Low-Rank Factorization for Robust Model Adaptation](https://mlsanity.com/paper/2510.18723/) (2025)

## Abstract (arXiv)

Data sparsity is a main problem hindering the development of code-switching (CS) NLP systems. In this paper, we investigate data augmentation techniques for synthesizing dialectal Arabic-English CS text. We perform lexical replacements using word-aligned parallel corpora where CS points are either randomly chosen or learnt using a sequence-to-sequence model. We compare these approaches against dictionary-based replacements. We assess the quality of the generated sentences through human evaluation and evaluate the effectiveness of data augmentation on machine translation (MT), automatic speech recognition (ASR), and speech translation (ST) tasks. Results show that using a predictive model results in more natural CS sentences compared to the random approach, as reported in human judgements. In the downstream tasks, despite the random approach generating more data, both approaches perform equally (outperforming dictionary-based replacements). Overall, data augmentation achieves 34% improvement in perplexity, 5.2% relative improvement on WER for ASR task, +4.0-5.1 BLEU points on MT task, and +2.1-2.2 BLEU points on ST over a baseline trained on available data without augmentation.