# Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation

arXiv: https://arxiv.org/abs/1811.02356 · HTML: https://mlsanity.com/paper/1811.02356/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 26 of 26)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text](https://mlsanity.com/paper/2107.06483/) (2021)
- [LAE: Language-Aware Encoder for Monolingual and Multilingual ASR](https://mlsanity.com/paper/2206.02093/) (2022)
- [Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization](https://mlsanity.com/paper/2111.15016/) (2021)
- [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022)
- [CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages](https://mlsanity.com/paper/2507.18791/) (2025)
- [Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models](https://mlsanity.com/paper/2110.03504/) (2021)
- [Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks](https://mlsanity.com/paper/2112.06327/) (2021)
- [Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation](https://mlsanity.com/paper/2205.12649/) (2022)
- [El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing](https://mlsanity.com/paper/2101.10524/) (2021)
- [Non-autoregressive Mandarin-English Code-switching Speech Recognition](https://mlsanity.com/paper/2104.02258/) (2021)
- [Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition](https://mlsanity.com/paper/2210.08992/) (2022)
- [Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien](https://mlsanity.com/paper/2301.08937/) (2023)
- [Code-Switched Text Synthesis in Unseen Language Pairs](https://mlsanity.com/paper/2305.16724/) (2023)
- [Reducing language context confusion for end-to-end code-switching automatic speech recognition](https://mlsanity.com/paper/2201.12155/) (2022)
- [Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition](https://mlsanity.com/paper/2207.04176/) (2022)
- [CST5: Data Augmentation for Code-Switched Semantic Parsing](https://mlsanity.com/paper/2211.07514/) (2022)
- [IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages](https://mlsanity.com/paper/2311.12405/) (2023)
- [Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data](https://mlsanity.com/paper/2407.10603/) (2024)
- [Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis](https://mlsanity.com/paper/2411.00691/) (2024)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation](https://mlsanity.com/paper/2306.08588/) (2023)
- [Grammatical Error Correction for Code-Switched Sentences by Learners of English](https://mlsanity.com/paper/2404.12489/) (2024)
- [Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed data](https://mlsanity.com/paper/2210.12215/) (2022)
- [Methods of Automatic Matrix Language Determination for Code-Switched Speech](https://mlsanity.com/paper/2410.02521/) (2024)
- [Strategies of Code-switching in Human-Machine Dialogs](https://mlsanity.com/paper/2508.07325/) (2025)

## Abstract (arXiv)

Code-switching is about dealing with alternative languages in speech or text. It is partially speaker-depend and domain-related, so completely explaining the phenomenon by linguistic rules is challenging. Compared to most monolingual tasks, insufficient data is an issue for code-switching. To mitigate the issue without expensive human annotation, we proposed an unsupervised method for code-switching data augmentation. By utilizing a generative adversarial network, we can generate intra-sentential code-switching sentences from monolingual sentences. We applied proposed method on two corpora, and the result shows that the generated code-switching sentences improve the performance of code-switching language models.