# Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling

arXiv: https://arxiv.org/abs/1810.10254 · HTML: https://mlsanity.com/paper/1810.10254/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

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

- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text](https://mlsanity.com/paper/2107.06483/) (2021)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline](https://mlsanity.com/paper/2206.13135/) (2022)
- [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)
- [Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling](https://mlsanity.com/paper/2104.06268/) (2021)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR](https://mlsanity.com/paper/2503.23576/) (2025)

## Abstract (arXiv)

Building large-scale datasets for training code-switching language models is challenging and very expensive. To alleviate this problem using parallel corpus has been a major workaround. However, existing solutions use linguistic constraints which may not capture the real data distribution. In this work, we propose a novel method for learning how to generate code-switching sentences from parallel corpora. Our model uses a Seq2Seq model in combination with pointer networks to align and choose words from the monolingual sentences and form a grammatical code-switching sentence. In our experiment, we show that by training a language model using the augmented sentences we improve the perplexity score by 10% compared to the LSTM baseline.