mlsanityData to 14 Oct 2026

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

arXiv 1811.02356 ↗First posted 6 Nov 2018cs.CLcited by 26

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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.

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

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Citing papers per year: 2021: 6; 2022: 9; 2023: 5; 2024: 4; 2025: 2.

26 papers.

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  1. 2022The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challengescited by 35
  2. 2021From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Textcited by 29
  3. 2022LAE: Language-Aware Encoder for Monolingual and Multilingual ASRcited by 22
  4. 2021Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorizationcited by 18
  5. 2022Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognitioncited by 16
  6. 2025CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languagescited by 10
  7. 2021Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Modelscited by 9
  8. 2021Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networkscited by 9
  9. 2022Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentationcited by 9
  10. 2021El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsingcited by 8
  11. 2021Non-autoregressive Mandarin-English Code-switching Speech Recognitioncited by 8
  12. 2022Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognitioncited by 7
14 more
  1. 2023Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkiencited by 7
  2. 2023Code-Switched Text Synthesis in Unseen Language Pairscited by 7
  3. 2022Reducing language context confusion for end-to-end code-switching automatic speech recognitioncited by 6
  4. 2022Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognitioncited by 5
  5. 2022CST5: Data Augmentation for Code-Switched Semantic Parsingcited by 5
  6. 2023IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languagescited by 5
  7. 2024Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Datacited by 5
  8. 2024Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysiscited by 4
  9. 2023Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Studycited by 3
  10. 2023Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentationcited by 2
  11. 2024Grammatical Error Correction for Code-Switched Sentences by Learners of Englishcited by 2
  12. 2022Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed datacited by 1
  13. 2024Methods of Automatic Matrix Language Determination for Code-Switched Speechcited by 0
  14. 2025Strategies of Code-switching in Human-Machine Dialogscited by 0