Code-switching Sentence Generation By Generative Adversarial Networks And Its Application To Data Augmentation

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keychang2018code

Related papers

• Code-switched Language Models Using Neural Based Synthetic Data From Parallel Sentences (2019)11.29
• Data Augmentation For End-to-end Code-switching Speech Recognition (2020)9.92
• Language-agnostic Code-switching In Sequence-to-sequence Speech Recognition (2022)0.00
• Investigating Lexical Replacements For Arabic-english Code-switched Data Augmentation (2022)5.84
• Adversarial Synthesis Based Data-augmentation For Code-switched Spoken Language Identification (2022)0.00
• Textual Data Augmentation For Arabic-english Code-switching Speech Recognition (2022)6.77
• Code-switching Speech Recognition Under The Lens: Model- And Data-centric Perspectives (2025)0.00
• Speech Collage: Code-switched Audio Generation By Collaging Monolingual Corpora (2023)3.58