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

2018/11/06 by Ching-Ting Chang, Chang, Ching-Ting, Shun-Po Chuang +3 · 4 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1811.02356

openalex publication_date 2018/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
• A Survey of Code-switched Speech and Language Processing
• Strategies of Code-switching in Human-Machine Dialogs
• CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages

Related

• Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks
• A Deep Generative Model for Code-Switched Text
• Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling
• A Survey of Code-switched Speech and Language Processing
• Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences