@inproceedings{chen-zhao-2025-educsw,
    title = "{E}du{CSW}: Building a {M}andarin-{E}nglish Code-Switched Generation Pipeline for Computer Science Learning",
    author = "Chen, Ruishi  and
      Zhao, Yiling",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bea-1.68/",
    pages = "908--919",
    ISBN = "979-8-89176-270-1",
    abstract = "This paper presents EduCSW, a novel pipeline for generating Mandarin-English code-switched text to support AI-powered educational tools that adapt computer science instruction to learners' language proficiency through mixed-language delivery. To address the scarcity of code-mixed datasets, we propose an encoder-decoder architecture that generates natural code-switched text using only minimal existing code-mixed examples and parallel corpora. Evaluated on a corpus curated for computer science education, human annotators rated 60{--}64{\%} of our model{'}s outputs as natural, significantly outperforming both a baseline fine-tuned neural machine translation (NMT) model (22{--}24{\%}) and the DeepSeek-R1 model (34{--}44{\%}). The generated text achieves a Code-Mixing Index (CMI) of 25.28{\%}, aligning with patterns observed in spontaneous Mandarin-English code-switching. Designed to be generalizable across language pairs and domains, this pipeline lays the groundwork for generating training data to support the development of educational tools with dynamic code-switching capabilities."
}