@inproceedings{paul-etal-2025-generating,
    title = "Generating and Analyzing Disfluency in a Code-Mixed Setting",
    author = "Paul, Aryan  and
      Mondal, Tapabrata  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.105/",
    pages = "915--924",
    abstract = "This work explores the intersection of code-mixing and disfluency in bilingual speech and text, with a focus on understanding how large language models (LLMs) handle code-mixed disfluent utterances. One of the primary objectives is to explore LLMs' ability to generate code-mixed disfluent sentences and to address the lack of high-quality code-mixed disfluent corpora, particularly for Indic languages. We aim to compare the performance of LLM-based approaches with traditional disfluency detection methods and to develop novel metrics for quantitatively assessing disfluency phenomena. Additionally, we investigate the relationship between code-mixing and disfluency, exploring how factors such as switching frequency and direction influence the occurrence of disfluencies. By analyzing these intriguing dynamics, we seek to gain a deeper understanding of the mutual influence between code-mixing and disfluency in multilingual speech."
}