@inproceedings{raihan-etal-2024-emomix,
    title = "{E}mo{M}ix-3{L}: A Code-Mixed Dataset for {B}angla-{E}nglish-{H}indi for Emotion Detection",
    author = "Raihan, Nishat  and
      Goswami, Dhiman  and
      Mahmud, Antara  and
      Anastasopoulos, Antonios  and
      Zampieri, Marcos",
    editor = "Jha, Girish Nath  and
      L., Sobha  and
      Bali, Kalika  and
      Ojha, Atul Kr.",
    booktitle = "Proceedings of the 7th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.wildre-1.2",
    pages = "11--16",
    abstract = "Code-mixing is a well-studied linguistic phenomenon that occurs when two or more languages are mixed in text or speech. Several studies have been conducted on building datasets and performing downstream NLP tasks on code-mixed data. Although it is not uncommon to observe code-mixing of three or more languages, most available datasets in this domain contain code-mixed data from only two languages. In this paper, we introduce EmoMix-3L, a novel multi-label emotion detection dataset containing code-mixed data from three different languages. We experiment with several models on EmoMix-3L and we report that MuRIL outperforms other models on this dataset.",
}