@inproceedings{ran-etal-2023-ynu,
    title = "{YNU}-{HPCC} at {WASSA} 2023: Using Text-Mixed Data Augmentation for Emotion Classification on Code-Mixed Text Message",
    author = "Ran, Xuqiao  and
      Zhang, You  and
      Wang, Jin  and
      Xu, Dan  and
      Zhang, Xuejie",
    editor = "Barnes, Jeremy  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman",
    booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.wassa-1.60/",
    doi = "10.18653/v1/2023.wassa-1.60",
    pages = "611--615",
    abstract = "Emotion classification on code-mixed texts has been widely used in real-world applications. In this paper, we build a system that participates in the WASSA 2023 Shared Task 2 for emotion classification on code-mixed text messages from Roman Urdu and English. The main goal of the proposed method is to adopt a text-mixed data augmentation for robust code-mixed text representation. We mix texts with both multi-label (track 1) and multi-class (track 2) annotations in a unified multilingual pre-trained model, i.e., XLM-RoBERTa, for both subtasks. Our results show that the proposed text-mixed method performs competitively, ranking first in both tracks, achieving an average Macro F1 score of 0.9782 on the multi-label track and of 0.9329 on the multi-class track."
}