@inproceedings{thin-etal-2023-abcd,
    title = "{ABCD} Team at {S}em{E}val-2023 Task 12: An Ensemble Transformer-based System for {A}frican Sentiment Analysis",
    author = "Thin, Dang  and
      Nguyen, Dai  and
      Qui, Dang  and
      Hao, Duong  and
      Nguyen, Ngan",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.semeval-1.44/",
    doi = "10.18653/v1/2023.semeval-1.44",
    pages = "324--330",
    abstract = "This paper describes the system of the ABCD team for three main tasks in the SemEval-2023 Task 12: AfriSenti-SemEval for Low-resource African Languages using Twitter Dataset. We focus on exploring the performance of ensemble architectures based on the soft voting technique and different pre-trained transformer-based language models. The experimental results show that our system has achieved competitive performance in some Tracks in Task A: Monolingual Sentiment Analysis, where we rank the Top 3, Top 2, and Top 4 for the Hause, Igbo and Moroccan languages. Besides, our model achieved competitive results and ranked {\$}14{\textasciicircum}{\{}th{\}}{\$} place in Task B (multilingual) setting and {\$}14{\textasciicircum}{\{}th{\}}{\$} and {\$}8{\textasciicircum}{\{}th{\}}{\$} place in Track 17 and Track 18 of Task C (zero-shot) setting."
}