@inproceedings{mukherjee-etal-2023-ufal-uld,
    title = "{UFAL}-{ULD} at {BLP}-2023 Task 2 Sentiment Classification in {B}angla Text",
    author = "Mukherjee, Sourabrata  and
      Ojha, Atul Kr.  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Sadeque, Farig  and
      Amin, Ruhul",
    booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.banglalp-1.45/",
    doi = "10.18653/v1/2023.banglalp-1.45",
    pages = "336--339",
    abstract = "In this paper, we present the UFAL-ULD team`s system for the BLP Shared Task 2: Sentiment Analysis of Bangla Social Media Posts. The Task 2 involves classifying text into Positive, Negative, or Neutral sentiments. As a part of this task, we conducted a series of experiments with several pre-trained sequence classification models {--} XLM-RoBERTa, BanglaBERT, Bangla BERT Base and Multilingual BERT. Among these, our best-performing model was based on the XLM-RoBERTa-base architecture, which outperforms baseline models. Our system was ranked 19th among the 30 teams that participated in the task."
}