@inproceedings{smid-priban-2023-prompt,
    title = "Prompt-Based Approach for {C}zech Sentiment Analysis",
    author = "{\v{S}}m{\'i}d, Jakub  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.ranlp-1.118/",
    pages = "1110--1120",
    abstract = "This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our prompt-based approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario."
}