@inproceedings{du-etal-2022-pali,
    title = "{PALI}-{NLP} at {S}em{E}val-2022 Task 6: i{S}arcasm{E}val- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm",
    author = "Du, Xiyang  and
      Hu, Dou  and
      Zhi, Jin  and
      Jiang, Lianxin  and
      Shi, Xiaofeng",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.semeval-1.112/",
    doi = "10.18653/v1/2022.semeval-1.112",
    pages = "815--819",
    abstract = "This paper describes the method we utilized in the SemEval-2022 Task 6 iSarcasmEval: Intended Sarcasm Detection In English and Arabic. Our system has achieved 1st in SubtaskB, which is to identify the categories of intended sarcasm. The proposed system integrates multiple BERT-based, RoBERTa-based and BERTweet-based models with finetuning. In this task, we contributed the following: 1) we reveal several large pre-trained models' performance on tasks coping with the tweet-like text. 2) Our methods prove that we can still achieve excellent results in this particular task without a complex classifier adopting some proper training method. 3) we found there is a hierarchical relationship of sarcasm types in this task."
}