@inproceedings{gong-etal-2020-design,
    title = "The Design and Construction of a {C}hinese Sarcasm Dataset",
    author = "Gong, Xiaochang  and
      Zhao, Qin  and
      Zhang, Jun  and
      Mao, Ruibin  and
      Xu, Ruifeng",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.lrec-1.619/",
    pages = "5034--5039",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    abstract = "As a typical multi-layered semi-conscious language phenomenon, sarcasm is widely existed in social media text for enhancing the emotion expression. Thus, the detection and processing of sarcasm is important to social media analysis. However, most existing sarcasm dataset are in English and there is still a lack of authoritative Chinese sarcasm dataset. In this paper, we presents the design and construction of a largest high-quality Chinese sarcasm dataset, which contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcastic texts. Furthermore, a balanced dataset through elaborately sampling the same amount non-sarcastic texts for training sarcasm classifier. Using the dataset as the benchmark, some sarcasm classification methods are evaluated."
}