@inproceedings{guan-zhou-2021-tsia,
    title = "Tsia at {S}em{E}val-2021 Task 7: Detecting and Rating Humor and Offense",
    author = "Guan, Zhengyi  and
      Zhou, Xiaobing ZXB",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.semeval-1.154/",
    doi = "10.18653/v1/2021.semeval-1.154",
    pages = "1108--1113",
    abstract = "This paper describes our contribution to SemEval-2021 Task 7: Detecting and Rating Humor and Of-fense.This task contains two sub-tasks, sub-task 1and sub-task 2. Among them, sub-task 1 containsthree sub-tasks, sub-task 1a ,sub-task 1b and sub-task 1c.Sub-task 1a is to predict if the text would beconsidered humorous. Sub-task 1c is described asfollows: if the text is classed as humorous, predictif the humor rating would be considered controver-sial, i.e. the variance of the rating between annota-tors is higher than the median.we combined threepre-trained model with CNN to complete these twoclassification sub-tasks. Sub-task 1b is to judge thedegree of humor. Sub-task 2 aims to predict how of-fensive a text would be with values between 0 and5.We use the idea of regression to deal with thesetwo sub-tasks. We analyze the performance of ourmethod and demonstrate the contribution of eachcomponent of our architecture. We have achievedgood results under the combination of multiple pre-training models and optimization methods."
}