%0 Conference Proceedings %T Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense %A Guan, Zhengyi %A Zhou, Xiaobing ZXB %Y Palmer, Alexis %Y Schneider, Nathan %Y Schluter, Natalie %Y Emerson, Guy %Y Herbelot, Aurelie %Y Zhu, Xiaodan %S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F guan-zhou-2021-tsia %X 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. %R 10.18653/v1/2021.semeval-1.154 %U https://aclanthology.org/2021.semeval-1.154 %U https://doi.org/10.18653/v1/2021.semeval-1.154 %P 1108-1113