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    <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.</abstract>
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%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

Markdown (Informal)

[Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense](https://aclanthology.org/2021.semeval-1.154) (Guan & Zhou, SemEval 2021)

• Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense (Guan & Zhou, SemEval 2021)

ACL

• Zhengyi Guan and Xiaobing ZXB Zhou. 2021. Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1108–1113, Online. Association for Computational Linguistics.