Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense

Zhengyi Guan, Xiaobing ZXB Zhou


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.
Anthology ID:
2021.semeval-1.154
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venue:
SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1108–1113
Language:
URL:
https://aclanthology.org/2021.semeval-1.154
DOI:
10.18653/v1/2021.semeval-1.154
Bibkey:
Cite (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.
Cite (Informal):
Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense (Guan & Zhou, SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.154.pdf