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
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.semeval-1.154.pdf