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    <abstract>This paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection task, the humor rating prediction task, and the humor controversy detection task. Subtask2 is an offensive rating prediction task. Detection task is a binary classification task, and the rating prediction task is a regression task between 0 to 5. 0 means the task is not humorous or not offensive, 5 means the task is very humorous or very offensive. For all the tasks, this paper chooses RoBERTa as the pre-trained model. In classification tasks, Bi-LSTM and adversarial training are adopted. In the regression task, the Bi-LSTM is also adopted. And then we propose a new approach named compare method. Finally, our system achieves an F1-score of 95.05% in the humor detection task, F1-score of 61.74% in the humor controversy detection task, 0.6143 RMSE in humor rating task, 0.4761 RMSE in the offensive rating task on the test datasets.</abstract>
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%0 Conference Proceedings
%T Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense
%A Liu, Renyuan
%A Zhou, Xiaobing
%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 liu-zhou-2021-grenzlinie
%X This paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection task, the humor rating prediction task, and the humor controversy detection task. Subtask2 is an offensive rating prediction task. Detection task is a binary classification task, and the rating prediction task is a regression task between 0 to 5. 0 means the task is not humorous or not offensive, 5 means the task is very humorous or very offensive. For all the tasks, this paper chooses RoBERTa as the pre-trained model. In classification tasks, Bi-LSTM and adversarial training are adopted. In the regression task, the Bi-LSTM is also adopted. And then we propose a new approach named compare method. Finally, our system achieves an F1-score of 95.05% in the humor detection task, F1-score of 61.74% in the humor controversy detection task, 0.6143 RMSE in humor rating task, 0.4761 RMSE in the offensive rating task on the test datasets.
%R 10.18653/v1/2021.semeval-1.34
%U https://aclanthology.org/2021.semeval-1.34
%U https://doi.org/10.18653/v1/2021.semeval-1.34
%P 281-285

Markdown (Informal)

[Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense](https://aclanthology.org/2021.semeval-1.34) (Liu & Zhou, SemEval 2021)

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

ACL

• Renyuan Liu and Xiaobing Zhou. 2021. Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 281–285, Online. Association for Computational Linguistics.