Abstract
This paper describes the system submitted to SemEval-2021 Task-7 for all four subtasks. Two subtasks focus on detecting humor and offense from the text (binary classification). On the other hand, the other two subtasks predict humor and offense ratings of the text (linear regression). In this paper, we present two different types of fine-tuning methods by using linear layers and bi-LSTM layers on top of the pre-trained BERT model. Results show that our system is able to outperform baseline models by a significant margin. We report F1 scores of 0.90 for the first subtask and 0.53 for the third subtask, while we report an RMSE of 0.57 and 0.58 for the second and fourth subtasks, respectively.- Anthology ID:
- 2021.semeval-1.164
- 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:
- 1169–1174
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.164
- DOI:
- 10.18653/v1/2021.semeval-1.164
- Cite (ACL):
- Anik Mondal and Raksha Sharma. 2021. Team_KGP at SemEval-2021 Task 7: A Deep Neural System to Detect Humor and Offense with Their Ratings in the Text Data. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1169–1174, Online. Association for Computational Linguistics.
- Cite (Informal):
- Team_KGP at SemEval-2021 Task 7: A Deep Neural System to Detect Humor and Offense with Their Ratings in the Text Data (Mondal & Sharma, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.semeval-1.164.pdf