Abstract
In this paper, we describe our system used for SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We used a simple fine-tuning approach using different Pre-trained Language Models (PLMs) to evaluate their performance for humor and offense detection. For regression tasks, we averaged the scores of different models leading to better performance than the original models. We participated in all SubTasks. Our best performing system was ranked 4 in SubTask 1-b, 8 in SubTask 1-c, 12 in SubTask 2, and performed well in SubTask 1-a. We further show comprehensive results using different pre-trained language models which will help as baselines for future work.- Anthology ID:
- 2021.semeval-1.161
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1146–1152
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.161
- DOI:
- 10.18653/v1/2021.semeval-1.161
- Cite (ACL):
- Mayukh Sharma, Ilanthenral Kandasamy, and W.b. Vasantha. 2021. YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1146–1152, Online. Association for Computational Linguistics.
- Cite (Informal):
- YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need (Sharma et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2021.semeval-1.161.pdf
- Code
- 04mayukh/YoungSheldon-at-SemEval-2021-Task-7-HaHackathon