YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need

Mayukh Sharma, Ilanthenral Kandasamy, W.b. Vasantha


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
Venues:
ACL | IJCNLP | 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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.161.pdf