Abstract
This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble approach to solve various sub-tasks that were part of the challenge. We also present a comparative analysis of various models we tried. Our method was ranked 4th in Humor Controversy Detection, 8th in Humor Detection, 19th in Average Offense Score prediction and 40th in Average Humor Score prediction globally. F1 score obtained for Humor classification was 0.9655 and for Controversy detection it was 0.6261. Our user name on the leader board is ThisIstheEnd and team name is EndTimes.- Anthology ID:
- 2021.semeval-1.172
- 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:
- 1215–1220
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.172
- DOI:
- 10.18653/v1/2021.semeval-1.172
- Cite (ACL):
- Chandan Kumar Pandey, Chirag Singh, and Karan Mangla. 2021. EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1215–1220, Online. Association for Computational Linguistics.
- Cite (Informal):
- EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles (Pandey et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.semeval-1.172.pdf