DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?
Alexandros Karasakalidis, Dimitrios Effrosynidis, Avi Arampatzis
Abstract
This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a variety of preprocessing techniques, vectorization methods, and numerous conventional machine learning algorithms, in order to construct classification and regression models for the given tasks. We used majority voting to combine the models’ outputs with small Neural Networks (NN) for classification tasks and their mean for regression for improving our system’s performance. While these methods proved weaker than modern, deep learning models, they are still relevant in research tasks because of their low requirements on computational power and faster training.- Anthology ID:
- 2021.semeval-1.157
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1125–1129
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.157
- DOI:
- 10.18653/v1/2021.semeval-1.157
- Cite (ACL):
- Alexandros Karasakalidis, Dimitrios Effrosynidis, and Avi Arampatzis. 2021. DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1125–1129, Online. Association for Computational Linguistics.
- Cite (Informal):
- DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021? (Karasakalidis et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.157.pdf