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
Editors:
Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
Venue:
SemEval
SIG:
SIGLEX
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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2021.semeval-1.157.pdf
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