Mr-Fosdick at SemEval-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation
Christian Falkenberg, Erik Schönwälder, Tom Rietzke, Chris-Andris Görner, Robert Walther, Julius Gonsior, Anja Reusch
Abstract
In supervised learning, a significant amount of data is essential. To achieve this, we generated and evaluated datasets based on a provided dataset using transformer and non-transformer models. By utilizing these generated datasets during the training of new models, we attain a higher balanced accuracy during validation compared to using only the original dataset.- Anthology ID:
- 2023.semeval-1.11
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 88–93
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.11
- DOI:
- 10.18653/v1/2023.semeval-1.11
- Cite (ACL):
- Christian Falkenberg, Erik Schönwälder, Tom Rietzke, Chris-Andris Görner, Robert Walther, Julius Gonsior, and Anja Reusch. 2023. Mr-Fosdick at SemEval-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 88–93, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Mr-Fosdick at SemEval-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation (Falkenberg et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.semeval-1.11.pdf