@inproceedings{falkenberg-etal-2023-mr,
    title = "Mr-Fosdick at {S}em{E}val-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation",
    author = {Falkenberg, Christian  and
      Sch{\"o}nw{\"a}lder, Erik  and
      Rietzke, Tom  and
      G{\"o}rner, Chris-Andris  and
      Walther, Robert  and
      Gonsior, Julius  and
      Reusch, Anja},
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.11",
    doi = "10.18653/v1/2023.semeval-1.11",
    pages = "88--93",
    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.",
}

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.
Falkenberg, Christian Sch{\"o}nw{\"a}lder, Erik Rietzke, Tom G{\"o}rner, Chris-Andris Walther, Robert Gonsior, Julius Reusch, Anja
Mr-Fosdick at SemEval-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation