@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.",
}
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%0 Conference Proceedings
%T Mr-Fosdick at SemEval-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation
%A Falkenberg, Christian
%A Schönwälder, Erik
%A Rietzke, Tom
%A Görner, Chris-Andris
%A Walther, Robert
%A Gonsior, Julius
%A Reusch, Anja
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F falkenberg-etal-2023-mr
%X 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.
%R 10.18653/v1/2023.semeval-1.11
%U https://aclanthology.org/2023.semeval-1.11
%U https://doi.org/10.18653/v1/2023.semeval-1.11
%P 88-93
Markdown (Informal)
[Mr-Fosdick at SemEval-2023 Task 5: Comparing Dataset Expansion Techniques for Non-Transformer and Transformer Models: Improving Model Performance through Data Augmentation](https://aclanthology.org/2023.semeval-1.11) (Falkenberg et al., SemEval 2023)
ACL