HULAT at SemEval-2023 Task 10: Data augmentation for pre-trained transformers applied to the detection of sexism in social media

Isabel Segura-Bedmar
2/24/2023
cs.CLcs.AIcs.LG

Official code: isegura/hulat_edos (0 stars, last commit 2025-05-27).

Abstract

This paper describes our participation in SemEval-2023 Task 10, whose goal is the detection of sexism in social media. We explore some of the most popular transformer models such as BERT, DistilBERT, RoBERTa, and XLNet. We also study different data augmentation techniques to increase the training dataset. During the development phase, our best results were obtained by using RoBERTa and data augmentation for tasks B and C. However, the use of synthetic data does not improve the results for task C. We participated in the three subtasks. Our approach still has much room for improvement, especially in the two fine-grained classifications. All our code is available in the repository https://github.com/isegura/hulat_edos.

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isegura/hulat_edosOfficial100%

Repository of Hulat's participation in SemEval-2023 task 10, EDOS

01Jupyter NotebookFeb 16, 20231 years ago

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Cite this paper

@article{segurabedmar2023hulat,
  title  = {HULAT at SemEval-2023 Task 10: Data augmentation for pre-trained transformers applied to the detection of sexism in social media},
  author = {Isabel Segura-Bedmar},
  year   = {2023},
  eprint = {2302.12840},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.12840v2}
}

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