tum-nlp/sexism-socialmedia-balanced

---
license: cc-by-sa-4.0
---

## Citation

```
@inproceedings{rydelek-etal-2023-adamr,
title = "{A}dam{R} at {S}em{E}val-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning",
author = "Rydelek, Adam and
Dementieva, Daryna and
Groh, Georg",
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-1.190",
doi = "10.18653/v1/2023.semeval-1.190",
pages = "1371--1381",
abstract = "The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to combat class imbalance throughout the tasks using data augmentation and loss alteration techniques. We tackled the challenge by utilising ensembles of Transformer models trained on different datasets, which are tested to find the balance between performance and interpretability. This solution ranked us in the top 40{\%} of teams for each of the tracks.",
}

```

原始信息汇总

数据集信息概述

许可证

• 类型: CC-BY-SA-4.0

引用信息

• 标题: AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
• 作者: Rydelek, Adam; Dementieva, Daryna; Groh, Georg
• 编辑: Ojha, Atul Kr.; Doğruöz, A. Seza; Da San Martino, Giovanni; Tayyar Madabushi, Harish; Kumar, Ritesh; Sartori, Elisa
• 出版物: Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
• 日期: July 2023
• 地点: Toronto, Canada
• 出版者: Association for Computational Linguistics
• DOI: 10.18653/v1/2023.semeval-1.190
• 页码: 1371--1381
• 摘要: 本研究针对可解释的在线性别歧视检测任务，通过细粒度分类处理性别歧视案例，采用数据增强和损失调整技术解决类别不平衡问题。使用Transformer模型集合，在不同数据集上训练，以平衡性能与可解释性。该解决方案在各赛道中排名前40%。