mlsanityData to 7 Oct 2026

SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers

arXiv 2304.03518 ↗First posted 7 Apr 2023cs.CL cs.AI cs.CYcited by 1

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Abstract (arXiv)

This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more important than ever to make social media safer and more accessible for women. Our approach consists of experimenting and finetuning BERT-based models and using a Majority Voting ensemble model that outperforms individual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319 for Task C.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 7 Apr 2023.

Builds on

14 of 22 references.

References · 14

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2 more
  1. 2019Multi-label Categorization of Accounts of Sexism using a Neural Frameworkcited by 19
  2. 2021Automatic Sexism Detection with Multilingual Transformer Modelscited by 9

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023AI-UPV at EXIST 2023 -- Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regimecited by 0