Enhancing the fairness of offensive memes detection models by mitigating unintended political bias
Author:
Funder
Wipro AI, India
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10844-023-00834-9.pdf
Reference80 articles.
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2. Arango, A., Perez-Martin, J., & Labrada, A. (2022). HateU at SemEval-2022 task 5: Multimedia automatic misogyny identification. In: Proceedings of the 16th international workshop on semantic evaluation (SemEval-2022). Association for Computational Linguistics, Seattle, United States, (pp. 581–584). https://doi.org/10.18653/v1/2022.semeval-1.80
3. Badjatiya, P., Gupta, M., & Varma, V. (2020). Stereotypical bias removal for hate speech detection task using knowledge-based generalizations. arXiv:2001.05495
4. Baldini, I., Wei, D., Natesan Ramamurthy, K., et al. (2022). Your fairness may vary: Pretrained language model fairness in toxic text classification. In: Findings of the association for computational linguistics: ACL 2022. Association for Computational Linguistics, Dublin, Ireland, (pp. 2245–2262). https://doi.org/10.18653/v1/2022.findings-acl.176
5. Bonchi, F., Castillo, C., & Ienco, D. (2013). Meme ranking to maximize posts virality in microblogging platforms. Journal of Intelligent Information Systems (pp. 120–136). https://doi.org/10.1007/s10844-011-0181-4
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