BERT-Based Logits Ensemble Model for Gender Bias and Hate Speech Detection


Sanggeon Yun, Seungshik Kang, Hyeokman Kim, Journal of Information Processing Systems Vol. 19, No. 5, pp. 641-651, Oct. 2023  

https://doi.org/10.3745/JIPS.04.0287
Keywords: BERT Embedding Model, Gender Bias, Hate Speech Detection, Logistics Ensemble
Fulltext:

Abstract

Malicious hate speech and gender bias comments are common in online communities, causing social problems in our society. Gender bias and hate speech detection has been investigated. However, it is difficult because there are diverse ways to express them in words. To solve this problem, we attempted to detect malicious comments in a Korean hate speech dataset constructed in 2020. We explored bidirectional encoder representations from transformers (BERT)-based deep learning models utilizing hyperparameter tuning, data sampling, and logits ensembles with a label distribution. We evaluated our model in Kaggle competitions for gender bias, general bias, and hate speech detection. For gender bias detection, an F1-score of 0.7711 was achieved using an ensemble of the Soongsil-BERT and KcELECTRA models. The general bias task included the gender bias task, and the ensemble model achieved the best F1-score of 0.7166.


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Cite this article
[APA Style]
Yun, S., Kang, S., & Kim, H. (2023). BERT-Based Logits Ensemble Model for Gender Bias and Hate Speech Detection. Journal of Information Processing Systems, 19(5), 641-651. DOI: 10.3745/JIPS.04.0287.

[IEEE Style]
S. Yun, S. Kang, H. Kim, "BERT-Based Logits Ensemble Model for Gender Bias and Hate Speech Detection," Journal of Information Processing Systems, vol. 19, no. 5, pp. 641-651, 2023. DOI: 10.3745/JIPS.04.0287.

[ACM Style]
Sanggeon Yun, Seungshik Kang, and Hyeokman Kim. 2023. BERT-Based Logits Ensemble Model for Gender Bias and Hate Speech Detection. Journal of Information Processing Systems, 19, 5, (2023), 641-651. DOI: 10.3745/JIPS.04.0287.