mlsanityData to 7 Oct 2026

Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning

arXiv 2211.06452 ↗First posted 11 Nov 2022cs.CL cs.LGcited by 1

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Abstract (arXiv)

The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection. However, prior works focus on concatenating data from multiple platforms, inherently adopting Empirical Risk Minimization (ERM) method. In this work, we address this challenge from the perspective of domain generalization objective. We design SCL-Fish, a supervised contrastive learning integrated meta-learning algorithm to detect abusive language on unseen platforms. Our experimental analysis shows that SCL-Fish achieves better performance over ERM and the existing state-of-the-art models. We also show that SCL-Fish is data-efficient and achieves comparable performance with the large-scale pre-trained models upon finetuning for the abusive language detection task.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 50 of its 91 references that are in the map. First posted on arXiv 11 Nov 2022.

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50 of 91 references.

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