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English(EN) Cross-Platform Chinese Offensive Comment Detection via Dual-Threshold Hard Example Mining

新AI方法提升跨中文社交媒体的冒犯性评论检测能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的双阈值硬样本挖掘策略，以提高不同中文社交媒体平台上冒犯性评论检测模型的性能。该方法首先对RoBERTa模型进行微调，然后使用从预测置信度低的未标记语料库中识别出的一小部分手动标记的硬样本进行进一步调整。这种方法有效地解决了模型在微博、小红书、贴吧和知乎等不同平台部署时通常会出现的性能下降问题，并取得了显著的性能提升。 AI

影响 改进了内容审核任务的跨平台AI模型适应性。

排序理由 该集群包含一篇详细介绍AI模型适应性新方法的学术论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新AI方法提升跨中文社交媒体的冒犯性评论检测能力

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该集群包含一篇详细介绍AI模型适应性新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ruixing Ren, Junhui Zhao, Fangfang Wang ·

    跨平台中文冒犯性评论检测通过双阈值硬样本挖掘

    arXiv:2606.27629v1 Announce Type: cross Abstract: Cross-platform deployment of offensive comment detection for Chinese social media suffers performance degradation. The paper proposes a dual-threshold hard mining method to address this. First, the clean-Chinese-base RoBERTa is fi…

  2. arXiv cs.CL TIER_1 English(EN) · Fangfang Wang ·

    跨平台中文冒犯性评论检测通过双阈值硬样本挖掘

    Cross-platform deployment of offensive comment detection for Chinese social media suffers performance degradation. The paper proposes a dual-threshold hard mining method to address this. First, the clean-Chinese-base RoBERTa is finetuned on COLD to establish a binary baseline for…

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