标签修改和引导用于零射击跨语言仇恨言论检测.
Irina Bigoulaeva1, Viktor Hangya2, Iryna Gurevych1
1Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany.
概括
本研究使用跨语言转移学习解决了在低资源语言中检测仇恨言论的问题. 词嵌入和数据平衡等技术提高了多语言在线内容调节的模型性能.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 检测仇恨言论对于在线安全至关重要,特别是在各种语言之间.
- 低资源语言的有限标记数据阻碍了有效的仇恨言论检测系统.
- 数据集之间不一致的标签和定义使跨语言转移学习复杂化.
研究的目的:
- 通过跨语言转移学习,为低资源语言开发有效的仇恨言论检测.
- 为应对多语言仇恨言论检测数据稀缺和标签不一致所带来的挑战.
- 通过结合未标记的数据和处理标签不平衡来提高模型性能.
主要方法:
- 利用跨语言的词嵌入在源语言的培训模型中,并将其应用于目标语言.
- 使用一组模型架构,从未标记的目标语言数据中引导标签.
- 实施数据采样不足和采样过多的技术,以减轻标签不平衡.
主要成果:
- 在使用跨语言转移学习检测低资源语言的仇恨言论方面取得了良好的表现.
- 通过标签引导将未标记的数据纳入标签的有效性得到证明.
- 通过采样技术解决标签不平衡,显示了模型性能的显著改善.
结论:
- 跨语言转移学习是低资源环境中检测仇恨言论的可行方法.
- 解决数据稀缺和标签问题对于强大的多语言仇恨言论检测至关重要.
- 数据平衡技术有效地提高了不平衡的仇恨言论数据集中的模型性能.
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