在社交媒体中使用快速学习方法识别攻击性语言
Leilei Su1, Yifan Peng2, Zezheng Wang3
1Department of Mathematics, Hainan University, Haikou, China.
概括
快速学习提高了在线攻击性语言的检测,超过了传统的微调方法. 这一进步有助于通过更好地识别有害的在线言论来促进情绪健康.
科学领域:
- 自然语言处理自然语言处理.
- 计算社会科学 计算社会科学
- 人工智能的人工智能
背景情况:
- 冒犯性语言对在线环境中的情感幸福构成威胁.
- 准确识别冒犯性语言对于内容调节和用户安全至关重要.
研究的目的:
- 引入和评估用于攻击性语言检测的快速学习方法.
- 将快速学习与传统微调方法的有效性进行比较.
主要方法:
- 开发了一种新的快速学习框架,用于识别攻击性语言.
- 在两个基准数据集上进行了实验:HatEval和OffensEval.
- 在完全监督的环境中评估业绩.
主要成果:
- 与微调相比,快速学习显示出更高的性能.
- 提出的方法在检测冒犯性语言方面取得了显著的改进.
- 在多个数据集中验证的有效性.
结论:
- 快速学习为识别冒犯性语言提供了比微调更有效的方法.
- 这种方法有助于提高在线安全和情感福祉.
- 进一步的研究可以探索各种NLP任务的快速学习.
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