学习标签平滑用于文本分类的标签
Han Ren1,2, Yajie Zhao3, Yong Zhang4
1Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou, China.
PeerJ. Computer science
|April 30, 2024
概括
对歧视意识的标签平滑通过自适应地分配软标签来改善深度学习模型. 这种方法提高了在文本分类任务中的模型稳定性和概括性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 深度学习模型受益于软标签而不是硬标签,以提高稳定性和概括性.
- 标准标签光滑分配统一的软标签,忽视语义标签差异.
- 现有的方法缺乏在培训期间标记语义的适应性.
研究的目的:
- 引入歧视意识的标签平滑,一种用于代优化的自适应方法.
- 通过考虑标签语义来提高模型规范化和校准.
- 提高深度学习模型在文本分类中的性能.
主要方法:
- 使用阳性和阴性样本来告知标签分发.
- 开发一种代学习方法,用于自适应软标签生成.
- 在深度学习培训中整合歧视意识的标签平滑.
主要成果:
- 在五个不同的文本分类数据集中证明了有效性.
- 在模型稳定性和通用性方面显著改进.
- 验证了自适应软标签分配在统一方法上的好处.
结论:
- 对歧视意识的标签平滑提供了一个更细致的方法来训练深度学习模型.
- 该方法有效地利用标签语义来提高性能.
- 这种适应性策略代表了强大的深度学习的有希望的进步.
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