HICL:用于社交媒体的hashtag驱动的上下文学习自然语言理解
IEEE transactions on neural networks and learning systems
|April 15, 2024
概括
这项研究引入了一个以标签驱动的上下文学习 (ICL) 框架,以改善对杂的社交媒体数据的自然语言理解 (NLU). 这种新的方法增强了上下文检索,大大提高了NLU的性能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 自然语言理解 (NLU) 模型因严重依赖上下文而难以处理短,杂的社交媒体文本.
- 现有的NLU方法在社交媒体内容上表现不佳,需要上下文丰富策略.
研究的目的:
- 提出一个新的标签驱动的上下文学习 (HICL) 框架,以加强社交媒体的NLU.
- 改进处理噪音和短文本数据的NLU模型的上下文丰富性.
主要方法:
- 开发了一个以hashtag驱动的上下文学习 (HICL) 框架,使用预训练的#Encoder模型.
- #Encoder使用标签和对比学习用于基于BERT的预训练,以纳入与主题相关的语义信息.
- 使用基于梯度的方法来识别用于将检索的上下文与源文本融合的触发术语.
主要成果:
- 该HICL框架大大提升了七个下游NLU任务的最新结果,使用了4500万条推特基准.
- 将源输入与来自#Encoder的顶部检索帖子结合起来,比使用语义上类似的帖子更有效.
- 触发词显著受益于源和检索帖子中的上下文的合并.
结论:
- 拟议的HICL框架有效地增强了社交媒体NLU,特别是在杂的环境中.
- 以hashtag驱动的上下文丰富和触发词集成是改善NLU在社交媒体数据上的表现的关键.
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