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压缩模型用于共同引用分辨率:通过删除字嵌入来提高效率.

Georgios Ioannides1,2, Aishwarya Jadhav3, Aditi Sharma3

  • 1Language Technologies Institute, Carnegie Mellon University, Pittsburgh, 15213, USA. gioannid@alumni.andrew.cmu.edu.

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科学领域:

  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 机器学习 机器学习
  • 计算语言学 计算语言学

背景情况:

  • 词嵌入捕捉语义关系,但可以继承社会偏见,如性别偏见.
  • 现有的退化方法可能会影响下游任务中嵌入的实用性.

研究的目的:

  • 开发和评估一种全面的方法,以减少GloVe词嵌入中的性别偏见.
  • 评估 debiased 嵌入对自然语言处理 (NLP) 任务的影响,包括共同引用分辨率和文本分类.
  • 在资源效率高,压缩的NLP模型上研究脱皮化技术的有效性.

主要方法:

  • 通过识别和减少性别方向,两种 GloVe 嵌入变异 (840B 和 50) 被删除.
  • 用词嵌入协会测试量化性别偏见.
  • 在使用准确度指标的共同引用分辨率和文本分类任务上评估了性能.
  • 使用推特错误信息数据集分析了上下文保存.

主要成果:

  • 偏见的嵌入表明性别偏见减少.
  • 在debiased嵌入式上训练的模型在共同引用分辨率和文本分类中保持或提高了准确性.
  • 调整偏差技术甚至在压缩的NLP模型中也被证明是有效的.
  • 对上下文保护的分析表明了无源嵌入的实际实用性.

结论:

  • 对于NLP任务来说,对词嵌入的全面消除是可行的,也是有益的.
  • 偏差嵌入保留了对模型性能至关重要的语义信息.
  • 这项研究开创了压缩技术的应用到非现实NLP模型的先驱,为真实世界的应用提供了见解,如人形状分析.