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如何不同是不同的吗? 系统地识别NER数据集中的分布转移及其影响
Xue Li1, Paul Groth1
1Informatics Institute, University of Amsterdam, Science Park, Amsterdam, 1098 XH Netherlands.
概括
分布转移显著降低了自然语言处理 (NLP) 模型的性能. 这项研究系统地测量了NLP数据集中的输入和标签转移,揭示了性能下降和指导模型微调策略.
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
背景情况:
- 分布转移是NLP中众所周知的挑战,在NLP中,在一个数据域上训练的模型在另一个数据域上表现不佳.
- 现有的研究缺乏系统研究,以检测和量化分布转移对NLP任务执行的影响.
研究的目的:
- 系统地检测和测量两种类型的分布转移:输入转移和标签转移.
- 调查这些转变对各种NLP任务和数据集的模型性能的影响.
- 提供对NLP模型管道定义和微调要求的见解.
主要方法:
- 检测和测量输入和标签转移.
- 在三个不同的数据表示中进行评估.
- 对12个基准命名实体识别 (NER) 数据集的分析.
主要成果:
- 输入和标签的转移都会导致NLP模型的显著性能下降.
- 一个具体的例子显示,在对广泛数据集进行微调和对更窄数据集进行测试时,F1得分下降了63分.
- 变速测量与有效微调所需的数据量相关.
结论:
- 量化分布转移对于理解NLP模型可靠性至关重要.
- 变速测量可以为决定模型是否可用于现成模型或是否需要微调提供信息.
- 测量分布转移对于定义强大的NLP模型管道至关重要.
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