SubRegWeigh：基于子词正则化的高效且有效的标注权重方法
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization
AI总结：
本文提出SubRegWeigh方法，利用子词正则化技术模拟多个错误检测模型，有效提升NLP数据集标注权重效率，速度比现有方法快四到五倍，并在文档分类和命名实体识别任务中提升性能。
摘要：
NLP数据集即使经过人工标注，仍可能包含标注错误。研究人员曾试图开发方法以自动减少数据集中错误的不利影响。然而，现有方法由于需要许多训练好的模型来检测错误，非常耗时。本文提出了一种省时的方法，利用一种称为子词正则化（subword regularization）的分词技术来模拟多个错误检测模型。我们提出的方法SubRegWeigh，执行标注加权的速度比现有方法快四到五倍。此外，SubRegWeigh在文档分类和命名实体识别任务中提升了性能。在使用伪错误标签的实验中，SubRegWeigh能将伪错误标签清晰地识别为标注错误。代码可在https://github.com/4ldk/SubRegWeigh获取。
英文摘要：
NLP datasets may still contain annotation errors, even when they are manually annotated. Researchers have attempted to develop methods to automatically reduce the adverse effect of errors in datasets. However, existing methods are time-consuming because they require many trained models to detect errors. This paper proposes a time-saving method that utilizes a tokenization technique called subword regularization to simulate multiple error detection models for detecting errors. Our proposed method, SubRegWeigh, can perform annotation weighting four to five times faster than the existing method. Additionally, SubRegWeigh improved performance in document classification and named entity recognition tasks. In experiments with pseudo-incorrect labels, SubRegWeigh clearly identifies pseudo-incorrect labels as annotation errors. Our code is available at https://github.com/4ldk/SubRegWeigh .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
