用子词正则化快速识别标注错误，提速五倍还更准。
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization
- 用子词正则化模拟多模型检测，无需训练多个模型。
- 在文档分类和命名实体识别中性能提升，误标识别准确率高。
- 适合处理有标注噪声的NLP数据集，尤其适合资源有限的研究者。
NLP数据集即使经过人工标注也可能存在标注错误。现有方法虽能自动降低错误影响，但需训练多个模型进行错误检测，耗时较长。本文提出一种高效方法SubRegWeigh，利用子词正则化技术模拟多个错误检测模型，实现快速标注加权。实验表明，SubRegWeigh比现有方法快四到五倍，同时在文档分类和命名实体识别任务中表现更优。在伪错误标签实验中，SubRegWeigh能有效识别出伪错误标签为标注异常。代码已开源于https://github.com/4ldk/SubRegWeigh。
原文摘要 · Abstract (English)
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 .
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