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 .
@article{arxiv.2409.06216,
title = {SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization},
author = {Kohei Tsuji and Tatsuya Hiraoka and Yuchang Cheng and Tomoya Iwakura},
journal= {arXiv preprint arXiv:2409.06216},
year = {2025}
}