SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization
This addresses the issue of time-consuming error detection in NLP datasets for researchers, though it appears incremental as it builds on existing annotation weighing methods.
The paper tackles the problem of annotation errors in NLP datasets by proposing SubRegWeigh, a method that uses subword regularization to simulate multiple error detection models, resulting in a 4-5 times speedup over existing methods and improved performance in document classification and named entity recognition tasks.
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 .