Label Noise in Context
Michael J Desmond, Catherine Finegan‐Dollak, Jeff Boston, Matt Arnold · 2020
Label noise-incorrectly or ambiguously labeled training examples-can negatively impact model performance.Although noise detection techniques have been around for decades, practitioners rarely apply them, as manual noise remediation is a tedious process.Examples incorrectly flagged as noise waste reviewers' time, and correcting label noise without guidance can be difficult.We propose LNIC, a noise-detection method that uses an example's neighborhood within the training set to (a) reduce false positives and (b) provide an explanation as to why the example was flagged as noise.We demonstrate on several short-text classification datasets that LNIC outperforms the state of the art on measures of precision and F 0.5 -score.We also show how LNIC's training set context helps a reviewer to understand and correct label noise in a dataset.The LNIC tool lowers the barriers to label noise remediation, increasing its utility for NLP practitioners.* The first two authors contributed equally.sports fitness ⇒ Unexpected increase in • Why doesn't my stamina running ability seem to improve? • Is it possible for the libero • Is there a rule of thumb for to score points in setting running goals?volleyball?• How counter-productive would having two coaches be?