Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution
David Q. Sun, Hadas Kotek, Christopher Klein, Mayank Gupta, William Ho Cheung Li, J. D. Williams · 2020
This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30% in comparison to other common labeling strategies.Importantly, this new approach to the labeling process, which we name Dynamic Automatic Conflict Resolution (DACR), does not require a ground truth dataset and is instead based on inter-project annotation inconsistencies.This makes DACR not only more accurate but also available to a broad range of labeling tasks.In what follows we present results from a text classification task performed at scale for a commercial personal assistant, and evaluate the inherent ambiguity uncovered by this annotation strategy as compared to other common labeling strategies.