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.
@article{arxiv.2012.04169,
title = {Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution},
author = {David Q. Sun and Hadas Kotek and Christopher Klein and Mayank Gupta and William Li and Jason D. Williams},
journal= {arXiv preprint arXiv:2012.04169},
year = {2020}
}
Comments
Conference Paper at COLING 2020: https://www.aclweb.org/anthology/2020.coling-main.316/