Corpus ID: 227230470

Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution

@inproceedings{Sun2020ImprovingHD,
  title={Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution},
  author={David Q. Sun and Hadas Kotek and C. Klein and Mayank Gupta and William Li and J. Williams},
  booktitle={COLING},
  year={2020}
}

David Q. Sun, Hadas Kotek, +3 authors J. Williams

Published in COLING 2020

Computer Science

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… CONTINUE READING

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