Semi-Supervised Consensus Labeling for Crowdsourcing

  title={Semi-Supervised Consensus Labeling for Crowdsourcing},
  author={Wei Tang},
Because individual crowd workers often exhibit high variance in annotation accuracy, we often ask multiple crowd workers to label each example to infer a single consensus label. While simple majority vote computes consensus by equally weighting each worker’s vote, weighted voting assigns greater weight to more accurate workers, where accuracy is estimated by inner-annotator agreement (unsupervised) and/or agreement with known expert labels (supervised). In this paper, we investigate the… CONTINUE READING
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