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Domain-Weighted Majority Voting for Crowdsourcing.

Dapeng Tao, Jun Cheng, Zhengtao Yu

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    |July 12, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a novel method for crowdsourcing data labeling by learning annotator expertise. It addresses diverse annotator competence by adapting domain knowledge, improving label accuracy when ground truth is unknown.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Crowdsourcing labeling systems generate multiple inaccurate labels.
    • Majority Voting (MV) is optimal for equal annotator competence.
    • Diverse annotator competence necessitates weighted MV, but direct competence calculation is difficult.

    Purpose of the Study:

    • To develop a method for learning annotator weights for weighted MV.
    • To address the challenge of unknown ground-truth labels in crowdsourcing.
    • To improve the reliability of merged labels from diverse annotators.

    Main Methods:

    • Modeling annotator domain knowledge with different distributions.
    • Treating crowdsourcing as a domain adaptation problem.
    • Learning weights by matching source (annotator) domains with the target (ground-truth) domain.

    Main Results:

    • Demonstrated that target-domain labels can be estimated under mild conditions.
    • Theoretical and empirical analyses confirm the proposed method's effectiveness.
    • Significant performance gains observed on specific datasets.

    Conclusions:

    • The proposed domain adaptation approach effectively learns annotator weights for weighted MV.
    • This method enhances the accuracy of crowdsourced labels even without ground-truth data.
    • Exploiting annotator expertise through domain adaptation offers a robust solution for reliable data labeling.