Efficient Online Scalar Annotation with Bounded Support

ACL 2018

Efficient Online Scalar Annotation with Bounded Support

Jan 27, 2021
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Abstract: We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Ef-ficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation. Authors: Keisuke Sakaguchi, Benjamin Van Durme (Johns Hopkins University)

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