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- W2951271148 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: Efficient 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." @default.
- W2951271148 created "2019-06-27" @default.
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- W2951271148 date "2018-06-04" @default.
- W2951271148 modified "2023-09-28" @default.
- W2951271148 title "Efficient Online Scalar Annotation with Bounded Support" @default.
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- W2951271148 doi "https://doi.org/10.48550/arxiv.1806.01170" @default.
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