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Efficient Online Scalar Annotation with Bounded Support
June 04, 2018 ยท Declared Dead ยท ๐ Annual Meeting of the Association for Computational Linguistics
"No code URL or promise found in abstract"
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Authors
Keisuke Sakaguchi, Benjamin Van Durme
arXiv ID
1806.01170
Category
cs.CL: Computation & Language
Citations
49
Venue
Annual Meeting of the Association for Computational Linguistics
Last Checked
5 months ago
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.
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