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• W2995639198 abstract "We model the production of quantified referring expressions (QREs) that identify collections of visual items. To address this task, we propose a method of perceptual cost pruning, which consists of two steps: (1) determine what subset of quantity information can be perceived given a time limit t, and (2) apply a preference order based REG algorithm (e.g., the Incremental Algorithm) to this reduced set of information. We demonstrate that this method successfully improves the human-likeness of the IA in the QRE generation task and successfully models human-generated language in most cases." @default.
• W2995639198 created "2019-12-26" @default.
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• W2995639198 date "2019-01-01" @default.
• W2995639198 modified "2023-09-24" @default.
• W2995639198 title "Generating Quantified Referring Expressions with Perceptual Cost Pruning" @default.
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• W2995639198 doi "https://doi.org/10.18653/v1/w19-8602" @default.
• W2995639198 hasPublicationYear "2019" @default.
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