Generating Quantified Referring Expressions with Perceptual Cost Pruning

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.