Hillary Harner


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Generating Quantified Referring Expressions with Perceptual Cost Pruning
Gordon Briggs | Hillary Harner
Proceedings of the 12th International Conference on Natural Language Generation

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.


Toward Fine-grained Annotation of Modality in Text
Aynat Rubinstein | Hillary Harner | Elizabeth Krawczyk | Daniel Simonson | Graham Katz | Paul Portner
Proceedings of the IWCS 2013 Workshop on Annotation of Modal Meanings in Natural Language (WAMM)