@inproceedings{briggs-2020-generating,
title = "Generating Quantified Referring Expressions through Attention-Driven Incremental Perception",
author = "Briggs, Gordon",
editor = "Davis, Brian and
Graham, Yvette and
Kelleher, John and
Sripada, Yaji",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.inlg-1.16",
doi = "10.18653/v1/2020.inlg-1.16",
pages = "107--112",
abstract = "We model the production of quantified referring expressions (QREs) that identity collections of visual items. A previous approach, called Perceptual Cost Pruning, modeled human QRE production using a preference-based referring expression generation algorithm, first removing facts from the input knowledge base based on a model of perceptual cost. In this paper, we present an alternative model that incrementally constructs a symbolic knowledge base through simulating human visual attention/perception from raw images. We demonstrate that this model produces the same output as Perceptual Cost Pruning. We argue that this is a more extensible approach and a step toward developing a wider range of process-level models of human visual description.",
}
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%0 Conference Proceedings
%T Generating Quantified Referring Expressions through Attention-Driven Incremental Perception
%A Briggs, Gordon
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F briggs-2020-generating
%X We model the production of quantified referring expressions (QREs) that identity collections of visual items. A previous approach, called Perceptual Cost Pruning, modeled human QRE production using a preference-based referring expression generation algorithm, first removing facts from the input knowledge base based on a model of perceptual cost. In this paper, we present an alternative model that incrementally constructs a symbolic knowledge base through simulating human visual attention/perception from raw images. We demonstrate that this model produces the same output as Perceptual Cost Pruning. We argue that this is a more extensible approach and a step toward developing a wider range of process-level models of human visual description.
%R 10.18653/v1/2020.inlg-1.16
%U https://aclanthology.org/2020.inlg-1.16
%U https://doi.org/10.18653/v1/2020.inlg-1.16
%P 107-112
Markdown (Informal)
[Generating Quantified Referring Expressions through Attention-Driven Incremental Perception](https://aclanthology.org/2020.inlg-1.16) (Briggs, INLG 2020)
ACL