@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}
Markdown (Informal)
[Generating Quantified Referring Expressions through Attention-Driven Incremental Perception](https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.inlg-1.16/) (Briggs, INLG 2020)
ACL