@inproceedings{gatt-etal-2018-specificity,
title = "Specificity measures and reference",
author = "Gatt, Albert and
Mar{\'\i}n, Nicol{\'a}s and
Rivas-Gervilla, Gustavo and
S{\'a}nchez, Daniel",
editor = "Krahmer, Emiel and
Gatt, Albert and
Goudbeek, Martijn",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
month = nov,
year = "2018",
address = "Tilburg University, The Netherlands",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6562",
doi = "10.18653/v1/W18-6562",
pages = "492--502",
abstract = "In this paper we study empirically the validity of measures of referential success for referring expressions involving gradual properties. More specifically, we study the ability of several measures of referential success to predict the success of a user in choosing the right object, given a referring expression. Experimental results indicate that certain fuzzy measures of success are able to predict human accuracy in reference resolution. Such measures are therefore suitable for the estimation of the success or otherwise of a referring expression produced by a generation algorithm, especially in case the properties in a domain cannot be assumed to have crisp denotations.",
}
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%0 Conference Proceedings
%T Specificity measures and reference
%A Gatt, Albert
%A Marín, Nicolás
%A Rivas-Gervilla, Gustavo
%A Sánchez, Daniel
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F gatt-etal-2018-specificity
%X In this paper we study empirically the validity of measures of referential success for referring expressions involving gradual properties. More specifically, we study the ability of several measures of referential success to predict the success of a user in choosing the right object, given a referring expression. Experimental results indicate that certain fuzzy measures of success are able to predict human accuracy in reference resolution. Such measures are therefore suitable for the estimation of the success or otherwise of a referring expression produced by a generation algorithm, especially in case the properties in a domain cannot be assumed to have crisp denotations.
%R 10.18653/v1/W18-6562
%U https://aclanthology.org/W18-6562
%U https://doi.org/10.18653/v1/W18-6562
%P 492-502
Markdown (Informal)
[Specificity measures and reference](https://aclanthology.org/W18-6562) (Gatt et al., INLG 2018)
ACL
- Albert Gatt, Nicolás Marín, Gustavo Rivas-Gervilla, and Daniel Sánchez. 2018. Specificity measures and reference. In Proceedings of the 11th International Conference on Natural Language Generation, pages 492–502, Tilburg University, The Netherlands. Association for Computational Linguistics.