@inproceedings{shirani-etal-2020-choose,
title = "Let Me Choose: From Verbal Context to Font Selection",
author = "Shirani, Amirreza and
Dernoncourt, Franck and
Echevarria, Jose and
Asente, Paul and
Lipka, Nedim and
Solorio, Thamar",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.762",
doi = "10.18653/v1/2020.acl-main.762",
pages = "8607--8613",
abstract = "In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the only visual element in the document. We introduce a new dataset, containing examples of different topics in social media posts and ads, labeled through crowd-sourcing. Due to the subjective nature of the task, multiple fonts might be perceived as acceptable for an input text, which makes this problem challenging. To this end, we investigate different end-to-end models to learn label distributions on crowd-sourced data, to capture inter-subjectivity across all annotations.",
}
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%0 Conference Proceedings
%T Let Me Choose: From Verbal Context to Font Selection
%A Shirani, Amirreza
%A Dernoncourt, Franck
%A Echevarria, Jose
%A Asente, Paul
%A Lipka, Nedim
%A Solorio, Thamar
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F shirani-etal-2020-choose
%X In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the only visual element in the document. We introduce a new dataset, containing examples of different topics in social media posts and ads, labeled through crowd-sourcing. Due to the subjective nature of the task, multiple fonts might be perceived as acceptable for an input text, which makes this problem challenging. To this end, we investigate different end-to-end models to learn label distributions on crowd-sourced data, to capture inter-subjectivity across all annotations.
%R 10.18653/v1/2020.acl-main.762
%U https://aclanthology.org/2020.acl-main.762
%U https://doi.org/10.18653/v1/2020.acl-main.762
%P 8607-8613
Markdown (Informal)
[Let Me Choose: From Verbal Context to Font Selection](https://aclanthology.org/2020.acl-main.762) (Shirani et al., ACL 2020)
ACL
- Amirreza Shirani, Franck Dernoncourt, Jose Echevarria, Paul Asente, Nedim Lipka, and Thamar Solorio. 2020. Let Me Choose: From Verbal Context to Font Selection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8607–8613, Online. Association for Computational Linguistics.