Let Me Choose: From Verbal Context to Font Selection

Amirreza Shirani, Franck Dernoncourt, Jose Echevarria, Paul Asente, Nedim Lipka, Thamar Solorio


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.
Anthology ID:
2020.acl-main.762
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8607–8613
Language:
URL:
https://aclanthology.org/2020.acl-main.762
DOI:
10.18653/v1/2020.acl-main.762
Bibkey:
Cite (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.
Cite (Informal):
Let Me Choose: From Verbal Context to Font Selection (Shirani et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.762.pdf
Video:
 http://slideslive.com/38929151
Code
 RiTUAL-UH/Font_LDL_2020 +  additional community code
Data
Short Text Font Dataset