Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions

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


Abstract
In visual communication, text emphasis is used to increase the comprehension of written text to convey the author’s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author’s intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.
Anthology ID:
P19-1112
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1167–1172
Language:
URL:
https://aclanthology.org/P19-1112
DOI:
10.18653/v1/P19-1112
Bibkey:
Cite (ACL):
Amirreza Shirani, Franck Dernoncourt, Paul Asente, Nedim Lipka, Seokhwan Kim, Jose Echevarria, and Thamar Solorio. 2019. Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1167–1172, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions (Shirani et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1112.pdf
Code
 RiTUAL-UH/emphasis-2019