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    <titleInfo>
        <title>Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions</title>
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    <name type="personal">
        <namePart type="given">Amirreza</namePart>
        <namePart type="family">Shirani</namePart>
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        <namePart type="given">Franck</namePart>
        <namePart type="family">Dernoncourt</namePart>
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        <namePart type="given">Paul</namePart>
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    <name type="personal">
        <namePart type="given">Nedim</namePart>
        <namePart type="family">Lipka</namePart>
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    <name type="personal">
        <namePart type="given">Seokhwan</namePart>
        <namePart type="family">Kim</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Jose</namePart>
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    <name type="personal">
        <namePart type="given">Thamar</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1112</identifier>
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        <date>2019-jul</date>
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%0 Conference Proceedings
%T Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
%A Shirani, Amirreza
%A Dernoncourt, Franck
%A Asente, Paul
%A Lipka, Nedim
%A Kim, Seokhwan
%A Echevarria, Jose
%A Solorio, Thamar
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 jul
%I Association for Computational Linguistics
%C Florence, Italy
%F shirani-etal-2019-learning
%X 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.
%R 10.18653/v1/P19-1112
%U https://aclanthology.org/P19-1112
%U https://doi.org/10.18653/v1/P19-1112
%P 1167-1172

Markdown (Informal)

[Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions](https://aclanthology.org/P19-1112) (Shirani et al., ACL 2019)

• Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions (Shirani et al., ACL 2019)

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.