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        <title>Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements</title>
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    <name type="personal">
        <namePart type="given">Andrey</namePart>
        <namePart type="family">Savchenko</namePart>
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        <namePart type="given">Anton</namePart>
        <namePart type="family">Alekseev</namePart>
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    <name type="personal">
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        <namePart type="family">Kwon</namePart>
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    <name type="personal">
        <namePart type="given">Elena</namePart>
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    <name type="personal">
        <namePart type="given">Evgeny</namePart>
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    <name type="personal">
        <namePart type="given">Sergey</namePart>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Understanding image advertisements is a challenging task, often requiring non-literal interpretation. We argue that standard image-based predictions are insufficient for symbolism prediction. Following the intuition that texts and images are complementary in advertising, we introduce a multimodal ensemble of a state of the art image-based classifier, a classifier based on an object detection architecture, and a fine-tuned language model applied to texts extracted from ads by OCR. The resulting system establishes a new state of the art in symbolism prediction.</abstract>
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            <start>1886</start>
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%0 Conference Proceedings
%T Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements
%A Savchenko, Andrey
%A Alekseev, Anton
%A Kwon, Sejeong
%A Tutubalina, Elena
%A Myasnikov, Evgeny
%A Nikolenko, Sergey
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F savchenko-etal-2020-ad
%X Understanding image advertisements is a challenging task, often requiring non-literal interpretation. We argue that standard image-based predictions are insufficient for symbolism prediction. Following the intuition that texts and images are complementary in advertising, we introduce a multimodal ensemble of a state of the art image-based classifier, a classifier based on an object detection architecture, and a fine-tuned language model applied to texts extracted from ads by OCR. The resulting system establishes a new state of the art in symbolism prediction.
%R 10.18653/v1/2020.coling-main.171
%U https://aclanthology.org/2020.coling-main.171
%U https://doi.org/10.18653/v1/2020.coling-main.171
%P 1886-1892

Markdown (Informal)

[Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements](https://aclanthology.org/2020.coling-main.171) (Savchenko et al., COLING 2020)

• Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements (Savchenko et al., COLING 2020)

ACL

• Andrey Savchenko, Anton Alekseev, Sejeong Kwon, Elena Tutubalina, Evgeny Myasnikov, and Sergey Nikolenko. 2020. Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1886–1892, Barcelona, Spain (Online). International Committee on Computational Linguistics.