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        <title>A Corpus for Reasoning about Natural Language Grounded in Photographs</title>
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        <namePart type="given">Alane</namePart>
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    <name type="personal">
        <namePart type="given">Stephanie</namePart>
        <namePart type="family">Zhou</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Iris</namePart>
        <namePart type="family">Zhang</namePart>
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    <name type="personal">
        <namePart type="given">Huajun</namePart>
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    <name type="personal">
        <namePart type="given">Yoav</namePart>
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            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.</abstract>
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%0 Conference Proceedings
%T A Corpus for Reasoning about Natural Language Grounded in Photographs
%A Suhr, Alane
%A Zhou, Stephanie
%A Zhang, Ally
%A Zhang, Iris
%A Bai, Huajun
%A Artzi, Yoav
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F suhr-etal-2019-corpus
%X We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.
%R 10.18653/v1/P19-1644
%U https://aclanthology.org/P19-1644
%U https://doi.org/10.18653/v1/P19-1644
%P 6418-6428

Markdown (Informal)

[A Corpus for Reasoning about Natural Language Grounded in Photographs](https://aclanthology.org/P19-1644) (Suhr et al., ACL 2019)

• A Corpus for Reasoning about Natural Language Grounded in Photographs (Suhr et al., ACL 2019)

ACL

• Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi. 2019. A Corpus for Reasoning about Natural Language Grounded in Photographs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6418–6428, Florence, Italy. Association for Computational Linguistics.