A Corpus for Reasoning About Natural Language Grounded in Photographs

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Authors Iris Zhang, Stephanie Zhou, Huajun Bai, Ally Zhang, Alane Suhr, Yoav Artzi
Journal/Conference Name ACL 2019 7
Paper Category
Paper 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.
Date of publication 2018
Code Programming Language Python
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