Iris Zhang
2019
A Corpus for Reasoning about Natural Language Grounded in Photographs
Alane Suhr
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Stephanie Zhou
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Ally Zhang
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Iris Zhang
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Huajun Bai
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Yoav Artzi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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.