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arXiv · 1811.00491

A Corpus for Reasoning About Natural Language Grounded in Photographs

Alane Suhr·Stephanie Zhou·Ally Zhang·Iris Zhang·Huajun Bai·Yoav Artzi
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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.

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Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, Yoav Artzi. 2018-11-01. A Corpus for Reasoning About Natural Language Grounded in Photographs. https://arxiv.org/abs/1811.00491

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