A Corpus for Reasoning about Natural Language Grounded in Photographs
Alane Suhr, Stephanie Zhou, Ally Quan Zhang, Iris Zhang, Huajun Bai, Yoav Artzi · 2019
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.* Contributed equally.† Work done as an undergraduate at Cornell University. 1 In parts of this paper, we use the term compositional differently than it is commonly used in linguistics to refer to reasoning that requires composition.This type of reasoning often manifests itself in highly compositional language.