A Corpus for Reasoning about Natural Language Grounded in Photographs

Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, Yoav Artzi


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.
Anthology ID:
P19-1644
Original:
P19-1644v1
Version 2:
P19-1644v2
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6418–6428
Language:
URL:
https://aclanthology.org/P19-1644
DOI:
10.18653/v1/P19-1644
Bibkey:
Cite (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.
Cite (Informal):
A Corpus for Reasoning about Natural Language Grounded in Photographs (Suhr et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1644.pdf
Supplementary:
 P19-1644.Supplementary.pdf
Code
 lil-lab/nlvr +  additional community code
Data
CLEVRCLEVR-HumansCOCONLVRVisual Question Answering