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
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-1644.pdf
- Code
- lil-lab/nlvr
- Data
- CLEVR, CLEVR-Humans, MS COCO, NLVR, Visual Question Answering