Abstract
We present a new visual reasoning language dataset, containing 92,244 pairs of examples of natural statements grounded in synthetic images with 3,962 unique sentences. We describe a method of crowdsourcing linguistically-diverse data, and present an analysis of our data. The data demonstrates a broad set of linguistic phenomena, requiring visual and set-theoretic reasoning. We experiment with various models, and show the data presents a strong challenge for future research.- Anthology ID:
- P17-2034
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 217–223
- Language:
- URL:
- https://aclanthology.org/P17-2034
- DOI:
- 10.18653/v1/P17-2034
- Cite (ACL):
- Alane Suhr, Mike Lewis, James Yeh, and Yoav Artzi. 2017. A Corpus of Natural Language for Visual Reasoning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 217–223, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Corpus of Natural Language for Visual Reasoning (Suhr et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-2034.pdf
- Data
- NLVR, CLEVR, SHAPES