Abstract
Data collection for the knowledge graph-to-text generation is expensive. As a result, research on unsupervised models has emerged as an active field recently. However, most unsupervised models have to use non-parallel versions of existing small supervised datasets, which largely constrain their potential. In this paper, we propose a large-scale, general-domain dataset, GenWiki. Our unsupervised dataset has 1.3M text and graph examples, respectively. With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.- Anthology ID:
- 2020.coling-main.217
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2398–2409
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.217
- DOI:
- 10.18653/v1/2020.coling-main.217
- Cite (ACL):
- Zhijing Jin, Qipeng Guo, Xipeng Qiu, and Zheng Zhang. 2020. GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2398–2409, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation (Jin et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.coling-main.217.pdf
- Data
- GenWiki, E2E, RoboCup, WikiBio