GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation

Zhijing Jin, Qipeng Guo, Xipeng Qiu, Zheng Zhang


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)
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2020.coling-main.217.pdf
Data
GenWikiE2ERoboCupWikiBio