The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation

Chris van der Lee, Chris Emmery, Sander Wubben, Emiel Krahmer


Abstract
This paper describes the CACAPO dataset, built for training both neural pipeline and end-to-end data-to-text language generation systems. The dataset is multilingual (Dutch and English), and contains almost 10,000 sentences from human-written news texts in the sports, weather, stocks, and incidents domain, together with aligned attribute-value paired data. The dataset is unique in that the linguistic variation and indirect ways of expressing data in these texts reflect the challenges of real world NLG tasks.
Anthology ID:
2020.inlg-1.10
Volume:
Proceedings of the 13th International Conference on Natural Language Generation
Month:
December
Year:
2020
Address:
Dublin, Ireland
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–79
Language:
URL:
https://aclanthology.org/2020.inlg-1.10
DOI:
Bibkey:
Cite (ACL):
Chris van der Lee, Chris Emmery, Sander Wubben, and Emiel Krahmer. 2020. The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation. In Proceedings of the 13th International Conference on Natural Language Generation, pages 68–79, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation (van der Lee et al., INLG 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.inlg-1.10.pdf
Supplementary attachment:
 2020.inlg-1.10.Supplementary_Attachment.pdf
Data
RotoWireWebNLG