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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of The 13th International Conference on Natural Language Generation
Place of PublicationDublin, Ireland
Pages68-79
Number of pages15
Publication statusPublished - 1 Dec 2020
EventInternational Conference on Natural Language Generation - online, Dublin , Ireland
Duration: 15 Dec 202018 Dec 2020
Conference number: 13
https://www.inlg2020.org/

Conference

ConferenceInternational Conference on Natural Language Generation
Abbreviated titleINLG 2020
CountryIreland
CityDublin
Period15/12/2018/12/20
Internet address

Fingerprint Dive into the research topics of 'The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation'. Together they form a unique fingerprint.

Cite this