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

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 proceeding › Conference contribution › Scientific › peer-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 language	English
Title of host publication	Proceedings of The 13th International Conference on Natural Language Generation
Place of Publication	Dublin, Ireland
Pages	68-79
Number of pages	15
Publication status	Published - 1 Dec 2020
Event	International Conference on Natural Language Generation - online, Dublin , Ireland Duration: 15 Dec 2020 → 18 Dec 2020 Conference number: 13 https://www.inlg2020.org/

Conference

Conference	International Conference on Natural Language Generation
Abbreviated title	INLG 2020
Country/Territory	Ireland
City	Dublin
Period	15/12/20 → 18/12/20
Internet address	https://www.inlg2020.org/

Access to Document

https://www.aclweb.org/anthology/2020.inlg-1.10.pdfLicence: CC BY

CACAPO dataset
van der Lee, C. (Creator), Emmery, C. (Creator), Wubben, S. (Creator) & Krahmer, E. (Creator), DataverseNL, 2 Aug 2022
DOI: 10.34894/libyhp, https://dataverse.nl/citation?persistentId=doi:10.34894/LIBYHP
Dataset

Cite this

@inproceedings{83492e02461647d88f16a6660d67a985,

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

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.",

author = "{van der Lee}, Chris and Chris Emmery and Sander Wubben and Emiel Krahmer",

year = "2020",

month = dec,

day = "1",

language = "English",

pages = "68--79",

booktitle = "Proceedings of The 13th International Conference on Natural Language Generation",

note = "International Conference on Natural Language Generation, INLG 2020 ; Conference date: 15-12-2020 Through 18-12-2020",

url = "https://www.inlg2020.org/",

}

van der Lee, C , Emmery, C, Wubben, S & Krahmer, E 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. Dublin, Ireland, pp. 68-79, International Conference on Natural Language Generation, Dublin , Ireland, 15/12/20. <https://www.aclweb.org/anthology/2020.inlg-1.10.pdf>

The CACAPO Dataset : A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation. / van der Lee, Chris ; Emmery, Chris; Wubben, Sander; Krahmer, Emiel.

Proceedings of The 13th International Conference on Natural Language Generation. Dublin, Ireland, 2020. p. 68-79.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - The CACAPO Dataset

T2 - International Conference on Natural Language Generation

AU - van der Lee, Chris

AU - Emmery, Chris

AU - Wubben, Sander

AU - Krahmer, Emiel

N1 - Conference code: 13

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 68

EP - 79

BT - Proceedings of The 13th International Conference on Natural Language Generation

CY - Dublin, Ireland

Y2 - 15 December 2020 through 18 December 2020

ER -

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

Abstract

Conference

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Datasets

CACAPO dataset

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