Publication

CURED4NLG: A dataset for table-to-text generation

Pasricha, Nivranshu
; 
Arcan, Mihael
; 
Buitelaar, Paul
Thumbnail Image
Files
Thumbnail Image
CURED4NLG_Paper.pdf
Adobe PDF, 544.67 KB
Repository DOI
10.13025/szze-8850
Publication Date
2023
Keywords
Data-to-Text Generation, Table-to-Text Generation, Natural Language Generation, COVID19, WHO
Type
Conference Paper
Downloads
Show all metadata
Citation
Pasricha, Nivranshu, Arcan, Mihael, & Buitelaar, Paul. (2023). CURED4NLG: A dataset for table-to-text generation. Paper presented at the 4th Conference on Language, Data and Knowledge, Vienna, Austria, 12-15 September. https://doi.org/10.13025/szze-8850
Abstract
We introduce CURED4NLG, a dataset for the task of table-to-text generation focusing on the public health domain. The dataset consists of 280 pairs of tables and documents extracted from weekly epidemiological reports published by the World Health Organisation (WHO). The tables report the number of cases and deaths from COVID-19, while the documents describe global and regional updates in English text. Along with the releasing the dataset, we present outputs from three different baselines for the task of table-to-text generation. The first is based on a manually defined template and the other two on end-to-end transformer-based models. Our results suggest that end-to-end models can learn a templatelike structure of the reports to produce fluent sentences, but may contain many factual errors especially related to numerical values.
Funder
Science Foundation Ireland
European Regional Development Fund
Publisher
University of Galway
Publisher DOI
Rights
Attribution 4.0 International (CC BY 4.0)
cb
Collections
Data Science Institute (Conference Papers)