CURED4NLG is designed as a resource to motivate further research into data-to-text generation.

For further details, see our paper / poster, presented at LDK 2023 – 4th Conference on Language, Data and Knowledge.

If you use this data, please cite the above paper.

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. Our results suggest that end-to-end models can learn a template-like structure of the reports to produce fluent sentences, but may contain many factual errors especially related to numerical values.