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.
| Original language | English (Ireland) |
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| Title of host publication | LDK 2023 - 4th Conference on Language, Data and Knowledge |
| Place of Publication | Vienna, Austria |
| DOIs | |
| Publication status | Published - 1 Aug 2023 |
Authors (Note for portal: view the doc link for the full list of authors)
- Authors
- Pasricha, N; Arcan, M; Buitelaar P