A live demo with user-friendly interface and more models is available here (Irish and English).
Important notice#1: this tool is still at an early stage of development; it can generate text that is unnatural, and sometimes ungrammatical.
The tool is intended to generate seed Wikipedia pages in several languages for people to edit, and possibly upload on Wikipedia. Our generator is fully rule-based and open-source; its general quality is lower than that of the best large language models, but unlike them, it is extremely data and resource efficient. If you like rule-based NLG systems, you may want to check this one too.
- Click on Wikipedia_generator.ipynb above and then click on Open in Colab at the top of the next page.
- In Colab, run cell of Step 1 to download the resources needed (click the play button just below Step 1); it takes a couple of minutes to install.
- Run first cell of Step 2 to set parameters (click the play button just below Set parameters; see below for more about parameters).
- Run second cell of Step 2 to fetch DBpedia properties (click the play button just below Get DBpedia properties)
- Optional: select one or more properties in the resulting table using Ctrl+click on each desired property (if none specified, all will be generated).
- Run all 17 cells of Step 3 to generate a text (click the play button just below Step 3).
- Run the cell of Step 4 to show the text in a mock Wikipedia page (click the play button just below Show text).
The code essentially outputs 2 files, which can be downloaded from the file browser on the left (click the folder icon on the left edge if you don't see the browser):
- the output file, which contains the generated text (all_[languageID]_[split]_out.txt).
- a file that contains a ready-made prompt designed for ChatGPT, the top line for this task (GPT_prompt_[entityName].txt).
Basic parameters
name: the name of the entity you want to generate a text about, e.g. "Titanic" or "Barack Obama".
category: the category the entity belongs to; it is usually not needed but can help the generation process.
language: choose the output language; currently supported: English (EN) and Irish (GA).
Advanced parameters
triple_source: choose where to get the triples from, either the DBpedia Ontology (Ontology, generally more quality) or the Wikipedia infoboxes ('Infobox', generally more coverage).
ignore_properties: list here the properties (separated by a comma) that you don't want to generate (e.g. "width" often has bad values on DBpedia, so it's recommended to ignore it).
generate_intermediate_representations: Select 'no' if you're only interested in the output, 'yes' to get all intermediate linguistic representations in the native .str format.
split: set to 'test' for Wikipedia page generation.
Irish Wikipedia generation poster
Simon Mille, Pronesti M, Thomson C, Lorandi M, Fitzpatrick S, Huidrom R, Sabry M, O’Riordan A, Belz A. Filling gaps in wikipedia: Leveraging data-to-text generation to improve encyclopedic coverage of underrepresented groups. In Proceedings of the 17th International Natural Language Generation Conference: System Demonstrations 2024 Sep (pp. 16-19). Paper PDF. Best demo award at INLG 2024 in Tokyo, Japan! Best poster award at IVI 2025 in Maynooth, Ireland!
Simon Mille, Elaine Uí Dhonnchadha, Lauren Cassidy, Brian Davis, Stamatia Dasiopoulou, Anya Belz. 2023. Generating Irish Text with a Flexible Plug-and-Play Architecture. In Proceedings of the Second Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning (Pan-DL'23@EMNLP), pages 25--42, Singapore. Paper PDF
Simon Mille, Elaine Uí Dhonnchadha, Stamatia Dasiopoulou, Lauren Cassidy, Brian Davis, Anya Belz. 2023. DCU/TCD-FORGe at WebNLG’23: Irish rules!. In Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG@INLG), pages 87--92, Prague, Czech Republic. Paper PDF Runner-up system behind GPT on Irish text generation!
The project was funded by the European Union under the Marie Skłodowska-Curie grant agreement No 101062572 (M-FleNS).