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The test set is split into two parts: seen, containing inputs created for entities and relations belonging to DBpedia categories that were seen in the training data, and unseen, containing inputs extracted for entities and relations belonging to 5 unseen categories.\r\n\r\nInitially, the dataset was used for the WebNLG natural language generation challenge which consists of mapping the sets of triplets to text, including referring expression generation, aggregation, lexicalization, surface realization, and sentence segmentation.\r\nThe corpus is also used for a reverse task of triplets extraction.\r\n\r\nVersioning history of the dataset can be found [here](https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/).\r\n\r\nSource: [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://arxiv.org/abs/1904.03396)\r\nImage Source: [https://paperswithcode.com/paper/creating-training-corpora-for-nlg-micro/](https://paperswithcode.com/paper/creating-training-corpora-for-nlg-micro/)\r\n\r\nIt's also available here: https://huggingface.co/datasets/web_nlg\r\nNote: \"The v3 release (release_v3.0_en, release_v3.0_ru) for the WebNLG2020 challenge also supports a semantic parsing task.\"","description_withheld":null,"homepage":"https://webnlg-challenge.loria.fr/","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/creating-training-corpora-for-nlg-micro","title":"Creating Training Corpora for NLG Micro-Planners","first_author":"Claire Gardent","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://gitlab.com/shimorina/webnlg-dataset"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Data-to-Text Generation","url":"/task/data-to-text-generation","datasets_with_task":"/datasets/task/data-to-text-generation"},{"name":"Joint Entity and Relation Extraction","url":"/task/joint-entity-and-relation-extraction","datasets_with_task":"/datasets/task/joint-entity-and-relation-extraction"},{"name":"KG-to-Text Generation","url":"/task/kg-to-text","datasets_with_task":"/datasets/task/kg-to-text"},{"name":"Table-to-Text Generation","url":"/task/table-to-text-generation","datasets_with_task":"/datasets/task/table-to-text-generation"},{"name":"Unsupervised KG-to-Text Generation","url":"/task/unsupervised-kg-to-text-generation","datasets_with_task":"/datasets/task/unsupervised-kg-to-text-generation"},{"name":"Graph-to-Sequence","url":"/task/graph-to-sequence","datasets_with_task":"/datasets/task/graph-to-sequence"},{"name":"Unsupervised semantic parsing","url":"/task/unsupervised-semantic-parsing","datasets_with_task":"/datasets/task/unsupervised-semantic-parsing"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["WebNLG 3.0","WebNLG 2.0 (Unconstrained)","WebNLG 2.0 (Constrained)","WebNLG (Unseen)","WebNLG (Seen)","WebNLG (All)","WebNLG (Constrained)","WebNLG(C)","WebNLG(U)","WebNLG en","WebNLG v2.1","WebNLG Full","WebNLG"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/webnlg-challenge/web_nlg","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/web_nlg","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/web_nlg","frameworks":["tf","jax"]}],"num_papers_in_archive":149,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/data-to-text-generation-on-webnlg","task":"Data-to-Text Generation","dataset_variant":"WebNLG","rows":20,"metrics":["BLEU","METEOR","Number of parameters (M)","FactSpotter","BLEU-4","ROUGE-L"],"first_row_in_archive_order":{"model":"Control Prefixes (A1, T5-large)","paper":"/paper/control-prefixes-for-text-generation","metrics":{"BLEU":"67.32"},"code_links":[{"title":"Yale-LILY/dart","url":"https://github.com/Yale-LILY/dart"},{"title":"jordiclive/ControlPrefixes","url":"https://github.com/jordiclive/ControlPrefixes"}]},"note":"rows are the archive's own order at snapshot; 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