The WebNLG corpus comprises of sets of triplets describing facts (entities and relations between them) and the corresponding facts in form of natural language text. The corpus contains sets with up to 7 triplets each along with one or more reference texts for each set. 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. Initially, 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. The corpus is also used for a reverse task of triplets extraction. Versioning history of the dataset can be found here. Source: Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation Image Source: https://paperswithcode.com/paper/creating-training-corpora-for-nlg-micro/ It's also available here: https://huggingface.co/datasets/web_nlg Note: "The v3 release (release_v3.0_en, release_v3.0_ru) for the WebNLG2020 challenge also supports a semantic parsing task."
EventNarrative is a knowledge graph-to-text dataset from publicly available open-world knowledge graphs. EventNarrative consists of approximately 230,000 graphs and their corresponding natural language text.
ENT-DESC involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. Source: ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
a high-level explanation of the dataset characteristics We introduce WikiOFGraph, a novel large-scale, domain-diverse dataset synthesized by LLMs, ensuring superior graph-text consistency to advance general-domain graph-to-text generation. * explain motivations and summary of its content The scarcity of high-quality, general-domain G2T generation datasets restricts progress in the generaldomain G2T generation research. To address this issue, we introduce Wikipedia OntologyFree Graph-text dataset (WikiOFGraph), a new large-scale G2T dataset generated using a novel method that leverages Large Language Model (LLM) and Data-QuestEval. * potential use cases of the dataset General Domain Knowledge Graph-to-Text generation