Conditional Text Generation

The VitaminC dataset contains more than 450,000 claim-evidence pairs for fact verification and factual consistent generation. Based on over 100,000 revisions to popular Wikipedia pages, and additional "synthetic" revisions.

WikiGraphs is a dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark with a subgraph from the Freebase knowledge graph. This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets.

This is a dataset of 3 English books which do not contain the letter "e" in them. This dataset includes all of "Gadsby" by Ernest Vincent Wright, all of "A Void" by Georges Perec, and almost all of "Eunoia" by Christian Bok (except for the single chapter that uses the letter "e" in it) This dataset is contributed as part of a paper titled "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio" to appear at COLING 2022. This dataset and the works within them are examples of Lipograms, which are works where a letter or string is systematically omitted. Lipograms are an example of hard-constrained writing.

A Filipino multi-modal language dataset for text+visual tasks. Consists of 351,755 Filipino news articles gathered from Filipino news outlets. Each entry contains: * body - Article text * title - Article title * website - Name of the news outlet * category - News category given by the news outlet * date - Date published * author - Article author * url - URL of the article * img_url - URL of the article image * img_path - Filename of the image in the dataset