- W
Webis-CausalQA-22
- webis.de
- anthology.aicmu.ac.cn
7186761Updated 2022
Share
Facebook
Twitter
EmailClick to copy linkLink copiedCiteAlexander Bondarenko; Magdalena Wolska; Stefan Heindorf; Pavel Braslavski; Benno Stein; Matthias Hagen; Martin Potthast (2022). Webis-CausalQA-22 [Dataset]. http://doi.org/10.5281/zenodo.71867617186761Available download formatsUnique identifierhttps://doi.org/10.5281/zenodo.7186761Dataset updated2022Dataset provided byUniversität Paderborn
The Web Technology & Information Systems Network
Friedrich Schiller University Jena
Combinatorial Algebra Lab, Ural Federal University
Bauhaus-Universität Weimar
Leipzig University and Friedrich Schiller University Jena
University of Kassel, hessian.AI, and ScaDS.AIAuthorsAlexander Bondarenko; Magdalena Wolska; Stefan Heindorf; Pavel Braslavski; Benno Stein; Matthias Hagen; Martin PotthastLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe Webis Causal Question Answering 2022 (Webis-CausalQA-22) corpus comprises 1.1M causal question-answer pairs collected from the public QA datasets.
Webis Causal Question Answering 2022
- zenodo.org
- data.niaid.nih.gov
zipUpdated Dec 23, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteAlexander Bondarenko; Alexander Bondarenko; Magdalena Wolska; Stefan Heindorf; Stefan Heindorf; Lukas Blübaum; Axel-Cyrille Ngonga Ngomo; Axel-Cyrille Ngonga Ngomo; Benno Stein; Benno Stein; Pavel Braslavski; Pavel Braslavski; Matthias Hagen; Matthias Hagen; Martin Potthast; Martin Potthast; Magdalena Wolska; Lukas Blübaum (2022). Webis Causal Question Answering 2022 [Dataset]. http://doi.org/10.5281/zenodo.7186761zipAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.7186761Dataset updatedDec 23, 2022AuthorsAlexander Bondarenko; Alexander Bondarenko; Magdalena Wolska; Stefan Heindorf; Stefan Heindorf; Lukas Blübaum; Axel-Cyrille Ngonga Ngomo; Axel-Cyrille Ngonga Ngomo; Benno Stein; Benno Stein; Pavel Braslavski; Pavel Braslavski; Matthias Hagen; Matthias Hagen; Martin Potthast; Martin Potthast; Magdalena Wolska; Lukas BlübaumLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe Webis Causal Question Answering 2022 (Webis-CausalQA-22) corpus comprises 1.1M causal question-answer pairs collected from the public QA datasets. This dataset was developed to support the development of tailored approaches that can answer causal questions.
Overview:
The directory "input" contains the train and validation splits (used for evaluation), the directory "output" contains the evaluation results, and the directory "models" includes the fine-tuned checkpoints.
Not seeing a result you expected?
Learn how you can add new datasets to our index.
FacebookTwitterEmailThe Web Technology & Information Systems Network
Friedrich Schiller University Jena
Combinatorial Algebra Lab, Ural Federal University
Bauhaus-Universität Weimar
Leipzig University and Friedrich Schiller University Jena
University of Kassel, hessian.AI, and ScaDS.AI
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Webis Causal Question Answering 2022 (Webis-CausalQA-22) corpus comprises 1.1M causal question-answer pairs collected from the public QA datasets.