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Webis-CausalQA-22
- webis.de
- anthology.aicmu.ac.cn
7186761Updated 2022
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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 byBauhaus-Universität Weimar
The Web Technology & Information Systems Network
Universität Paderborn
Combinatorial Algebra Lab, Ural Federal University
Leipzig University and Friedrich Schiller University Jena
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 24, 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.7476615zipAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.7476615Dataset updatedDec 24, 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.
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FacebookTwitterEmailThe Web Technology & Information Systems Network
Universität Paderborn
Combinatorial Algebra Lab, Ural Federal University
Leipzig University and Friedrich Schiller University Jena
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.