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Webis-CausalQA-22
- webis.de
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 byCombinatorial Algebra Lab, Ural Federal University
Universit?t Paderborn
University of Kassel, hessian.AI, and ScaDS.AI
Friedrich Schiller University Jena
Leipzig University and Friedrich Schiller University Jena
The Web Technology & Information Systems Network
Bauhaus-Universit?t WeimarAuthorsAlexander 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.
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Webis Causal Question Answering 2022
- data.niaid.nih.gov
Updated Dec 23, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteAlexander Bondarenko; Magdalena Wolska; Stefan Heindorf; Lukas Blübaum; Axel-Cyrille Ngonga Ngomo; Benno Stein; Pavel Braslavski; Matthias Hagen; Martin Potthast (2022). Webis Causal Question Answering 2022 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7186760Dataset updatedDec 23, 2022Dataset provided byLeipzig University
Paderborn University
Bauhaus-Universität Weimar
Ural Federal University
Martin-Luther-Univeristät Halle-WittenbergAuthorsAlexander Bondarenko; Magdalena Wolska; Stefan Heindorf; Lukas Blübaum; Axel-Cyrille Ngonga Ngomo; Benno Stein; Pavel Braslavski; 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. 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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FacebookTwitterEmailUniversit?t Paderborn
University of Kassel, hessian.AI, and ScaDS.AI
Friedrich Schiller University Jena
Leipzig University and Friedrich Schiller University Jena
The Web Technology & Information Systems Network
Bauhaus-Universit?t Weimar
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.