Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale
G. Skitalinskaya, J. Klaff, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–1729.
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Skitalinskaya, Gabriella;
Klaff, Jonas;
Wachsmuth, HenningLibreCat
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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics
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1718-1729
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Skitalinskaya G, Klaff J, Wachsmuth H. Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. ; 2021:1718-1729.
Skitalinskaya, G., Klaff, J., & Wachsmuth, H. (2021). Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1718–1729).
@inproceedings{Skitalinskaya_Klaff_Wachsmuth_2021, title={Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale}, booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics}, author={Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning}, year={2021}, pages={1718–1729} }
Skitalinskaya, Gabriella, Jonas Klaff, and Henning Wachsmuth. “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale.” In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 1718–29, 2021.
G. Skitalinskaya, J. Klaff, and H. Wachsmuth, “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–1729.
Skitalinskaya, Gabriella, et al. “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale.” Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–29.
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