FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to Identify Toxic, Engaging, & Fact-Claiming Comments

Christian Gawron, Sebastian Schmidt. FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to Identify Toxic, Engaging, & Fact-Claiming Comments. In Julian Risch, Anke Stoll, Lena Wilms, Michael Wiegand, editors, Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, GermEval@KONVENS 2021, Düsseldorf, Germany, September 6, 2021. pages 19-24, Association for Computational Linguistics, 2021. [doi]

Abstract

Abstract is missing.