WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments

Skye Morgan, Tharindu Ranasinghe, Marcos Zampieri

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    This paper addresses the identification of toxic, engaging, and fact-claiming comments on social media. We used the dataset made available by the organizers of the GermEval-2021 shared task containing over 3,000 manually annotated Facebook comments in German. Considering the relatedness of the three tasks, we approached the problem using large pre-trained transformer models and multitask learning. Our results indicate that multitask learning achieves performance superior to the more common single task learning approach in all three tasks. We submit our best systems to GermEval-2021 under the team name WLV-RIT.
    Original languageEnglish
    Title of host publicationProceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments
    Pages32-38
    DOIs
    Publication statusPublished - Sept 2021

    Bibliographical note

    This is an accepted manuscript of a paper published in Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments. This accepted manuscript is distributed under the terms of the Creative Commons Attribution License CC BY [https://creativecommons.org/licenses/by/4.0/], which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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