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


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.

Publication DOI: https://doi.org/10.48550/arXiv.2108.00057
Additional Information: 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.
Last Modified: 27 Jun 2024 12:36
Date Deposited: 15 May 2023 10:32
Full Text Link: https://aclanth ... 21.germeval-1.5
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PURE Output Type: Conference contribution
Published Date: 2021-09
Authors: Morgan, Skye
Ranasinghe, Tharindu (ORCID Profile 0000-0003-3207-3821)
Zampieri, Marcos



Version: Accepted Version

License: Creative Commons Attribution

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