  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.
