WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments
This work addresses content moderation challenges for social media platforms, but it is incremental as it applies existing multitask learning methods to a specific dataset.
The paper tackled the problem of identifying toxic, engaging, and fact-claiming comments on social media using a dataset of over 3,000 German Facebook comments, and found that multitask learning with transformers achieved superior performance compared to single-task learning across all three tasks.
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.