3 citations · 5 across the 7 of their papers we have counts for
7 papers
AIxcellent Vibes at GermEval 2025 Shared Task on Candy Speech Detection: Improving Model Performance by Span-Level Training
Christian Rene Thelen, Patrick Gustav Blaneck, Tobias Bornheim +2
Positive, supportive online communication in social media (candy speech) has the potential to foster civility, yet automated detection of such language remains underexplored, limit…
Detecting Sexism in German Online Newspaper Comments with Open-Source Text Embeddings (Team GDA, GermEval2024 Shared Task 1: GerMS-Detect, Subtasks 1 and 2, Closed Track)
Florian Bremm, Patrick Gustav Blaneck, Tobias Bornheim +2
Sexism in online media comments is a pervasive challenge that often manifests subtly, complicating moderation efforts as interpretations of what constitutes sexism can vary among i…
Speaker attribution in German parliamentary debates with QLoRA-adapted large language models
Tobias Bornheim, Niklas Grieger, Patrick Gustav Blaneck +1
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automat…
Automatic Readability Assessment of German Sentences with Transformer Ensembles
Patrick Gustav Blaneck, Tobias Bornheim, Niklas Grieger +1
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large…
ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models
Hoai Nam Tran, Udo Kruschwitz
This paper describes our approach (ur-iw-hnt) for the Shared Task of GermEval2021 to identify toxic, engaging, and fact-claiming comments. We submitted three runs using an ensembli…
FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning
Tobias Bornheim, Niklas Grieger, Stephan Bialonski
The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Languag…
FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to Identify Toxic, Engaging, & Fact-Claiming Comments
Christian Gawron, Sebastian Schmidt
In this paper we describe the methods we used for our submissions to the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. For all thr…