25 citations · 32 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
Automated scoring of pre-REM sleep in mice with deep learning
Niklas Grieger, Justus T. C. Schwabedal, Stefanie Wendel +2
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems,…