  Automatically estimating the complexity of texts for readers has a variety of
applications, such as recommending texts with an appropriate complexity level
to language learners or supporting the evaluation of text simplification
approaches. In this paper, we present our submission to the Text Complexity DE
Challenge 2022, a regression task where the goal is to predict the complexity
of a German sentence for German learners at level B. Our approach relies on
more than 220,000 pseudo-labels created from the German Wikipedia and other
corpora to train Transformer-based models, and refrains from any feature
engineering or any additional, labeled data. We find that the
pseudo-label-based approach gives impressive results yet requires little to no
adjustment to the specific task and therefore could be easily adapted to other
domains and tasks.
