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.
German CheXpert Chest X-ray Radiology Report Labeler
Munich Institute of Biomedical Engineering, Technical University of Munich（慕尼黑工业大学慕尼黑生物医学工程研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；University Hospital, LMU Munich（慕尼黑大学附属医院）；Institute of Neuroradiology, University Hospital, LMU Munich（慕尼黑大学附属医院神经放射学研究所）
Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
Munich Institute of Biomedical Engineering, Technical University of Munich（慕尼黑工业大学慕尼黑生物医学工程研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；University Hospital, LMU Munich（慕尼黑大学附属医院）；Institute of Neuroradiology, University Hospital, LMU Munich（慕尼黑大学附属医院神经放射学研究所）
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Xiaomi Corporation（小米集团）
