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 Language Processing tasks in recent years. Pretrained models usually differ i
n pretraining objectives, architectures, and datasets they are trained on which can affect downstream performance. In this contribution, we fine-tuned German BERT and German ELECTRA models to identify toxic (subtask 1), engaging (subtask 2), and fact-claiming comments (subtask 3) in Facebook data provided by the GermEval 2021 competition. We created ensembles of these models and investigated whether and how classification performance depends on the number of ensemble members and their composition. On out-of-sample data, our best ensemble achieved a macro-F1 score of 0.73 (for all subtasks), and F1 scores of 0.72, 0.70, and 0.76 for subtasks 1, 2, and 3, respectively.
Sentence-level text simplification is currently evaluated using both automated metrics and human evaluation. For automatic evaluation, a combination of metrics is usually employed to evaluate different aspects of the simplification. Flesch-Kincaid Gr
ade Level (FKGL) is one metric that has been regularly used to measure the readability of system output. In this paper, we argue that FKGL should not be used to evaluate text simplification systems. We provide experimental analyses on recent system output showing that the FKGL score can easily be manipulated to improve the score dramatically with only minor impact on other automated metrics (BLEU and SARI). Instead of using FKGL, we suggest that the component statistics, along with others, be used for posthoc analysis to understand system behavior.
