Automatic Readability Assessment of German Sentences with Transformer Ensembles
This work addresses readability assessment for German text, which is incremental as it applies existing transformer models to a specific language and task.
The paper tackled the problem of automatically assessing the readability of German sentences by using ensembles of fine-tuned transformer models like GBERT and GPT-2-Wechsel, combined with linguistic features, and achieved a root mean squared error of 0.435 on out-of-sample data.
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 language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0.435.