A Transfer Learning Based Model for Text Readability Assessment in German
This work addresses text complexity assessment for German, benefiting language learners and people with disabilities, but it is incremental as it applies an existing method to a new language.
The paper tackles the problem of assessing text readability in German by proposing a transfer learning model, which outperforms classical linguistic feature-based methods with a Root Mean Square Error of 0.483.
Text readability assessment has a wide range of applications for different target people, from language learners to people with disabilities. The fast pace of textual content production on the web makes it impossible to measure text complexity without the benefit of machine learning and natural language processing techniques. Although various research addressed the readability assessment of English text in recent years, there is still room for improvement of the models for other languages. In this paper, we proposed a new model for text complexity assessment for German text based on transfer learning. Our results show that the model outperforms more classical solutions based on linguistic features extraction from input text. The best model is based on the BERT pre-trained language model achieved the Root Mean Square Error (RMSE) of 0.483.