Automatic readability assessment of german sentences with transformer ensembles

  • 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.

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Metadaten
Author:Patrick Gustav Blaneck, Tobias Bornheim, Niklas Grieger, Stephan BialonskiORCiD
DOI:https://doi.org/10.48550/arXiv.2209.04299
Parent Title (English):Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text
Publisher:Association for Computational Linguistics
Place of publication:Potsdam
Contributor:Salar Mohtaj, Babak Naderi, Sebastian Möller
Document Type:Conference Proceeding
Language:English
Year of Completion:2022
Date of the Publication (Server):2022/10/19
First Page:57
Last Page:62
Note:
Proceedings of the 18th Conference on Natural Language Processing / Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2022), 12-15 September, 2022, University of Potsdam, Potsdam, Germany
Link:https://www.doi.org/10.48550/arXiv.2209.04299
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik