Abstract
In this paper we explain HIIG’s contribution to the shared task Text Complexity DE Challenge 2022. Our best-performing model for the task of automatically determining the complexity level of a German-language sentence is a combination of a transformer model and a classic feature-based model, which achieves a mapped root square mean error of 0.446 on the test data.- Anthology ID:
- 2022.germeval-1.3
- Volume:
- Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text
- Month:
- September
- Year:
- 2022
- Address:
- Potsdam, Germany
- Editors:
- Sebastian Möller, Salar Mohtaj, Babak Naderi
- Venue:
- GermEval
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15–20
- Language:
- URL:
- https://aclanthology.org/2022.germeval-1.3
- DOI:
- Cite (ACL):
- Hadi Asghari and Freya Hewett. 2022. HIIG at GermEval 2022: Best of Both Worlds Ensemble for Automatic Text Complexity Assessment. In Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text, pages 15–20, Potsdam, Germany. Association for Computational Linguistics.
- Cite (Informal):
- HIIG at GermEval 2022: Best of Both Worlds Ensemble for Automatic Text Complexity Assessment (Asghari & Hewett, GermEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.germeval-1.3.pdf
- Code
- hadiasghari/konvens22-shared-task
- Data
- TextComplexityDE