@inproceedings{arps-etal-2022-hhuplexity,
title = "{HHU}plexity at Text Complexity {DE} Challenge 2022",
author = {Arps, David and
Kels, Jan and
Kr{\"a}mer, Florian and
Renz, Yunus and
Stodden, Regina and
Petersen, Wiebke},
editor = {M{\"o}ller, Sebastian and
Mohtaj, Salar and
Naderi, Babak},
booktitle = "Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text",
month = sep,
year = "2022",
address = "Potsdam, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.germeval-1.5",
pages = "27--32",
abstract = "In this paper, we describe our submission to the {`}Text Complexity DE Challenge 2022{'} shared task on predicting the complexity of German sentences. We compare performance of different feature-based regression architectures and transformer language models. Our best candidate is a fine-tuned German Distilbert model that ignores linguistic features of the sentences. Our model ranks 7th place in the shared task.",
}
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<abstract>In this paper, we describe our submission to the ‘Text Complexity DE Challenge 2022’ shared task on predicting the complexity of German sentences. We compare performance of different feature-based regression architectures and transformer language models. Our best candidate is a fine-tuned German Distilbert model that ignores linguistic features of the sentences. Our model ranks 7th place in the shared task.</abstract>
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%0 Conference Proceedings
%T HHUplexity at Text Complexity DE Challenge 2022
%A Arps, David
%A Kels, Jan
%A Krämer, Florian
%A Renz, Yunus
%A Stodden, Regina
%A Petersen, Wiebke
%Y Möller, Sebastian
%Y Mohtaj, Salar
%Y Naderi, Babak
%S Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text
%D 2022
%8 September
%I Association for Computational Linguistics
%C Potsdam, Germany
%F arps-etal-2022-hhuplexity
%X In this paper, we describe our submission to the ‘Text Complexity DE Challenge 2022’ shared task on predicting the complexity of German sentences. We compare performance of different feature-based regression architectures and transformer language models. Our best candidate is a fine-tuned German Distilbert model that ignores linguistic features of the sentences. Our model ranks 7th place in the shared task.
%U https://aclanthology.org/2022.germeval-1.5
%P 27-32
Markdown (Informal)
[HHUplexity at Text Complexity DE Challenge 2022](https://aclanthology.org/2022.germeval-1.5) (Arps et al., GermEval 2022)
ACL
- David Arps, Jan Kels, Florian Krämer, Yunus Renz, Regina Stodden, and Wiebke Petersen. 2022. HHUplexity at Text Complexity DE Challenge 2022. In Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text, pages 27–32, Potsdam, Germany. Association for Computational Linguistics.