A Corpus for Automatic Readability Assessment and Text Simplification of German

Alessia Battisti, Dominik Pfütze, Andreas Säuberli, Marek Kostrzewa, Sarah Ebling


Abstract
In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification for German, the first of its kind for this language. The corpus is compiled from web sources and consists of parallel as well as monolingual-only (simplified German) data amounting to approximately 6,200 documents (nearly 211,000 sentences). As a unique feature, the corpus contains information on text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and images (content, position, and dimensions). While the importance of considering such information in machine learning tasks involving simplified language, such as readability assessment, has repeatedly been stressed in the literature, we provide empirical evidence for its benefit. We also demonstrate the added value of leveraging monolingual-only data for automatic text simplification via machine translation through applying back-translation, a data augmentation technique.
Anthology ID:
2020.lrec-1.404
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3302–3311
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.404
DOI:
Bibkey:
Cite (ACL):
Alessia Battisti, Dominik Pfütze, Andreas Säuberli, Marek Kostrzewa, and Sarah Ebling. 2020. A Corpus for Automatic Readability Assessment and Text Simplification of German. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 3302–3311, Marseille, France. European Language Resources Association.
Cite (Informal):
A Corpus for Automatic Readability Assessment and Text Simplification of German (Battisti et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.404.pdf
Data
Newsela