DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification
This addresses the problem of limited resources for German text simplification, enabling better training and evaluation of automatic systems, though it is incremental as it builds on existing methods for data collection and modeling.
The paper tackles the lack of high-quality parallel data for German text simplification by introducing DEplain, a dataset of professionally written and manually aligned simplifications, which includes approximately 650 document pairs and 15,000 sentence pairs across news and web domains, and shows that training a transformer-based model on this corpus yields promising results.
Text simplification is an intralingual translation task in which documents, or sentences of a complex source text are simplified for a target audience. The success of automatic text simplification systems is highly dependent on the quality of parallel data used for training and evaluation. To advance sentence simplification and document simplification in German, this paper presents DEplain, a new dataset of parallel, professionally written and manually aligned simplifications in plain German ("plain DE" or in German: "Einfache Sprache"). DEplain consists of a news domain (approx. 500 document pairs, approx. 13k sentence pairs) and a web-domain corpus (approx. 150 aligned documents, approx. 2k aligned sentence pairs). In addition, we are building a web harvester and experimenting with automatic alignment methods to facilitate the integration of non-aligned and to be published parallel documents. Using this approach, we are dynamically increasing the web domain corpus, so it is currently extended to approx. 750 document pairs and approx. 3.5k aligned sentence pairs. We show that using DEplain to train a transformer-based seq2seq text simplification model can achieve promising results. We make available the corpus, the adapted alignment methods for German, the web harvester and the trained models here: https://github.com/rstodden/DEPlain.