  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.
