# DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification

arXiv: https://arxiv.org/abs/2305.18939 · HTML: https://mlsanity.com/paper/2305.18939/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 72 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 21 of 21)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://mlsanity.com/paper/2005.00481/) (2020)
- [BLEU is Not Suitable for the Evaluation of Text Simplification](https://mlsanity.com/paper/1810.05995/) (2018)
- [Controllable Text Simplification with Explicit Paraphrasing](https://mlsanity.com/paper/2010.11004/) (2020)
- [EASSE: Easier Automatic Sentence Simplification Evaluation](https://mlsanity.com/paper/1908.04567/) (2019)
- [Semantic Structural Evaluation for Text Simplification](https://mlsanity.com/paper/1810.05022/) (2018)
- [Probabilistic FastText for Multi-Sense Word Embeddings](https://mlsanity.com/paper/1806.02901/) (2018)
- [Simple and Effective Text Simplification Using Semantic and Neural Methods](https://mlsanity.com/paper/1810.05104/) (2018)
- [On the Ethical Considerations of Text Simplification](https://mlsanity.com/paper/2204.09565/) (2022)
- [A Corpus for Automatic Readability Assessment and Text Simplification of German](https://mlsanity.com/paper/1909.09067/) (2019)
- [BiSECT: Learning to Split and Rephrase Sentences with Bitexts](https://mlsanity.com/paper/2109.05006/) (2021)
- [Subjective Assessment of Text Complexity: A Dataset for German Language](https://mlsanity.com/paper/1904.07733/) (2019)
- [Klexikon: A German Dataset for Joint Summarization and Simplification](https://mlsanity.com/paper/2201.07198/) (2022)
- [Exploiting Sentence Order in Document Alignment](https://mlsanity.com/paper/2004.14523/) (2020)
- [A New Aligned Simple German Corpus](https://mlsanity.com/paper/2209.01106/) (2022)
- [Vicinity-Driven Paragraph and Sentence Alignment for Comparable Corpora](https://mlsanity.com/paper/1612.04113/) (2016)

## Built on by (most cited first, 14 of 14)

- [VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models](https://mlsanity.com/paper/2411.04825/) (2024)
- [SiTSE: Sinhala Text Simplification Dataset and Evaluation](https://mlsanity.com/paper/2412.01293/) (2024)
- [Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition](https://mlsanity.com/paper/2502.11457/) (2025)
- [Data and Approaches for German Text simplification -- towards an Accessibility-enhanced Communication](https://mlsanity.com/paper/2312.09966/) (2023)
- [Images Speak Volumes: User-Centric Assessment of Image Generation for Accessible Communication](https://mlsanity.com/paper/2410.03430/) (2024)
- [German4All -- A Dataset and Model for Readability-Controlled Paraphrasing in German](https://mlsanity.com/paper/2508.17973/) (2025)
- [Facilitating Cognitive Accessibility with LLMs: A Multi-Task Approach to Easy-to-Read Text Generation](https://mlsanity.com/paper/2510.00662/) (2025)
- [Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?](https://mlsanity.com/paper/2404.06838/) (2024)
- [Exploring Automatic Text Simplification of German Narrative Documents](https://mlsanity.com/paper/2312.09907/) (2023)
- [EASSE-DE: Easier Automatic Sentence Simplification Evaluation for German](https://mlsanity.com/paper/2404.03563/) (2024)
- [Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities](https://mlsanity.com/paper/2507.01479/) (2025)
- [DETECT: Determining Ease and Textual Clarity of German Text Simplifications](https://mlsanity.com/paper/2510.22212/) (2025)
- [Text Simplification with Sentence Embeddings](https://mlsanity.com/paper/2510.24365/) (2025)
- [PlainMedScale: A Corpus of Multi-Level Simplified Medical Texts in German and English](https://mlsanity.com/paper/2608.01158/) (2026)

## Abstract (arXiv)

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.