Bibliographic record
Abstract
DEplain: A corpus for German Text SimplificationThis repository contains the corpus called DEplain-APA for German text simplification (document and sentence simplification). The corpus contains Austrian nexts text provided by the APA - Austria Presse Agentur eG. All of the sentence-wise aligned pairs (complex-simple) are manually aligned. The following table summarizes the most important meta data of the corpus. meta data value language DE-AT (Austrian German) domain news source language level B1 target language level A2 # document pairs (total, train/dev/test) 483 (387/48/48) # sentence pairs (total, train/dev/test) 13,122 (10,660/1,231/1,231) # complex sentences 25,607 # simple sentences 26,471 Updates: Version 1.1: Alignment Labels in Simplification Plans are repaired. For more info see https://github.com/rstodden/DEPlain/issues/2#issue-1875006089 For more information, please have a look at our paper. If you use this corpus, please also cite our paper and name APA - Austria Presse Agentur eG as data provider: Regina Stodden, Omar Momen, and Laura Kallmeyer. 2023. DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16441–16463, Toronto, Canada. Association for Computational Linguistics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.200 | 0.257 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".