Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<strong>DEplain: A corpus for German Text Simplification</strong>This 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. <strong>meta data</strong> <strong>value</strong> 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 <strong>Updates:</strong> 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 <em>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</em>, pages 16441–16463, Toronto, Canada. Association for Computational Linguistics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.119 |
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 it