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 1.2: More system outputs are added. For comparisons of your models with existing models, please have a look at ./DEPlain/G__Automatic_Text_Simplification_Experiments/generated_outputs/sentence-level. 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. For more information regarding available system outputs and comparisons between these models, please have a look at the following paper: Regina Stodden. 2024. Reproduction & Benchmarking of German Text Simplification Systems. In Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024, pages 1–15, Torino, Italia. ELRA and ICCL.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.145 | 0.155 |
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".