Dutch Parallel Corpus: A Balanced Copyright-Cleared Parallel Corpus
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
This paper presents the Dutch Parallel Corpus, a high-quality parallel corpus for Dutch, French and English consisting of more than ten million words. The corpus contains five different text types and is balanced with respect to text type and translation direction. All texts included in the corpus have been cleared from copyright. We discuss the importance of parallel corpora in various research domains and contrast the Dutch Parallel Corpus with existing parallel corpora. The Dutch Parallel Corpus distinguishes itself from other parallel corpora by having a balanced composition and by its availability to the wide research community, thanks to its copyright clearance. All texts in the corpus are sentence-aligned and further enriched with basic linguistic annotations (lemmas and word class information). Approximately 25,000 words of the Dutch-English part have been manually aligned at the sub-sentential level. Rich metadata facilitates the navigability of the corpus and enables users to select the texts that satisfy their needs. The entire corpus is released as full texts in XML format and is also available via a web interface, which supports basic and complex search queries and presents the results as parallel concordances. The corpus will be distributed by the Flemish-Dutch Human Language Technology Agency ( TST-Centrale ).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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