Identifying Parallel Documents from a Large Bilingual Collection of Texts: Application to Parallel Article Extraction in Wikipedia.
Bibliographic record
Abstract
While several recent works on dealing with large bilingual collections of texts, e.g. (Smith et al., 2010), seek for extracting parallel sentences from comparable corpora, we present PARADOCS, a system designed to recognize pairs of parallel documents in a (large) bilingual collection of texts. We show that this system outperforms a fair baseline (Enright and Kondrak, 2007) in a number of controlled tasks. We applied it on the French-English cross-language linked article pairs of Wikipedia in order see whether parallel articles in this resource are available, and if our system is able to locate them. According to some manual evaluation we conducted, a fourth of the article pairs in Wikipedia are indeed in translation relation, and PARADOCS identifies parallel or noisy parallel article pairs with a precision of 80%. 1
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".