Exploiting alignment in multiparallel corpora for applications in linguistics and language learning
Bibliographic record
Abstract
This thesis exploits the automatic identification of semantically corresponding units in parallel and multiparallel corpora, which is referred to as alignment.Multiparallel corpora are text collections of more than two languages that comprise reciprocal translations.The contributions of this thesis are threefold: First, we prepare a large multiparallel corpus by adding several layers of annotation and alignment.Annotation is first performed on each language individually, while alignment is applied to two or more languages.For the latter case, we use the term multilingual alignment.We show that word alignment on parallel corpora can improve language-specific annotation by means of disambiguation.First of all, I wish to thank my supervisor Martin Volk who guided me through the initial troubles, gave me room to realize my own ideas and had the necessary confidence in me to finish this big project of mine.I am likewise
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".