Dialect and Variant Identification as a Multi-Label Classification Task: A Proposal Based on Near-Duplicate Analysis
Bibliographic record
Abstract
We argue that dialect identification should be treated as a multi-label classification problem rather than the single-class setting prevalent in existing collections and evaluations.In order to avoid extensive human re-labelling of the data, we propose an analysis of ambiguous near-duplicates in an existing collection covering four variants of French.We show how this analysis helps us provide multiple labels for a significant subset of the original data, therefore enriching the annotation with minimal human intervention.The resulting data can then be used to train dialect identifiers in a multi-label setting.Experimental results show that on the enriched dataset, the multi-label classifier produces similar accuracy to the single-label classifier on test cases that are unambiguous (single label), but it increases the macro-averaged F1score by 0.225 absolute (71% relative gain) on ambiguous texts with multiple labels.On the original data, gains on the ambiguous test cases are smaller but still considerable (+0.077 absolute, 20% relative gain), and accuracy on nonambiguous test cases is again similar in this case.This supports our thesis that modelling dialect identification as a multi-label problem potentially has a positive impact.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".