Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling
Bibliographic record
Abstract
Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity.Dialectal variants of morphologically rich languages suffer more as they tend to be more noisy and have less resources.In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging.We use multitask learning for joint morphological modeling for the features within two dialects, and as a knowledge-transfer scheme for crossdialectal modeling.We use adversarial training to learn dialect invariant features that can help the knowledge-transfer scheme from the high to low-resource variants.We work with two dialectal variants: Modern Standard Arabic (high-resource "dialect" 1 ) and Egyptian Arabic (low-resource dialect) as a case study.Our models achieve state-of-the-art results for both.Furthermore, adversarial training provides more significant improvement when using smaller training datasets in particular.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".