Noor at BAREC Shared Task 2025: A Hybrid Transformer-Feature Architecture for Sentence-level Readability Assessment
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 my participation in the Sentence-level Readability Assessment, Strict track of the BAREC Shared Task 2025 (Elmadani et al., 2025a).Building upon prior work that fine-tuned pre-trained transformer models (Elmadani et al., 2025b), this work explores the impact of incorporating a rich set of handcrafted features on readability prediction performance.A total of 51 features were extracted from the BAREC corpus (Elmadani et al., 2025b), including morphological, lexical, and syntactic indicators, leveraging established computational linguistics tools.These features were integrated into a hybrid architecture that combines transformerbased contextual embeddings with dense layers for feature processing.To optimize performance, experiments included freezing strategies and gradual unfreezing, alongside architectural variations with additional classification layers.Among the tested models, the best performance was achieved with MARBERT, reaching a Quadratic Weighted Kappa (QWK) of 80.95% on the test set, and 83.1% on the blind test set.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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