Year: 2025
Type: conference-paper
Abstract: We present a simple, model-agnostic postprocessing technique for fine-grained Arabic readability classification in the BAREC 2025 Shared Task (19 ordinal levels).Our method applies conformal prediction to generate prediction sets with coverage guarantees, then computes weighted averages using softmaxrenormalized probabilities over the conformal sets.This uncertainty-aware decoding improves Quadratic Weighted Kappa (QWK) by reducing high-penalty misclassifications to nearer levels.Our approach shows consistent QWK improvements of 1-3 points across different base models.In the strict track, our submission achieves QWK scores of 84.9%(test) and 85.7% (blind test) for sentence level, and 73.3% for document level.For Arabic educational assessment, this enables human reviewers to focus on a handful of plausible levels, combining statistical guarantees with practical usability.
Author: Ahmed Abdou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037655