This paper describes our submission to NADI 2026 Subtask 1.1, speech recognition for eight Arabic dialects under noisy conditions. Our system adapts whisper-large-v3-turbo with one low-rank adapter per dialect, decodes with tuned beam search, and applies a normalization step that matches the spelling conventions of the reference transcriptions rather than applying generic orthographic rules. We train only on the shared-task data, which places our results well below those of systems built with large external corpora, and we report our numbers with that constraint in mind. Country-average WER falls from 0.804 for the zero-shot backbone to 0.6053 on the test set. We also report three observations that shaped the system: per-dialect adapters outperform a jointly trained adapter on all eight dialects even when the joint model is trained for twice as long; replacing beam search with temperature sampling in order to produce diverse hypotheses for rescoring costs more accuracy than the rescoring recovers; and because the official scorer normalizes hypothesis and reference identically, generic character-level normalization cannot help, whereas learning each dialect’s annotation convention from the training transcriptions does.

Sentiment swap asks a system to rewrite a sentence so that its polarity is inverted while everything else stays the same, two halves of an instruction that pull against each other. This paper describes how the Sabaa system navigates that trade-off for Arabic dialects in Subtask 2 of DialectSentEval 2026. Rather than asking one model for a single correct rewrite, we ask two fine-tuned Arabic sequence-to-sequence models to each propose several, then choose among the pooled proposals with a scoring function built from the signals the task grades us on. The system scores 49.07 BLEU, 69.33 chrF, and 0.7447 sentiment accuracy on the official test set. That last figure is the most informative result here: the system places 90.37% of its outputs at the polarity it was aiming for, and the shortfall traces to inferring each sentence’s original polarity, which the test data withholds. Measured against gold labels on development, that inference is correct only 74.07% of the time, and sixteen points of sentiment accuracy separate our submission from a perfect estimator.