We describe our participation in Subtask 2 of the DialectSentEval 2026 Shared Task, whose goal is to rewrite an Arabic dialectal sentence so its sentiment polarity is reversed while its meaning, entities, and dialect are preserved. Our system, SWAPPER (Sentiment-SWAPping with Parameter-Efficient adapteRs), is an ensemble of LoRA-adapted mBART-50 checkpoints in which the adapters are extended beyond attention into the feed-forward sub-layers, where the actual lexical rewriting is learned. Three design choices carried most of the weight: (i) a minimal Arabic instruction prefix that reserves encoder capacity for content; (ii) feed-forward LoRA; (iii) forcing the decoder’s beginning-of-sequence token to ar_AR so mBART-50 does not drift into other scripts. On the official blind test SWAPPER reaches Sentiment-Style Accuracy 0.6732, BLEU 41.38, and chrF 65.84. As a secondary contribution, we introduce the J-Gap (𝛥 J = J_tgt - J_src), a Jaccard-based copy-collapse diagnostic that cleanly separates genuine sentiment swappers from models that merely copy their input, and explains why superficially strong backbones such as AraT5v2 fail the task despite high surface metrics.