Year: 2026
Type: conference-paper
Abstract: Sentiment swap is a controlled text generation task that rewrites a sentence by inverting its sentiment polarity while preserving semantic content and fluency.In this paper, we present our system for AraSentEval 2026 Subtask 2 on Arabic sentiment swap, a particularly challenging problem due to Arabic's rich morphology and dialectal variation.We investigate multiple modeling paradigms, including encoder-decoder and multilingual approaches, and propose an enhanced system that combines targeted data augmentation and ensemble learning.Specifically, we augment underrepresented dialectal patterns to improve robustness and ensemble two Arabic-focused sequence-to-sequence models, AraBART and AraT5v2.Experiments are conducted on the MA'aks parallel dataset under fine-tuned settings.Our system ranked first in AraSentEval 2026 Subtask 2, achieving a BLEU score of 43.0, chrF of 65.36, and sentiment preservation accuracy of 0.7554.The results demonstrate that dialect-aware augmentation together with model ensembling substantially improves sentiment-controlled generation in Arabic and establishes strong baselines for future research in low-resource sentiment manipulation.
Institution: Nile University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166560394