Codezone Research Group at AraSentEval Shared Task: Arabic Sentiment Swap beyond Negation Prepending, Benchmarking Multilingual T5 against Large Language Models on the MA’AKS Corpus
Year: 2026
Type: conference-paper
Abstract: We launched ASBN-MT5, the system for Arabic Sentiment Swap, which performs the task of inverting the sentiment of a sentence while keeping the meaning intact.This is a sequenceto-sequence task.We demonstrate ASBN-MT5: mT5, which is a MultiLingual T5 model, finetuned on the provided dataset of the AraSentEval 2026 Shared Task.We describe the data as the first of its kind for the Arabic language, as MAAKS is the first manually composed, parallel, cross-linguistic corpus for the Arabic language.With the preliminary results of Sentiment Flip for the task of Sentiment Inversion, we have recorded a rate of 59.5% for positive to negative conversions and 58.5% for negative to positive conversions, while maintaining an average similarity to the original sentences of 0.955.We present the Arabic prompts and a neuro-developmental (Deep Learning) recipe.Due to the evaluation criteria which include Exact Match, Flip Success, Surface Similarity, and Quality of Output, we restrict the use of Prepended Negation as the main technique and recommend the use of LLMs designed for the Arabic language in the near future.
Authors: Abdulkadir Shehu Bichi, Sarah Yassine
Institutions: Vivekananda Global University, Lebanese University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166534861