L3IA at AraSentEval 2026 Subtask 2: LLM-Based Multi-Step Pipeline for Arabic Sentiment Swap
Year: 2026
Type: conference-paper
Abstract: This paper describes our system submitted to the AraSentEval 2026 Shared Task, Subtask 2: Arabic Sentiment Swap.The task requires rewriting Arabic sentences to invert their sentiment polarity while preserving the core meaning.We propose a multi-step pipeline approach that uses large language models (LLMs).Our method decomposes the sentiment inversion problem into three stages: (1) sentiment expression extraction, where the model identifies all sentiment-bearing words and phrases in the input sentence; (2) opposite expression generation, where each identified expression is replaced by its semantic opposite; and (3) sentence reconstruction, where the final output is assembled to ensure grammatical correctness and natural fluency.Our system achieves 74.3% sentiment style accuracy, 27.22 BLEU, and 55.04 chrF on the official test set.
Institution: Sidi Mohamed Ben Abdellah University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166503839