@inproceedings{benlahbib-etal-2026-l3ia,
title = "{L}3{IA} at {A}ra{S}ent{E}val 2026 Subtask 2: {LLM}-Based Multi-Step Pipeline for {A}rabic Sentiment Swap",
author = "Benlahbib, Abdessamad and
Alami, Hamza and
M{'}haouach, Mohamed and
Elyoussoufi, Kaouthar",
editor = "Al-Khalifa, Hend and
El-Haj, Mo and
Ezzini, Saad",
booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.osact-1.37/",
doi = "10.63317/4wtc4onqmfgo",
pages = "274--277",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T L3IA at AraSentEval 2026 Subtask 2: LLM-Based Multi-Step Pipeline for Arabic Sentiment Swap
%A Benlahbib, Abdessamad
%A Alami, Hamza
%A M’haouach, Mohamed
%A Elyoussoufi, Kaouthar
%Y Al-Khalifa, Hend
%Y El-Haj, Mo
%Y Ezzini, Saad
%S The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks
%D 2026
%8 May
%I Association for Computational Linguistics
%C Palma, Mallorca (Spain)
%F benlahbib-etal-2026-l3ia
%X 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.
%R 10.63317/4wtc4onqmfgo
%U https://aclanthology.org/2026.osact-1.37/
%U https://doi.org/10.63317/4wtc4onqmfgo
%P 274-277
Markdown (Informal)
[L3IA at AraSentEval 2026 Subtask 2: LLM-Based Multi-Step Pipeline for Arabic Sentiment Swap](https://aclanthology.org/2026.osact-1.37/) (Benlahbib et al., OSACT 2026)
ACL