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ALEXIS at DialectSentEval Subtask 2 Retrieval-Augmented, Preference-Tuned Generation for Arabic Sentiment Swap

The ALEXIS team's system for Arabic sentiment swap ranked 1st of 15 submissions in the DialectSentEval Subtask 2 shared task at ArabicNLP 2026, reaching 0.9581 Sentiment Style Accuracy, 56.91 BLEU and 72.18 chrF, according to the paper by Momen Ibrahim, Mariam E. Hassib, Ahmed Mahmoud Salem and Marwan Torki. The system fine-tunes mT5 and refines it with Direct Preference Optimization under a reward balancing target-polarity probability against content preservation, then pools candidates from four generators plus a retrieval index and reranks and repairs them with polarity-flip lexicons. A dedicated source-polarity classifier reaching 97.3% accuracy, versus 74.3% for the off-the-shelf sentiment model used in the evaluation, added +0.155 Sentiment Style Accuracy, while generation-side work contributed +0.044.

read2 min views1 publishedOct 23, 2026
ALEXIS at DialectSentEval Subtask 2 Retrieval-Augmented, Preference-Tuned Generation for Arabic Sentiment Swap
Image: Aclanthology (auto-discovered)
Abstract

Sentiment swap is the task of rewriting a sentence so that it carries the opposite polarity while its topic stays the same. We present a system for Arabic sentiment swap built on two observations about the data. First, the swap is a local edit rather than a paraphrase: 91% of reference rewrites stay within two words of their source. Second, the polarity to swap away from has to be predicted, because the test data does not provide it. We therefore fine-tune mT5 and refine it with Direct Preference Optimization under a reward that balances target-polarity probability against content preservation. At inference we pool candidates from four generators, one of them a minimal-edit tagger, together with a retrieval index, rerank them with the same balance, and repair them with polarity-flip lexicons mined from the training set. Source polarity is supplied by a dedicated classifier that reaches 97.3% accuracy, against 74.3% for the off-the-shelf sentiment model that the evaluation itself uses. Every component was scored on the official leaderboard: generation-side work contributes +0.044 Sentiment Style Accuracy, and the dedicated polarity classifier a further +0.155. The final system reaches 0.9581 Sentiment Style Accuracy, 56.91 BLEU and 72.18 chrF, ranking 1st of 15 submissions.

- Anthology ID:
- 2026.arabicnlp-sharedtasks.188
- Volume:
- [Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks](https://aclanthology.org/volumes/2026.arabicnlp-sharedtasks/)
- Month:
- Venue:
- [ArabicNLP](https://aclanthology.org/venues/arabicnlp/)
- SIG:
- [SIGARAB](https://aclanthology.org/sigs/sigarab/)
- Publisher:
  • Association for Computational Linguistics
- Note:
- Pages:
  • 1467–1472
- Language:
- URL:
- [https://aclanthology.org/2026.arabicnlp-sharedtasks.188/](https://aclanthology.org/2026.arabicnlp-sharedtasks.188/)
- DOI:
- Cite (ACL):
- PDF:
- [https://aclanthology.org/2026.arabicnlp-sharedtasks.188.pdf](https://aclanthology.org/2026.arabicnlp-sharedtasks.188.pdf)
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