New Arabic sentence segmentation corpus challenges LLMs, favors lightweight models

Researchers have developed AraSEG, a new corpus designed to improve sentence segmentation for Arabic text, which is often challenging due to inconsistent punctuation. The corpus spans eight genres and various punctuation conditions to test model robustness. Experiments using AraSEG showed that lightweight encoder models and dependency parser-based models outperformed large language models (LLMs) in difficult segmentation scenarios. The study also found that while increasing training data size can improve performance, cross-genre generalization remains a challenge, though accurate segmentation significantly benefits downstream tasks like dependency parsing. AI

IMPACT This research could lead to more robust NLP tools for Arabic, improving downstream tasks like dependency parsing and information extraction.

RANK_REASON The cluster contains an academic paper detailing a new corpus and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

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• Arabic
• AraSEG
• arXiv
• Bashar Alhafni
• dependency parser-based models
• encoder models
• Hugging Face
• LLMs

• paper
• other

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