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A Benchmark Study of Contrastive Learning for Arabic Social Meaning

arXiv · · Notable

NLP Arabic AI Research Benchmark Sentiment Analysis

Summary

This paper presents a benchmark study of contrastive learning (CL) methods applied to Arabic social meaning tasks like sentiment analysis and dialect identification. The study compares state-of-the-art supervised CL techniques against vanilla fine-tuning across a range of tasks. Results indicate that CL methods outperform vanilla fine-tuning in most cases and demonstrate data efficiency. Why it matters: This work highlights the potential of contrastive learning for improving performance in Arabic NLP, especially in low-resource scenarios.

Keywords

contrastive learning · Arabic NLP · social meaning · sentiment analysis · dialect identification

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From FusHa to Folk: Exploring Cross-Lingual Transfer in Arabic Language Models

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Arabic Language Models (LMs) are primarily pretrained on Modern Standard Arabic (MSA), with an expectation of transferring to diverse Arabic dialects for real-world applications. This work explores cross-lingual transfer in Arabic LMs using probing on three Natural Language Processing (NLP) tasks and representational similarity. The findings indicate that transfer is possible but disproportionate across dialects, with some evidence of negative interference in models trained to support all Arabic dialects. Why it matters: This research highlights crucial challenges for building robust Arabic AI systems that effectively handle the significant linguistic diversity of the Arab world.