This paper presents a comprehensive examination of the impact of tokenization strategies and vocabulary sizes on the performance of Arabic language models in downstream natural language processing tasks. Our investigation focused on the effectiveness of four tokenizers across various tasks, including News Classification, Hate Speech Detection, Sentiment Analysis, and Natural Language Inference. Leveraging a diverse set of vocabulary sizes, we scrutinize the intricate interplay between tokenization approaches and model performance. The results reveal that Byte Pair Encoding (BPE) with Farasa outperforms other strategies in multiple tasks, underscoring the significance of morphological analysis in capturing the nuances of the Arabic language. However, challenges arise in sentiment analysis, where dialect specific segmentation issues impact model efficiency. Computational efficiency analysis demonstrates the stability of BPE with Farasa, suggesting its practical viability. Our study uncovers limited impacts of vocabulary size on model performance while keeping the model size unchanged. This is challenging the established beliefs about the relationship between vocabulary, model size, and downstream tasks, emphasizing the need for the study of models' size and their corresponding vocabulary size to generalize across domains and mitigate biases, particularly in dialect based datasets. Paper's recommendations include refining tokenization strategies to address dialect challenges, enhancing model robustness across diverse linguistic contexts, and expanding datasets to encompass the rich dialect based Arabic. This work not only advances our understanding of Arabic language models but also lays the foundation for responsible and ethical developments in natural language processing technologies tailored to the intricacies of the Arabic language.

翻译：本文全面考察了标记化策略和词汇量大小对阿拉伯语语言模型在下游自然语言处理任务中性能的影响。研究聚焦于四种分词器在新闻分类、仇恨言论检测、情感分析和自然语言推理等任务中的有效性。通过采用多样化的词汇量大小设置，我们深入剖析了标记化方法与模型性能之间错综复杂的相互作用。结果显示，基于Farasa的字节对编码（Byte Pair Encoding, BPE）在多项任务中优于其他策略，凸显了形态学分析在捕捉阿拉伯语言细微差别方面的重要性。然而，情感分析任务面临挑战，其中方言特有的分词问题影响了模型效率。计算效率分析表明，基于Farasa的BPE具有稳定性，验证了其实用可行性。本研究发现，在保持模型大小不变的情况下，词汇量对模型性能的影响有限，这对传统认知中词汇量、模型规模与下游任务之间的关系提出了质疑，强调了需要研究模型规模及其对应词汇量，以便跨领域泛化并减少偏差，特别是在基于方言的数据集中。本文建议包括优化标记化策略以应对方言挑战、增强模型在不同语言语境中的鲁棒性，以及扩展数据集以涵盖丰富的阿拉伯方言变体。这项工作不仅深化了对阿拉伯语语言模型的理解，也为针对阿拉伯语言复杂性量身定制的自然语言处理技术的负责任和道德发展奠定了基础。