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.

本研究通过考察不同的分词策略和词汇量对阿拉伯语言模型在自然语言处理任务中的表现影响，全面探究了其对新闻分类、仇恨言论检测、情感分析和自然语言推理等多个任务的有效性。结果显示，Farasa 的字节对编码 (BPE) 在多个任务中表现优于其他策略，突显了形态分析在捕捉阿拉伯语言细微差异方面的重要性。然而，在情感分析中存在方言特定的分割问题，影响模型效率。计算效率分析验证了 Farasa 的 BPE 的稳定性，表明其实际可行性。研究发现，词汇量对模型性能的影响有限，在不改变模型大小的前提下。这挑战了关于词汇量、模型大小和下游任务关系的既有信念，强调了对模型大小及其对应词汇量进行研究以在不同领域泛化和减少偏差的重要性，尤其是在基于方言的数据集中。本文建议改进分词策略以解决方言挑战，增强模型在不同语境中的鲁棒性，并扩大数据集以涵盖丰富的基于方言的阿拉伯语言。这项研究不仅推进了对阿拉伯语言模型的理解，而且为针对阿拉伯语言的复杂性定制的自然语言处理技术的负责和道德性发展奠定了基础。