Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models

TL;DR

This study evaluates how different tokenization strategies and vocabulary sizes affect Arabic language model performance across various NLP tasks, highlighting BPE with Farasa as the most effective approach.

Contribution

It provides a comprehensive analysis of tokenization and vocabulary impacts on Arabic NLP models, emphasizing morphological analysis and dialect challenges, which are less explored in prior work.

Findings

BPE with Farasa outperforms other tokenization strategies in multiple tasks.

Vocabulary size has limited impact on model performance.

Dialect-specific segmentation issues affect sentiment analysis accuracy.

Abstract

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.