Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization

TL;DR

This paper investigates naturally occurring Arabic diacritics in various genres, introduces a new dataset, and enhances diacritization methods to improve NLP performance by leveraging real-world diacritic patterns.

Contribution

It presents a novel dataset of real-world diacritized words, analyzes diacritic patterns across genres, and extends diacritization algorithms to utilize these patterns effectively.

Findings

Improved diacritization accuracy using real-world diacritic data

Identification of genre-specific diacritic patterns

Enhanced NLP performance on Arabic text

Abstract

The widespread absence of diacritical marks in Arabic text poses a significant challenge for Arabic natural language processing (NLP). This paper explores instances of naturally occurring diacritics, referred to as "diacritics in the wild," to unveil patterns and latent information across six diverse genres: news articles, novels, children's books, poetry, political documents, and ChatGPT outputs. We present a new annotated dataset that maps real-world partially diacritized words to their maximal full diacritization in context. Additionally, we propose extensions to the analyze-and-disambiguate approach in Arabic NLP to leverage these diacritics, resulting in notable improvements. Our contributions encompass a thorough analysis, valuable datasets, and an extended diacritization algorithm. We release our code and datasets as open source.