Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization
This work addresses a key problem in Arabic NLP for applications like text processing and machine translation, but it is incremental as it builds on existing approaches.
The paper tackles the challenge of missing diacritical marks in Arabic text by analyzing naturally occurring diacritics across six genres and introducing a new annotated dataset and extended algorithm, resulting in notable improvements in diacritization.
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.