Diacritic Restoration

Diacritic restoration aims to automatically add missing accent marks and other diacritical symbols to text, improving language processing and disambiguation. Recent research focuses on improving accuracy, particularly when dealing with noisy speech transcripts, using models like transformer networks (e.g., ByT5) and convolutional neural networks (CNNs), often incorporating parallel data sources for enhanced performance. These advancements are significant for applications requiring accurate text processing in languages using diacritics, impacting fields such as natural language processing, machine translation, and digital humanities.