Koshur Diacritizer: A Byte-Level Sequence-to-Sequence Model for Kashmiri Diacritic Restoration

2026-06-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Koshur Diacritizer is a ByT5-small byte-level sequence-to-sequence model designed to restore diacritic marks in Kashmiri digital text, addressing the ambiguity caused by their frequent omission in the modified Perso-Arabic script. To facilitate this, a public dataset of 23.7k aligned undiacritized and diacritized Kashmiri sentence pairs has been released. The model's framework integrates script-aware normalization, alignment validation, and skeleton-preserving inference to ensure accurate restoration while preserving the original base-letter sequence. Experimental evaluations on a held-out test set demonstrated a DERm of 0.2012 and a WER of 0.2159. Furthermore, a native Kashmiri linguistic expert assessed the model, yielding a mean accuracy of 77.5%. The dataset, model, and source code are publicly available, establishing a reproducible baseline for future research in low-resource language NLP.

Key takeaway

For NLP Engineers and Research Scientists developing solutions for low-resource languages, particularly those with complex orthographies like Kashmiri, you should consider adopting byte-level sequence-to-sequence models. The Koshur Diacritizer's approach, combining script-aware normalization and skeleton-preserving inference, offers a robust framework for diacritic restoration. You can leverage the publicly released dataset and model as a strong baseline, accelerating your development and ensuring higher accuracy in text processing for similar linguistic challenges.

Key insights

Koshur Diacritizer is a ByT5-small model that restores Kashmiri diacritics using a new 23.7k sentence pair dataset.

Principles

• Script-aware normalization improves diacritic restoration.
• Skeleton-preserving inference maintains base-letter integrity.
• Public datasets are crucial for low-resource language NLP.

Method

The framework combines script-aware normalization, alignment validation, and skeleton-preserving inference to restore diacritics while maintaining the original base-letter sequence.

In practice

• Use ByT5-small for byte-level sequence tasks.
• Develop aligned datasets for low-resource languages.
• Incorporate expert linguistic evaluation for quality.

Topics

• Kashmiri Language
• Diacritic Restoration
• Low-Resource NLP
• ByT5-small
• Sequence-to-Sequence Models
• Perso-Arabic Script

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.