KSAA2026-Fine-Tashkeel: Arabic Phonetic Annotation (Tashkeel) Evaluation and Multi-Model Comparison Tool

KSAA2026-Fine-Tashkeel is an evaluation tool for Arabic text phonetic annotation (Tashkeel/Diacritization). It supports comparative testing of multiple model architectures including Seq2Seq, Token Classification, Decoder-based LLM, and ASR, provides a ready-to-use application for the Windows platform, is associated with the KSAA-2026 shared task, and contributes to the development of Arabic NLP technology.

Arabic writing is consonant-dominant, and Tashkeel symbols indicate vowels and grammar, which are crucial for understanding and reading aloud. Modern texts often omit annotations, posing challenges for learners, speech systems, and automatic processing. Application scenarios of Tashkeel include language learning, TTS, ASR, religious texts, and children's education; manual annotation is time-consuming, so automatic technology has become a focus of NLP research.

Seq2Seq Models

A classic sequence transformation method, such as ByT5 (Byte-level T5), processes bytes directly without tokenization, which is suitable for the morphological characteristics of Arabic.

Token Classification Models

Treats Tashkeel as a sequence labeling task. Its advantages include task simplification, parallel decoding, and error localization. Representative models include BERT and ArabicBERT.

Decoder-based LLM

Such as the GPT series, which generates annotations autoregressively. Its advantages are strong context understanding and zero-shot capability, but the computational cost is high.

ASR-based Systems

A multimodal method that combines text and speech signals, and can use real speech data to verify the correctness of annotations.

Model Comparison Dimensions

• Accuracy: WER (Word Error Rate), DER (Diacritization Error Rate)
• Speed: Processing time per unit text
• Resource Consumption: Memory and computational requirements
• Robustness: Ability to handle noisy text and mixed languages

Datasets and Benchmarks

Based on the KSAA-2026 shared task dataset, which includes Modern Standard Arabic, Classical Arabic, multi-domain texts, and manually annotated reference data. Official benchmark results and evaluation code are provided.

Education Sector

• Provide annotated materials for learners
• Assist teachers in preparing resources
• Develop interactive learning applications

Publishing and Media

• Annotation of children's books
• Processing of religious texts
• Speechification of news texts

Speech Technology

• Preprocessing for TTS systems
• Evaluation of ASR systems
• Speech-assisted learning tools

Academic Research

• Compare the effects of different NLP architectures
• Explore multimodal methods
• Study the application of LLM in low-resource languages

1. Multi-Model Comparison: One-stop comparison of methods such as Seq2Seq, Token Classification, LLM, and ASR
2. Ready-to-Use Tool: Windows application lowers the threshold for non-technical users
3. Shared Task Association: Directly linked to the official KSAA-2026 task, with authoritative results
4. Open-Source and Extensible: Open-source code for easy extension and customization by researchers

KSAA2026-Fine-Tashkeel provides a comprehensive evaluation platform for Arabic phonetic annotation, helping to select solutions and provide benchmark data. With the advancement of LLM and multimodal technologies, the accuracy of Tashkeel tasks will improve, and this platform framework lays the foundation for future development.