DATASHI: A Parallel English-Tashlhiyt Corpus for Orthography Normalization and Low-Resource Language Processing

TL;DR

DATASHI introduces a parallel English-Tashlhiyt corpus to advance orthography normalization and NLP for low-resource Amazigh languages, enabling systematic evaluation and model analysis.

Contribution

It provides the first comprehensive parallel corpus for English-Tashlhiyt, supporting diverse NLP tasks and detailed analysis of model performance on orthographic normalization.

Findings

State-of-the-art LLMs improve with few-shot prompting on Tashlhiyt normalization

Gemini-2.5-Pro achieves lowest error rates and strong cross-lingual generalization

Analysis reveals model sensitivities to phonological features and orthographic variations

Abstract

DATASHI is a new parallel English-Tashlhiyt corpus that fills a critical gap in computational resources for Amazigh languages. It contains 5,000 sentence pairs, including a 1,500-sentence subset with expert-standardized and non-standard user-generated versions, enabling systematic study of orthographic diversity and normalization. This dual design supports text-based NLP tasks - such as tokenization, translation, and normalization - and also serves as a foundation for read-speech data collection and multimodal alignment. Comprehensive evaluations with state-of-the-art Large Language Models (GPT-5, Claude-Sonnet-4.5, Gemini-2.5-Pro, Mistral, Qwen3-Max) show clear improvements from zero-shot to few-shot prompting, with Gemini-2.5-Pro achieving the lowest word and character-level error rates and exhibiting robust cross-lingual generalization. A fine-grained analysis of edit operations - deletions, substitutions, and insertions - across phonological classes (geminates, emphatics, uvulars, and pharyngeals) further highlights model-specific sensitivities to marked Tashlhiyt features and provides new diagnostic insights for low-resource Amazigh orthography normalization.