Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

O96a/sudanese-mt-benchmark

Domain:

natural language processing

Record type:

dataset
Creator:
O96a
Host:
This benchmark addresses a documented gap in Arabic NLP research: the exclusion of Sudanese Arabic from dialectal machine translation evaluation. Recent research (Alabdullah et al., 2025) states: "The focus on three dialects (Levantine, Gulf, Egyptian) constrained generalization, leaving the proposed translation techniques untested on varieties such as Maghrebi and Sudanese Arabic."

Visit

huggingface.co

Tasks

machine translation

Languages

Arabic, Sudanese Spoken

Tags

sudanese-arabicdialectal-arabicmachine-translationbenchmarklow-resource-nlp

Licenses

cc-by-4.0

Similar

Sudanese Arabic Dialect Identification BenchmarkAfriScience-MTotaruMendez/MTAfriScience-MTAFRIDOC-MT: Document-level MT Corpus for African LanguagesNasamuAlhassan/kusaal-mt

Sudanese Arabic Dialect Identification Benchmark

A curated benchmark dataset for Arabic dialect identification, with special focus on Sudanese Arabic

AfriScience-MT

A parallel scientific machine-translation corpus for English + six African languages (Amharic, Hausa

otaruMendez/MT

English to Yoruba Translator # MT (Machine Translator) English to Yoruba Translator. ## Motivatio

AfriScience-MT

A parallel scientific machine-translation corpus for English + six African languages (Amharic, Hausa

AFRIDOC-MT: Document-level MT Corpus for African Languages

This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yorùbá, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-t

NasamuAlhassan/kusaal-mt

Fine-tuned NLLB-200 translation model for Kusaal (kus_Latn) ↔ English — MT system for this low-resou