analiza aiqbench
The introduction of AfriSUD represents a critical advancement in addressing the severe underrepresentation of African languages within large-scale NLP datasets and evaluation benchmarks. By providing syntactically annotated treebanks that capture unique typological features like agglutination and tone, this resource directly challenges current model architectures to better handle structural diversity beyond dominant Indo-European languages. This work is essential for developing truly inclusive AI systems that function effectively across Sub-Saharan Africa's linguistic landscape.
AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages
arXiv:2606.12708v2 Announce Type: replace-cross Abstract: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP. We aim to bridge this gap by introducing AfriSUD, the first large-scale collection of syntactically annotated treebanks for nine diverse African languages spanning major language families and regions across Sub-Saharan Africa. Using the Surface-Syntactic Universal Dependencies (SUD) framework, our community-led effort provides high-quality, native-speaker verified data that capture typological key features such as agglutination and tone. We evaluate a range of models on AfriSUD for part-of-speech tagging and dependency parsing including non-transformer baselines, multilingual pretrained encoders, and LLMs. Our results reveal a significant syntax gap, where models still show clear limitations across the nine languages, suggesting that existing architectures may not fully capture the structural diversity of African-language syntax.
Authors:Happy Buzaaba, Cheikh Mouhamadou Bamba Dione, David Ifeoluwa Adelani, Sylvain Kahane, Kim Gerdes, Bruno Guillaume, Kevin Guan, Aremu Anuoluwapo, Naome A. Etori, Shamsuddeen Hassan Muhammad, Utitofon Inyang, Peter Nabende, David Sabiiti Bamutura, Andiswa Bukula, Chinedu Uchechukwu, Rooweither Mabuya, Idris Akinade, Christiane Fellbaum
Abstract:Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP. We aim to bridge this gap by introducing AfriSUD, the first large-scale collection of syntactically annotated treebanks for nine diverse African languages spanning major language families and regions across Sub-Saharan Africa. Using the Surface-Syntactic Universal Dependencies (SUD) framework, our community-led effort provides high-quality, native-speaker verified data that capture typological key features such as agglutination and tone. We evaluate a range of models on AfriSUD for part-of-speech tagging and dependency parsing including non-transformer baselines, multilingual pretrained encoders, and LLMs. Our results reveal a significant syntax gap, where models still show clear limitations across the nine languages, suggesting that existing architectures may not fully capture the structural diversity of African-language syntax.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.12708 [cs.CL]
(or arXiv:2606.12708v2 [cs.CL] for this version)
Text integral sub licența deschisă a publicației · 184 cuvinte
Subiecte conexe
AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages
arXiv · 12 iun. 2026
Game-Theoretic Disempowerment: Loss of Control to Agencyless AI
LessWrong · 27 sept. 2026
Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
arXiv · 15 oct. 2026
The Entangled Dimensions of Decision Theory
LessWrong · 30 iul. 2026