AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages
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Computer Science > Computation and Language
Title:AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages
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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.12708 [cs.CL] |
| (or arXiv:2606.12708v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12708
arXiv-issued DOI via DataCite (pending registration)
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